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Observational Study vs Experiment with Examples

By Jim Frost 1 Comment

Comparing Observational Studies vs Experiments

Observational studies and experiments are two standard research methods for understanding the world. Both research designs collect data and use statistical analysis to understand relationships between variables. Beyond that commonality, they are vastly different and have dissimilar sets of pros and cons.

Experiments are controlled investigations where researchers actively manipulate one or more variables to observe the effect on another variable, all within a carefully controlled environment. Researchers must be able to control the treatment condition each subject experiences. Experiments typically use randomization to equalize the experimental groups at the start of the study to control potential confounders.

In this post, we’ll compare an observational study vs experiment, highlighting their definitions, strengths, and when to use them effectively. I work through an example showing how a study can use either approach to answer the same research question.

Learn more about Experimental Design: Definition and Types and Confounding Variable Bias .

Strengths of Observational Studies

Real-World Insights : Observational studies reflect real-world scenarios, providing valuable insights into how things naturally occur. Well-designed observational studies have high external validity , specifically ecological validity .

Does Not Require Randomization : Observational studies shine when researchers can’t manipulate treatment conditions or ethical constraints prevent randomization. For example, studying the long-term effects of smoking requires an observational approach because we can’t ethically assign people to smoke or abstain from smoking.

Cost-Effective : Observational studies are generally less expensive and time-consuming than experiments.

Longitudinal Research : They are well-suited for long-term studies or those tracking trends over time.

Strengths of Experiments

Causality : Experiments are the gold standard for establishing causality. By controlling variables and randomly assigning treatment conditions to participants, researchers can confidently attribute changes to the manipulated factor . Well-designed experiments have high internal validity . Learn more about Correlation vs. Causation: Understanding the Differences .

Controlled Environment : Experiments offer a controlled environment, reducing the influence of confounding variables and enhancing the reliability of results.

Replicability : Well-designed experiments are often easier to replicate, increasing researchers’ ability to compare and confirm results.

Randomization : Random assignment in experiments minimizes bias, ensuring all groups are comparable. Learn more about Random Assignment in Experiments .

When to Choose Observational Studies vs Experiments

Observational studies vs experiments are two vital tools in the statistician ’s arsenal, each offering unique advantages.

Experiments excel in establishing causality, controlling variables, and minimizing the impact of confounders. However, they are more expensive and randomly assigning subjects to the treatment groups is impossible in some settings. Learn more about Randomized Controlled Trials .

Meanwhile, observational studies provide real-world insights, are less expensive, and do not require randomization but are more susceptible to the effects of confounders. Identifying causal relationships is problematic in these studies. Learn more about Observational Studies: Definition & Examples  and Correlational Studies .

Observational studies can be prospective or retrospective studies . On the other hand, randomized experiments must be prospective studies .

The choice between an observational study vs experiment hinges on your research objectives, the context in which you’re working, available time and resources, and your ability to assign subjects to the experimental groups and control other variables.

If you’re looking for a middle ground choice between observational studies vs experiments, consider using a quasi-experimental design. These methods don’t require you to randomly assign participants to the experimental groups and still allow you to draw better causal conclusions about an intervention than an observational study. Learn more about Quasi-Experimental Design Overview & Examples .

Understanding their strengths and differences will help you make the right choice for your statistical endeavors.

Observational Study vs Experiment Example

Suppose you want to assess the health benefits of consuming a daily multivitamin. Let’s explore how an observational study vs experiment would evaluate this research question and their pros and cons.

An observational study will recruit subjects and have them record their vitamin consumption, various health outcomes, and, ideally, record confounding variables. The participants choose whether or not to take vitamins during the study based on their existing habits. Some medical measurements might occur in a lab setting, but researchers are not administering treatments (vitamins). Then, using statistical models, researchers can evaluate the relationship between vitamin consumption and health outcomes while controlling for potential confounders they measured.

An experiment will recruit subjects and then randomly assign them to the treatment group that takes daily vitamins or the control group taking a placebo . Randomization controls all confounders whether the researchers know of them or not. Finally, the researchers compare the treatment to the control group. Learn more about Control Groups in Experiments .

Most vitamin studies are observational because the randomization process would be challenging to implement, and it raises ethical concerns in this context. The random assignment process would override the participants’ preferences for taking vitamins by randomly forcing subjects to consume vitamins or placebos for decades . That’s how long it takes for the differences in health outcomes to manifest. Consequently, enforcing the rigid protocol for so long would be difficult and unethical.

For an observational study, a critical downside is that the pre-existing differences between those who do and do not take vitamins daily comprise a pretty long list of health-related habits and medical measures. Any of them can potentially explain the difference in outcomes instead of the vitamin consumption!

As you can see, using an observational study vs experiment involves many tradeoffs! Let’s close with a table that summarizes the differences.

Differences between an Observational Study and Experiment

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October 22, 2023 at 11:17 pm

Well stated: ” Both research designs collect data and use statistical analysis to understand relationships between variables” I was not familiar with the terms research designs. 😀

PS, I am already receiving all your wonderful mailing. I binge-read them every few weeks. I am planning on getting your other two books when I can. Thanks, and Cheers!

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Experiment vs Observational Study: Similarities & Differences

An experiment involves the deliberate manipulation of variables to observe their effect, while an observational study involves collecting data without interfering with the subjects or variables under study.

This article will explore both, but let’s start with some quick explanations:

• Experimental Study : An experiment is a research design wherein an investigator manipulates one or more variables to establish a cause-effect relationship (Tan, 2022). For example, a pharmaceutical company may conduct an experiment to find out if a new medicine for diabetes is effective by administering it to a selected group (experimental group), while not administering it to another group (control group).
• Observational Study : An observational study is a type of research wherein the researcher observes characteristics and measures variables of interest in a subset of a population, but does not manipulate or intervene (Atkinson et al., 2021). An example may be a sociologist who conducts a cross-sectional survey of the population to determine health disparities across different income groups. 

Experiment vs Observational Study

1. experiment.

An experiment is a research method characterized by a high degree of experimental control exerted by the researcher. In the context of academia, it allows for the testing of causal hypotheses (Privitera, 2022).

When conducting an experiment, the researcher first formulates a hypothesis , which is a predictive statement about the potential relationship between at least two variables.

For instance, a psychologist may want to test the hypothesis that participation in physical exercise ( independent variable ) improves the cognitive abilities (dependent variable) of the elderly.

In an experiment, the researcher manipulates the independent variable(s) and then observes the effects on the dependent variable(s). This method of research involves two or more comparison groups—an experimental group that is subjected to the variable being tested and a control group that is not (Sampselle, 2012).

For instance, in the physical exercise study noted above, the psychologist would administer a physical exercise regime to an experimental group of elderly people, while a control group would continue with their usual lifestyle activities .

One of the unique features of an experiment is random assignment . Participants are randomly allocated to either the experimental or control groups to ensure that every participant has an equal chance of being in either group. This reduces the risk of confounding variables and increases the likelihood that the results are attributable to the independent variable rather than another factor (Eich, 2014).

For instance, in the physical exercise example, the psychologist would randomly assign participants to the experimental or control group to reduce the potential impact of external variables such as diet or sleep patterns.

1. Impacts of Films on Happiness: A psychologist might create an experimental study where she shows participants either a happy, sad, or neutral film (independent variable) then measures their mood afterward (dependent variable). Participants would be randomly assigned to one of the three film conditions.

2. Impacts of Exercise on Weight Loss: In a fitness study, a trainer could investigate the impact of a high-intensity interval training (HIIT) program on weight loss. Half of the participants in the study are randomly selected to follow the HIIT program (experimental group), while the others follow a standard exercise routine (control group).

3. Impacts of Veganism on Cholesterol Levels: A nutritional experimenter could study the effects of a particular diet, such as veganism, on cholesterol levels. The chosen population gets assigned either to adopt a vegan diet (experimental group) or stick to their usual diet (control group) for a specific period, after which cholesterol levels are measured.

Read More: Examples of Random Assignment

Strengths and Weaknesses

Read More: Experimental Research Examples

2. Observational Study

Observational research is a non-experimental research method in which the researcher merely observes the subjects and notes behaviors or responses that occur (Ary et al., 2018).

This approach is unintrusive in that there is no manipulation or control exerted by the researcher. For instance, a researcher could study the relationships between traffic congestion and road rage by just observing and recording behaviors at a set of busy traffic lights, without applying any control or altering any variables.

In observational studies, the researcher distinguishes variables and measures their values as they naturally occur. The goal is to capture naturally occurring behaviors , conditions, or events (Ary et al., 2018).

For example, a sociologist might sit in a cafe to observe and record interactions between staff and customers in order to examine social and occupational roles .

There is a significant advantage of observational research in that it provides a high level of ecological validity – the extent to which the data collected reflects real-world situations – as the behaviors and responses are observed in a natural setting without experimenter interference (Holleman et al., 2020)

However, the inability to control various factors that might influence the observations may expose these studies to potential confounding bias , a consideration researchers must take into account (Schober & Vetter, 2020).

1. Behavior of Animals in the Wild: Zoologists often use observational studies to understand the behaviors and interactions of animals in their natural habitats. For instance, a researcher could document the social structure and mating behaviors of a wolf pack over a period of time.

2. Impact of Office Layout on Productivity: A researcher in organizational psychology might observe how different office layouts affect staff productivity and collaboration. This involves the observation and recording of staff interactions and work output without altering the office setting.

3. Foot Traffic and Retail Sales: A market researcher might conduct an observational study on how foot traffic (the number of people passing by a store) impacts retail sales. This could involve observing and documenting the number of walk-ins, time spent in-store, and purchase behaviors.

Read More: Observational Research Examples

Experimental and Observational Study Similarities and Differences

Experimental and observational research both have their place – one is right for one situation, another for the next.

Experimental research is best employed when the aim of the study is to establish cause-and-effect relationships between variables – that is, when there is a need to determine the impact of specific changes on the outcome (Walker & Myrick, 2016).

One of the standout features of experimental research is the control it gives to the researcher, who dictates how variables should be changed and assigns participants to different conditions (Privitera, 2022). This makes it an excellent choice for medical or pharmaceutical studies, behavioral interventions, and any research where hypotheses concerning influence and change need to be tested.

For example, a company might use experimental research to understand the effects of staff training on job satisfaction and productivity.

Observational research , on the other hand, serves best when it’s vital to capture the phenomena in their natural state, without intervention, or when ethical or practical considerations prevent the researcher from manipulating the variables of interest (Creswell & Poth, 2018).

It is the method of choice when the interest of the research lies in describing what is, rather than altering a situation to see what could be (Atkinson et al., 2021).

This approach might be utilized in studies that aim to describe patterns of social interaction, daily routines, user experiences, and so on. A real-world example of observational research could be a study examining the interactions and learning behaviors of students in a classroom setting.

I’ve demonstrated their similarities and differences a little more in the table below:

Experimental and observational research each have their place, depending upon the study. Importantly, when selecting your approach, you need to reflect upon your research goals and objectives, and select from the vast range of research methodologies , which you can read up on in my next article, the 15 types of research designs .

Ary, D., Jacobs, L. C., Irvine, C. K. S., & Walker, D. (2018). Introduction to research in education . London: Cengage Learning.

Atkinson, P., Delamont, S., Cernat, A., Sakshaug, J. W., & Williams, R. A. (2021). SAGE research methods foundations . New York: SAGE Publications Ltd.

Creswell, J.W., and Poth, C.N. (2018). Qualitative Inquiry and Research Design: Choosing among Five Approaches . New York: Sage Publications.

Eich, E. (2014). Business Research Methods: A Radically Open Approach . Frontiers Media SA.

Holleman, G. A., Hooge, I. T., Kemner, C., & Hessels, R. S. (2020). The ‘real-world approach’and its problems: A critique of the term ecological validity. Frontiers in Psychology , 11 , 721. doi: https://doi.org/10.3389/fpsyg.2020.00721  

Privitera, G. J. (2022). Research methods for the behavioral sciences . Sage Publications.

Sampselle, C. M. (2012). The Science and Art of Nursing Research . South University Online Press.

Schober, P., & Vetter, T. R. (2020). Confounding in observational research. Anesthesia & Analgesia , 130 (3), 635.

Tan, W. C. K. (2022). Research methods: A practical guide for students and researchers . World Scientific.

Walker, D., and Myrick, F. (2016). Grounded Theory: An Exploration of Process and Procedure . New York: Qualitative Health Research.

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• What Is an Observational Study? | Guide & Examples

What Is an Observational Study? | Guide & Examples

Published on March 31, 2022 by Tegan George . Revised on June 22, 2023.

An observational study is used to answer a research question based purely on what the researcher observes. There is no interference or manipulation of the research subjects, and no control and treatment groups .

These studies are often qualitative in nature and can be used for both exploratory and explanatory research purposes. While quantitative observational studies exist, they are less common.

Observational studies are generally used in hard science, medical, and social science fields. This is often due to ethical or practical concerns that prevent the researcher from conducting a traditional experiment . However, the lack of control and treatment groups means that forming inferences is difficult, and there is a risk of confounding variables and observer bias impacting your analysis.

Table of contents

Types of observation, types of observational studies, observational study example, advantages and disadvantages of observational studies, observational study vs. experiment, other interesting articles, frequently asked questions.

There are many types of observation, and it can be challenging to tell the difference between them. Here are some of the most common types to help you choose the best one for your observational study.

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There are three main types of observational studies: cohort studies, case–control studies, and cross-sectional studies .

Cohort studies

Cohort studies are more longitudinal in nature, as they follow a group of participants over a period of time. Members of the cohort are selected because of a shared characteristic, such as smoking, and they are often observed over a period of years.

Case–control studies

Case–control studies bring together two groups, a case study group and a control group . The case study group has a particular attribute while the control group does not. The two groups are then compared, to see if the case group exhibits a particular characteristic more than the control group.

For example, if you compared smokers (the case study group) with non-smokers (the control group), you could observe whether the smokers had more instances of lung disease than the non-smokers.

Cross-sectional studies

Cross-sectional studies analyze a population of study at a specific point in time.

This often involves narrowing previously collected data to one point in time to test the prevalence of a theory—for example, analyzing how many people were diagnosed with lung disease in March of a given year. It can also be a one-time observation, such as spending one day in the lung disease wing of a hospital.

Observational studies are usually quite straightforward to design and conduct. Sometimes all you need is a notebook and pen! As you design your study, you can follow these steps.

Step 1: Identify your research topic and objectives

The first step is to determine what you’re interested in observing and why. Observational studies are a great fit if you are unable to do an experiment for practical or ethical reasons , or if your research topic hinges on natural behaviors.

Step 2: Choose your observation type and technique

In terms of technique, there are a few things to consider:

• Are you determining what you want to observe beforehand, or going in open-minded?
• Is there another research method that would make sense in tandem with an observational study?
• If yes, make sure you conduct a covert observation.
• If not, think about whether observing from afar or actively participating in your observation is a better fit.
• How can you preempt confounding variables that could impact your analysis?
• You could observe the children playing at the playground in a naturalistic observation.
• You could spend a month at a day care in your town conducting participant observation, immersing yourself in the day-to-day life of the children.
• You could conduct covert observation behind a wall or glass, where the children can’t see you.

Overall, it is crucial to stay organized. Devise a shorthand for your notes, or perhaps design templates that you can fill in. Since these observations occur in real time, you won’t get a second chance with the same data.

Step 3: Set up your observational study

Before conducting your observations, there are a few things to attend to:

• Plan ahead: If you’re interested in day cares, you’ll need to call a few in your area to plan a visit. They may not all allow observation, or consent from parents may be needed, so give yourself enough time to set everything up.
• Determine your note-taking method: Observational studies often rely on note-taking because other methods, like video or audio recording, run the risk of changing participant behavior.
• Get informed consent from your participants (or their parents) if you want to record:  Ultimately, even though it may make your analysis easier, the challenges posed by recording participants often make pen-and-paper a better choice.

Step 4: Conduct your observation

After you’ve chosen a type of observation, decided on your technique, and chosen a time and place, it’s time to conduct your observation.

Here, you can split them into case and control groups. The children with siblings have a characteristic you are interested in (siblings), while the children in the control group do not.

When conducting observational studies, be very careful of confounding or “lurking” variables. In the example above, you observed children as they were dropped off, gauging whether or not they were upset. However, there are a variety of other factors that could be at play here (e.g., illness).

Step 5: Analyze your data

After you finish your observation, immediately record your initial thoughts and impressions, as well as follow-up questions or any issues you perceived during the observation. If you audio- or video-recorded your observations, you can transcribe them.

Your analysis can take an inductive  or deductive approach :

• If you conducted your observations in a more open-ended way, an inductive approach allows your data to determine your themes.
• If you had specific hypotheses prior to conducting your observations, a deductive approach analyzes whether your data confirm those themes or ideas you had previously.

Next, you can conduct your thematic or content analysis . Due to the open-ended nature of observational studies, the best fit is likely thematic analysis .

Step 6: Discuss avenues for future research

Observational studies are generally exploratory in nature, and they often aren’t strong enough to yield standalone conclusions due to their very high susceptibility to observer bias and confounding variables. For this reason, observational studies can only show association, not causation .

If you are excited about the preliminary conclusions you’ve drawn and wish to proceed with your topic, you may need to change to a different research method , such as an experiment.

• Observational studies can provide information about difficult-to-analyze topics in a low-cost, efficient manner.
• They allow you to study subjects that cannot be randomized safely, efficiently, or ethically .
• They are often quite straightforward to conduct, since you just observe participant behavior as it happens or utilize preexisting data.
• They’re often invaluable in informing later, larger-scale clinical trials or experimental designs.

Disadvantages

• Observational studies struggle to stand on their own as a reliable research method. There is a high risk of observer bias and undetected confounding variables or omitted variables .
• They lack conclusive results, typically are not externally valid or generalizable, and can usually only form a basis for further research.
• They cannot make statements about the safety or efficacy of the intervention or treatment they study, only observe reactions to it. Therefore, they offer less satisfying results than other methods.

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The key difference between observational studies and experiments is that a properly conducted observational study will never attempt to influence responses, while experimental designs by definition have some sort of treatment condition applied to a portion of participants.

However, there may be times when it’s impossible, dangerous, or impractical to influence the behavior of your participants. This can be the case in medical studies, where it is unethical or cruel to withhold potentially life-saving intervention, or in longitudinal analyses where you don’t have the ability to follow your group over the course of their lifetime.

An observational study may be the right fit for your research if random assignment of participants to control and treatment groups is impossible or highly difficult. However, the issues observational studies raise in terms of validity , confounding variables, and conclusiveness can mean that an experiment is more reliable.

If you’re able to randomize your participants safely and your research question is definitely causal in nature, consider using an experiment.

If you want to know more about statistics , methodology , or research bias , make sure to check out some of our other articles with explanations and examples.

• Student’s  t -distribution
• Normal distribution
• Null and Alternative Hypotheses
• Chi square tests
• Confidence interval
• Quartiles & Quantiles
• Cluster sampling
• Stratified sampling
• Data cleansing
• Reproducibility vs Replicability
• Peer review
• Prospective cohort study

Research bias

• Implicit bias
• Cognitive bias
• Placebo effect
• Hawthorne effect
• Hindsight bias
• Affect heuristic
• Social desirability bias

An observational study is a great choice for you if your research question is based purely on observations. If there are ethical, logistical, or practical concerns that prevent you from conducting a traditional experiment , an observational study may be a good choice. In an observational study, there is no interference or manipulation of the research subjects, as well as no control or treatment groups .

The key difference between observational studies and experimental designs is that a well-done observational study does not influence the responses of participants, while experiments do have some sort of treatment condition applied to at least some participants by random assignment .

A quasi-experiment is a type of research design that attempts to establish a cause-and-effect relationship. The main difference with a true experiment is that the groups are not randomly assigned.

Exploratory research aims to explore the main aspects of an under-researched problem, while explanatory research aims to explain the causes and consequences of a well-defined problem.

Experimental design means planning a set of procedures to investigate a relationship between variables . To design a controlled experiment, you need:

• A testable hypothesis
• At least one independent variable that can be precisely manipulated
• At least one dependent variable that can be precisely measured

When designing the experiment, you decide:

• How you will manipulate the variable(s)
• How you will control for any potential confounding variables
• How many subjects or samples will be included in the study
• How subjects will be assigned to treatment levels

Experimental design is essential to the internal and external validity of your experiment.

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Experiment vs. Observational Study | Definition & Examples

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Cat has taught a variety of subjects, including communications, mathematics, and technology. Cat has a master's degree in education and is currently working on her Ph.D.

What is an example of an observational study?

An observational study includes following 100 children as they grow up, and recording how often their parents read books to them as a child and measuring how well they did in school.

What is an example of an experimental study?

An example of an experimental study is a study where researchers had 20 test subjects and 10 were randomly assigned to receive a medicine being studied and the other 10 would receive a placebo. The effects of the medicine will then be recorded.

What is the difference between observational study and experimental study?

In an observational study researchers watch what happens naturally instead of intervening with specific treatments. In an experimental study researchers control the study by controlling as many variables as possible.

What is an experimental study in research?

An experiment study is a type of research where the variables are controlled as much as possible by the researcher. Test subjects are given a specific explanatory variable or treatment and the specific response variable is measured.

Table of Contents

Observational study vs experiment, experimental study definition, observational study definition, observational study vs. experiment examples, lesson summary.

There are multiple ways to conduct research, and understanding the different ways to do so is important in interpreting the results of the research. The two main types of research are observational studies and experiments. An observational study is when the researcher observes the effect of a specific variable as it occurs naturally, without making any attempt to intervene. In an experiment , the researcher manipulates the situation and observes the effect in a more controlled setting. There are a few different types of experiments , including randomized experiments and controlled experiments .

A randomized experiment introduces randomization into the research to try to control some of the natural variance and biases and better understand the effect of a variable. In a randomized experiment, the researcher randomizes which "treatment" each test subject receives. A treatment is the variable being imposed or measured on the test subjects. The amount of times the treatment is applied is called the factor . In observational studies, researchers cannot control where randomization occurs, and it may not always occur specifically with the desired treatment.

A controlled experiment compares the treatment group to a control group. The treatment group receives the "treatment" and the control group does not receive anything or may receive a placebo . In an observational study, some test subjects will naturally receive a treatment or naturally will not, but the researcher has no control over who is in which group. Because of this, there could be underlying reasons that lead some people to receive the treatment or not, which makes it difficult to have a control group.

Types of Variables in Research

In research it is important to be aware of certain variables. The response variable is the variable being measured and is the result of the treatment. The explanatory variable is the treatment being given to test subjects (whether given naturally or by the researcher). The dependent variable is a response that isn't the specific variable being measured, but still occurs in response to the explanatory variable, or still affects the response variable. An independent variable is something that may occur to the test subject at the same time as the research is occurring, but isn't connected to the explanatory variable.

For example, a researcher wants to study how eating oranges affects the length of a cold. The response variable is the length of the cold and the explanatory variable could be how many oranges a person eats. There are typically many possible dependent and independent variables. Researchers need to identify which ones may be important to the study. Possible dependent variables include: other foods eaten during the cold (perhaps someone doesn't eat oranges, but does eat grapefruit) and medicines taken during the cold. Independent variables could include a number of different factors, ranging from the type of music the test subject listened to or how much they like or dislike oranges. In an observational study, the researchers would have no control over these dependent and independent variables. The researcher would track and measure theses variables and explain how they may or may not have affected the results. In an experiment, the researcher would try to find ways to control as many of the dependent and independent variables as possible. For example, they may tell all test subjects to not eat any other citrus fruit during the course of the study.

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• 0:00 Conducting Research
• 1:45 Observational Studies
• 3:25 Experiments

An experimental study has both benefits and weaknesses. The benefits of an experimental study include:

• Typically seen as stronger research
• Can more definitively draw conclusions
• Can eliminate some or most variables
• Easier to control
• Is often shorter in duration than observational studies
• Can be tailored to answer a specific question

The weaknesses of an experimental study include:

• Typically very expensive
• Limited in ability based on ethical issues
• When studying humans, often see high dropout rates

The first step in developing an experimental study is asking the specific question to be answered. This question is often in the form of what happens to the response variable when a specific explanatory variable is introduced. For example, the question may be:

• Does the growth of corn change (the response variable) when given twice as much fertilizer (the explanatory variable)?
• Does this new medicine (the explanatory variable) affect blood pressure control (the response variable)?

After forming the question, the experiment is designed in a way to answer the question. The researcher will form control and treatment groups. The control group receives no treatment, while the treatment group does. For example, when medications are studied, this typically means that the control group is given a placebo, so that researchers can identify if the outcome (response variable) is a result of the medication. There can be multiple treatment groups, perhaps receiving varying levels, or factors, of the explanatory variable (in this example, different groups could receive varying doses of medication).

Randomization is typically a part of experiments, and the way test subjects are randomized has to happen before the experiment starts. To randomize the experiment, the researcher would develop a system to ensure that people are assigned to the control group or treatment group in a completely random manner. This helps to ensure that the groups will be more equal and comparable to one another.

Experiments can also have varying levels of 'blinding.' One level of blinding is where the researcher (the person or people applying the treatments to the test subjects) does not know if they are applying a treatment or applying a placebo to each test subject. In another type of blinding, the test subjects do not know if they are receiving the treatment or a placebo. Often, both the researcher and the test subjects are "blinded."

Before starting the experiment, the researcher also needs to identify potential dependent variables that they may want to control. For example, they may want to control how much water the crops receive so that the amount of water doesn't muddle the effect of fertilizer on corn growth. To control this, they grow the corn underneath a cover so that the only water that crops receive is applied by the researchers. If a dependent variable cannot be controlled, it is identified and measured. For example, the weight of test subjects may be a dependent variable, but there is no way for the researcher to to control this. Instead, the researcher will record the weights of test subjects throughout the study, and at the end of the study use statistics to determine if and how weight may have affected the results.

An observational study also has many benefits and weaknesses. Benefits include:

• Can often be performed for very little expense
• Can study issues that would otherwise have ethical issues

One of the biggest strengths of observational studies is its ability to study issues that would otherwise be unethical. For example, an experiment cannot be set up to determine how abuse affects children, because it is highly unethical to impose abuse on children as a random treatment. However, an observational study can look back at a group of children and observe what differences and similarities exist between children who were abused and who were not.

Weaknesses include:

• Very difficult to conclude causation; without control of dependent variables, it is hard to say that one variable is actually caused by the explanatory variable,
• Difficult to control biases and variables
• Results are often seen as weaker

Because observational studies are difficult to conclude causation, they are often used as an initial study to determine what experiments may be important to perform in order to further identify causation.

An observational study is set up by first asking a question, just as with an experimental study. The question also includes how an explanatory variable affects the response variable. Typically this includes answering questions such as:

• How will the information be collected (common methods includes survey, researcher observing groups, specific metric measurements such as weight, etc.)
• How long will groups be observed (this could be hours, days, months, or even years)
• What potential dependent variables can be identified within the groups (while researchers can't control these variables, they can be identified and potentially included in statistical analysis)

There are a few methods to conduct observational studies. A common method used is surveying. A survey can given at a single point in time survey that asks respondents to think back on past activities (such as how many vegetables are typically eaten per week). Alternatively, a survey can be given at scheduled intervals over time to collect data at multiple points (perhaps asking respondents to record vegetable intake each day as it is consumed, over a period of a couple months).

An observational study can be a cohort study or a case control study. A cohort study follows one group of people for a long period of time (often many years). The people in this group, or "cohort," share a common explanatory variable, such as they work in a specific field, or they were born with the same disability. The researcher follows the cohort to see who gets the response variable and who does not. For example, if the cohort is a group of football players (explanatory variable), the researcher could follow them over many years to determine how many get head injuries (response variable).

A case control study looks at two groups. The "case" group contains members who all share the response variable, and a control group that does not share the response variable. Using a similar example, the "case" group could be people who have had head injuries and the control group would be people who have not. The researcher would look at both groups and determine if the same or a different number in each group were football players.

Observational studies and experimental studies both have a role in research. Both may be used to answer the same question. For example, if researchers wanted to understand how eating vegetables affects mood, they could do so with either an observational study or experimental study. However, there would be important differences in how they answer this question depending what type of study they use. An observational study may answer this question by looking over a course of many years by asking test subjects to rate their overall mood every month and indicate how many vegetables they consumed on average each day. An experimental study may answer this question by setting up an experiment where a group of people is housed for two weeks. One group receives a large portion of vegetables at meal time and the other group receives a smaller portion. Throughout the study mood is measured in each group.

Both studies described above look at the impact of vegetables on mood, but would be able to draw slightly different conclusions. The observational study answers the question from a broader perspective, by looking at how mood is affected with long-term vegetable consumption. However, it cannot control for why those people do or do not eat large amount of vegetables. It could be that someone who is very health conscious eats large amounts of vegetables, but also works out frequently. In this example, it is possible that it is not the vegetables that affect mood but rather working out that does. A researcher would have no way to differentiate this in an observational study. On the other hand, the experimental study randomizes which group receives large amounts of vegetables and also keeps people in a controlled environment, thus controlling for many dependent variables. However, it only looks at a very small period of time. Perhaps eating vegetables only starts to affect mood if they are continually consumed for at least a year, but in this example the researcher would not ascertain that.

Observational studies are particularly beneficial to answer questions that include ethical issues such as:

• How does food insecurity affect scholastic abilities?
• How does the age someone starts smoking affect chances of getting lung cancer?

Experimental studies are particularly beneficial to answer questions looking at one specific variable:

• What are the side effects seen within two weeks of taking a new drug?
• How much more (or less) do plants grow using natural fertilizer vs synthetic fertilizer?

Experimental studies are studies that control variables to answer a specific research question. Observational studies are studies that observe what happens naturally and record results. Two main types of observational studies are cohort and case controlled studies. A cohort study looks at a single group of people with a common explanatory variable. While a Case control study looks at two groups, one with the explanatory variable and one without the explanatory variable. Both studies have benefits and a place in research. Generally observational studies are used to get a big picture, answer several questions over a long period of time, don't cost much, and can answer questions that would otherwise be unethical to research. Experimental studies help strengthen causal relationships , answer a specific question, are easier to control, are used to gain stronger research answers, and are shorter in duration.

The weaknesses of an observational study are that they often take a long time and they cannot usually establish a causal relationship. The weakness of experimental studies are that they are expensive and cannot look into questions that would violate ethics.

In both studies various types of variables are important to consider, including:

• Response variable : the variable that is measured, this is the result of the treatment
• Explanatory variable : the treatment being given to the test subjects
• Dependent variable : responses that are a result of the treatment, but aren't the specific variables of interest or the ones being measured
• Independent variable : any other variables or responses the test subjects may have that are not the result of the treatment

In these studies the treatment is the variable that is being imposed on the test subjects or measured in the test subjects and the factor is how many times that treatment is imposed on the test subjects.

Video Transcript

Conducting research.

Emily has been doing research for her psychology class. She is studying the academic successes of ballet dancers. She wants to know if ballet dancers have higher grades than peers who do not study dance. She can approach this research a couple of different ways. Emily can either conduct an experimental or an observational study.

In this lesson, you will learn about experimental and observational studies and how they both use statistics. First, you will need to understand two types of variables that occur in both - response and explanatory - as well as the more familiar independent and dependent variables.

A response variable is the observed variable, or variable in question. In Emily's study, the grades, or academic success, would be the response variable. This is similar to a dependent variable , which is a condition or piece of data in an experiment that is controlled or influenced by an outside factor, most often the independent variable. These concepts are similar, but not the same. We will look at how each are used in experimental and observational studies later in this lesson. First, let's finish reviewing the other important terms.

An explanatory variable is a variable, or set of variables, that can influence the response variable. In Emily's case, she believes that ballet is the explanation for increased academic success. This is similar to an independent variable , which is a condition or piece of data in an experiment that can be controlled or changed. The difference between explanatory variables and independent variables is that explanatory variables can't always be controlled or changed.

Now that you understand explanatory, independent, response and dependent variables, let's discuss observational studies.

Observational Studies

Emily has decided to conduct an observational study to collect data on ballet dancers and their grades in school. An observational study is a study where researchers simply collect data based on what is seen and heard and infer based on the data collected. Researchers should not interfere with the subjects or variables in any way. Emily will collect data by conducting a survey, asking each dancer his or her level of ability, number of hours spent training per week, GPA in school and averages in each class. Once Emily collects this data, she can use the data to develop a conclusion.

Two things are required of an observational study. First, the variables must be observed and the data must be collected through observation. A researcher can't add in any extra information, or guesses. All of the information must be evidence in the observational study. Second, the researcher can only observe, they cannot interfere with the study in any way.

Observational studies have explanatory and response variables only. Because the researcher cannot interfere with the study, there cannot be any independent nor dependent variables. Remember, an independent variable is something that is controlled in the study. The researcher has no control of the variables in an observational study. Emily can use an observational study to make comparisons between dancers and non-dancers for her class.

What if you need a study that you can control? What if you need independent and dependent variables? Or what if you need to find cause and effect? It is better studied using an experiment.

Experiments

Emily decides to conduct an experiment to find if dance is the cause of better grades in students. She uses the students in a local middle school for her experiment. She uses two groups of middle school students that have never taken a dance class before. She has one group take one dance class a day, and the other group takes no classes. Each week over the semester, she records their progress in class.

An experiment is a method of applying treatments to a group and recording the effects. A good group experiment will have two basic elements: a control and a treatment. The control is the group that remains untreated throughout the duration of an experiment. In Emily's case, the group that takes no dance classes is the control group. An experiment will also have a treatment , which is the variable in an experiment that is used on an experimental group. In Emily's experiment, the treatment is the dance classes.

Another element of an experiment is the factor , which is the degree the treatment is applied to the experimental group. For example, Emily may decide to have multiple experimental groups. She could have one group take one dance class a week and a second group take two dance classes a week, while still having a control group that takes no classes each week. The number of dance classes each group takes is the factor of the treatment.

There are two basic ways you can conduct studies and collect data: observational and experimental studies. An observational study is a study where researchers simply collect data based on what is seen and heard and infer based on the data collected. Researchers should not interfere with the subjects or variables in any way. Remember, observational studies have explanatory and response variables only. Because the researcher cannot interfere with the study, there cannot be any independent nor dependent variables. When Emily was passing around surveys about grades and time spent training to all of the dancers, she was conducting an observational study. The researcher has no control over the variables in an observational study.

An experiment is a method of applying treatments to a group and recording the effects. Remember, a good group experiment will have two basic elements: a control and a treatment. The control is the group that remains untreated throughout the duration of an experiment. In Emily's case, the group that takes no dance classes was the control group. An experiment will also have a treatment , which is the variable in an experiment that is used on an experimental group. In Emily's experiment, the treatment was the dance classes. The number of dance classes each group took was the factor , which is the degree the treatment is applied to the experimental group.

Learning Outcomes

Following this video lesson, you should be able to:

• Define response variable and explanatory variable
• Identify the requirements of conducting an observational study
• Explain how to conduct an experiment
• Recall three elements of an experiment: control, treatment and factor

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3.4 - experimental and observational studies.

Now that Jaylen can weigh the different sampling strategies, he might want to consider the type of study he is conduction. As a note, for students interested in research designs, please consult STAT 503 for a much more in-depth discussion. However, for this example, we will simply distinguish between experimental and observational studies.

Now that we know how to collect data, the next step is to determine the type of study. The type of study will determine what type of relationship we can conclude.

There are predominantly two different types of studies: 

Let's say that there is an option to take quizzes throughout this class. In an  observational study , we may find that better students tend to take the quizzes and do better on exams. Consequently, we might conclude that there may be a relationship between quizzes and exam scores.

In an experimental study , we would randomly assign quizzes to specific students to look for improvements. In other words, we would look to see whether taking quizzes causes higher exam scores.

Causation Section  

It is very important to distinguish between observational and experimental studies since one has to be very skeptical about drawing cause and effect conclusions using observational studies. The use of random assignment of treatments (i.e. what distinguishes an experimental study from an observational study) allows one to employ cause and effect conclusions.

Ethics is an important aspect of experimental design to keep in mind. For example, the original relationship between smoking and lung cancer was based on an observational study and not an assignment of smoking behavior.

Observational vs. Experimental Study: A Comprehensive Guide

Explore the fundamental disparities between experimental and observational studies in this comprehensive guide by Santos Research Center, Corp. Uncover concepts such as control group, random sample, cohort studies, response variable, and explanatory variable that shape the foundation of these methodologies. Discover the significance of randomized controlled trials and case control studies, examining causal relationships and the role of dependent variables and independent variables in research designs.

This enlightening exploration also delves into the meticulous scientific study process, involving survey members, systematic reviews, and statistical analyses. Investigate the careful balance of control group and treatment group dynamics, highlighting how researchers meticulously assign variables and analyze statistical patterns to discern meaningful insights. From dissecting issues like lung cancer to understanding sleep patterns, this guide emphasizes the precision of controlled experiments and controlled trials, where variables are isolated and scrutinized, paving the way for a deeper comprehension of the world through empirical research.

Introduction to Observational and Experimental Studies

These two studies are the cornerstones of scientific inquiry, each offering a distinct approach to unraveling the mysteries of the natural world.

Observational studies allow us to observe, document, and gather data without direct intervention. They provide a means to explore real-world scenarios and trends, making them valuable when manipulating variables is not feasible or ethical. From surveys to meticulous observations, these studies shed light on existing conditions and relationships.

Experimental studies , in contrast, put researchers in the driver's seat. They involve the deliberate manipulation of variables to understand their impact on specific outcomes. By controlling the conditions, experimental studies establish causal relationships, answering questions of causality with precision. This approach is pivotal for hypothesis testing and informed decision-making.

At Santos Research Center, Corp., we recognize the importance of both observational and experimental studies. We employ these methodologies in our diverse research projects to ensure the highest quality of scientific investigation and to answer a wide range of research questions.

Observational Studies: A Closer Look

In our exploration of research methodologies, let's zoom in on observational research studies—an essential facet of scientific inquiry that we at Santos Research Center, Corp., expertly employ in our diverse research projects.

What is an Observational Study?

Observational research studies involve the passive observation of subjects without any intervention or manipulation by researchers. These studies are designed to scrutinize the relationships between variables and test subjects, uncover patterns, and draw conclusions grounded in real-world data.

Researchers refrain from interfering with the natural course of events in controlled experiment. Instead, they meticulously gather data by keenly observing and documenting information about the test subjects and their surroundings. This approach permits the examination of variables that cannot be ethically or feasibly manipulated, making it particularly valuable in certain research scenarios.

Types of Observational Studies

Now, let's delve into the various forms that observational studies can take, each with its distinct characteristics and applications.

Cohort Studies:  A cohort study is a type of observational study that entails tracking one group of individuals over an extended period. Its primary goal is to identify potential causes or risk factors for specific outcomes or treatment group. Cohort studies provide valuable insights into the development of conditions or diseases and the factors that influence them.

Case-Control Studies:  Case-control studies, on the other hand, involve the comparison of individuals with a particular condition or outcome to those without it (the control group). These studies aim to discern potential causal factors or associations that may have contributed to the development of the condition under investigation.

Cross-Sectional Studies:  Cross-sectional studies take a snapshot of a diverse group of individuals at a single point in time. By collecting data from this snapshot, researchers gain insights into the prevalence of a specific condition or the relationships between variables at that precise moment. Cross-sectional studies are often used to assess the health status of the different groups within a population or explore the interplay between various factors.

Advantages and Limitations of Observational Studies

Observational studies, as we've explored, are a vital pillar of scientific research, offering unique insights into real-world phenomena. In this section, we will dissect the advantages and limitations that characterize these studies, shedding light on the intricacies that researchers grapple with when employing this methodology.

Advantages: One of the paramount advantages of observational studies lies in their utilization of real-world data. Unlike controlled experiments that operate in artificial settings, observational studies embrace the complexities of the natural world. This approach enables researchers to capture genuine behaviors, patterns, and occurrences as they unfold. As a result, the data collected reflects the intricacies of real-life scenarios, making it highly relevant and applicable to diverse settings and populations.

Moreover, in a randomized controlled trial, researchers looked to randomly assign participants to a group. Observational studies excel in their capacity to examine long-term trends. By observing one group of subjects over extended periods, research scientists gain the ability to track developments, trends, and shifts in behavior or outcomes. This longitudinal perspective is invaluable when studying phenomena that evolve gradually, such as chronic diseases, societal changes, or environmental shifts. It allows for the detection of subtle nuances that may be missed in shorter-term investigations.

Limitations: However, like any research methodology, observational studies are not without their limitations. One significant challenge of statistical study lies in the potential for biases. Since researchers do not intervene in the subjects' experiences, various biases can creep into the data collection process. These biases may arise from participant self-reporting, observer bias, or selection bias in random sample, among others. Careful design and rigorous data analysis are crucial for mitigating these biases.

Another limitation is the presence of confounding variables. In observational studies, it can be challenging to isolate the effect of a specific variable from the myriad of other factors at play. These confounding variables can obscure the true relationship between the variables of interest, making it difficult to establish causation definitively. Research scientists must employ statistical techniques to control for or adjust these confounding variables.

Additionally, observational studies face constraints in their ability to establish causation. While they can identify associations and correlations between variables, they cannot prove causality or causal relationship. Establishing causation typically requires controlled experiments where researchers can manipulate independent variables systematically. In observational studies, researchers can only infer potential causation based on the observed associations.

Experimental Studies: Delving Deeper

In the intricate landscape of scientific research, we now turn our gaze toward experimental studies—a dynamic and powerful method that Santos Research Center, Corp. skillfully employs in our pursuit of knowledge.

What is an Experimental Study?

While some studies observe and gather data passively, experimental studies take a more proactive approach. Here, researchers actively introduce an intervention or treatment to an experiment group study its effects on one or more variables. This methodology empowers researchers to manipulate independent variables deliberately and examine their direct impact on dependent variables.

Experimental research are distinguished by their exceptional ability to establish cause-and-effect relationships. This invaluable characteristic allows researchers to unlock the mysteries of how one variable influences another, offering profound insights into the scientific questions at hand. Within the controlled environment of an experimental study, researchers can systematically test hypotheses, shedding light on complex phenomena.

Key Features of Experimental Studies

Central to statistical analysis, the rigor and reliability of experimental studies are several key features that ensure the validity of their findings.

Randomized Controlled Trials:  Randomization is a critical element in experimental studies, as it ensures that subjects are assigned to groups in a random assignment. This randomly assigned allocation minimizes the risk of unintentional biases and confounding variables, strengthening the credibility of the study's outcomes.

Control Groups:  Control groups play a pivotal role in experimental studies by serving as a baseline for comparison. They enable researchers to assess the true impact of the intervention being studied. By comparing the outcomes of the intervention group to those of survey members of the control group, researchers can discern whether the intervention caused the observed changes.

Blinding:  Both single-blind and double-blind techniques are employed in experimental studies to prevent biases from influencing the study or controlled trial's outcomes. Single-blind studies keep either the subjects or the researchers unaware of certain aspects of the study, while double-blind studies extend this blindness to both parties, enhancing the objectivity of the study.

These key features work in concert to uphold the integrity and trustworthiness of the results generated through experimental studies.

Advantages and Limitations of Experimental Studies

As with any research methodology, this one comes with its unique set of advantages and limitations.

Advantages:  These studies offer the distinct advantage of establishing causal relationships between two or more variables together. The controlled environment allows researchers to exert authority over variables, ensuring that changes in the dependent variable can be attributed to the independent variable. This meticulous control results in high-quality, reliable data that can significantly contribute to scientific knowledge.

Limitations:  However, experimental ones are not without their challenges. They may raise ethical concerns, particularly when the interventions involve potential risks to subjects. Additionally, their controlled nature can limit their real-world applicability, as the conditions in experiments may not accurately mirror those in the natural world. Moreover, executing an experimental study in randomized controlled, often demands substantial resources, with other variables including time, funding, and personnel.

Observational vs Experimental: A Side-by-Side Comparison

Having previously examined observational and experimental studies individually, we now embark on a side-by-side comparison to illuminate the key distinctions and commonalities between these foundational research approaches.

Key Differences and Notable Similarities

Methodologies

• Observational Studies : Characterized by passive observation, where researchers collect data without direct intervention, allowing the natural course of events to unfold.
• Experimental Studies : Involve active intervention, where researchers deliberately manipulate variables to discern their impact on specific outcomes, ensuring control over the experimental conditions.
• Observational Studies : Designed to identify patterns, correlations, and associations within existing data, shedding light on relationships within real-world settings.
• Experimental Studies : Geared toward establishing causality by determining the cause-and-effect relationships between variables, often in controlled laboratory environments.
• Observational Studies : Yield real-world data, reflecting the complexities and nuances of natural phenomena.
• Experimental Studies : Generate controlled data, allowing for precise analysis and the establishment of clear causal connections.

Observational studies excel at exploring associations and uncovering patterns within the intricacies of real-world settings, while experimental studies shine as the gold standard for discerning cause-and-effect relationships through meticulous control and manipulation in controlled environments. Understanding these differences and similarities empowers researchers to choose the most appropriate method for their specific research objectives.

When to Use Which: Practical Applications

The decision to employ either observational or experimental studies hinges on the research objectives at hand and the available resources. Observational studies prove invaluable when variable manipulation is impractical or ethically challenging, making them ideal for delving into long-term trends and uncovering intricate associations between certain variables (response variable or explanatory variable). On the other hand, experimental studies emerge as indispensable tools when the aim is to definitively establish causation and methodically control variables.

At Santos Research Center, Corp., our approach to both scientific study and methodology is characterized by meticulous consideration of the specific research goals. We recognize that the quality of outcomes hinges on selecting the most appropriate method of research study. Our unwavering commitment to employing both observational and experimental research studies further underscores our dedication to advancing scientific knowledge across diverse domains.

Conclusion: The Synergy of Experimental and Observational Studies in Research

In conclusion, both observational and experimental studies are integral to scientific research, offering complementary approaches with unique strengths and limitations. At Santos Research Center, Corp., we leverage these methodologies to contribute meaningfully to the scientific community.

Explore our projects and initiatives at Santos Research Center, Corp. by visiting our website or contacting us at (813) 249-9100, where our unwavering commitment to rigorous research practices and advancing scientific knowledge awaits.

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Observational Studies vs. Experiments ( Topic 3.2 )

Chapter 4 - day 6, day 12 day 13, learning targets.

Explain the concept of confounding and how it limits the ability to make cause-and-effect conclusions.

Distinguish between an observational study and an experiment, and identify the explanatory and response variables in each type of study.

Identify the experimental units and treatments in an experiment.

Activity: Does SAT Prep Produce Higher Scores? 

Answer Key:

When trying to decide if the SAT prep class caused an improvement in scores, we must recognize that those students who sign up for a prep class are likely different than the rest of the student population. They may be harder working, have more time for studying, and care more about their SAT score than those students who did not sign up.  We don’t know if the difference in the average SAT scores (1220 vs 1050) is because of these confounding variables or because of the SAT prep class.  We suggest making a visible list of these confounding variables.

Some students will suggest that there should be an SAT taken before and after the prep class.  This is a matched pairs design and will be discussed later.  For now, we want to focus on basic experimental design.

Students will likely use a paragraph to explain how they would design an experiment.  In the debrief of the activity, show them an outline of the experiment:

When students draw these outlines, they often want to skip the step that shows “Group 1” and “Group 2”.  This step is very important to show the purpose of the random assignment.  The random assignment is hopefully equally distributing the confounding variables into the two groups.  Some hard working students go in Group 1 and some in Group 2, and the same for all other confounding variables.  When the treatment is applied to each group, the groups are now different…and the only difference between the groups is the treatment.  So if there is a significant difference in SAT scores between the two groups, we can say that the SAT prep class caused the higher scores.  In a nutshell, random assignment allows us to show causation.

Random Sample vs Random Assignment

These two concepts allow us to make different conclusions.

Random Sample : A random sample should be representative of the population from which it was taken, so a random sample allows us to generalize our conclusion to the population.

In the SAT prep class example, we have 44 student volunteers.  Thus we cannot generalize our conclusion to all students.  Instead we can only make a conclusion for these 44 students or other students like the ones who volunteered for the experiment.

Random Assignment : Random assignment hopefully will equally distribute the various levels of the confounding variables into the treatment groups, so that only difference between groups is the treatment.  If there is a significant difference between the groups, the random assignment allows us to conclude that the treatment caused the difference.

Luke's Lesson Notes

Here is a brief video highlighting some key information to help you prepare to teach this lesson.

Experimental Studies and Observational Studies

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Experimental studies: Experiments, Randomized controlled trials (RCTs) ; Observational studies: Non-experimental studies, Non-manipulation studies, Naturalistic studies

Definitions

The experimental study is a powerful methodology for testing causal relations between one or more explanatory variables (i.e., independent variables) and one or more outcome variables (i.e., dependent variable). In order to accomplish this goal, experiments have to meet three basic criteria: (a) experimental manipulation (variation) of the independent variable(s), (b) randomization – the participants are randomly assigned to one of the experimental conditions, and (c) experimental control for the effect of third variables by eliminating them or keeping them constant.

In observational studies, investigators observe or assess individuals without manipulation or intervention. Observational studies are used for assessing the mean levels, the natural variation, and the structure of variables, as well as...

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Pinquart, M. (2021). Experimental Studies and Observational Studies. In: Gu, D., Dupre, M.E. (eds) Encyclopedia of Gerontology and Population Aging. Springer, Cham. https://doi.org/10.1007/978-3-030-22009-9_573

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Section 1.2: Observational Studies versus Designed Experiments

• 1.1 Introduction to the Practice of Statistics
• 1.2 Observational Studies versus Designed Experiments
• 1.3 Random Sampling
• 1.4 Bias in Sampling
• 1.5 The Design of Experiments

By the end of this lesson, you will be able to...

• distinguish between an observational study and a designed experiment
• identify possible lurking variables
• explain the various types of observational studies

For a quick overview of this section, watch this short video summary:

To begin, we're going to discuss some of the ways to collect data. In general, there are a few standards:

• existing sources
• survey sampling
• designed experiments

Most of us associate the word census with the U.S. Census, but it actually has a broader definition. Here's typical definition:

A census is a list of all individuals in a population along with certain characteristics of each individual.

The nice part about a census is that it gives us all the information we want. Of course, it's usually impossible to get - imagine trying to interview every single ECC student . That'd be over 10,000 interviews!

So if we can't get a census, what do we do? A great source of data is other studies that have already been completed. If you're trying to answer a particular question, look to see if someone else has already collected data about that population. The moral of the story is this: Don't collect data that have already been collected!

Observational Studies versus Designed Experiments

Now to one of the main objectives for this section. Two other very common sources of data are observational studies and designed experiments . We're going to take some time here to describe them and distinguish between them - you'll be expected to be able to do the same in homework and on your first exam.

The easiest examples of observational studies are surveys. No attempt is made to influence anything - just ask questions and record the responses. By definition,

An observational study measures the characteristics of a population by studying individuals in a sample, but does not attempt to manipulate or influence the variables of interest.

For a good example, try visiting the Pew Research Center . Just click on any article and you'll see an example of an observational study. They just sample a particular group and ask them questions.

In contrast, designed experiments explicitly do attempt to influence results. They try to determine what affect a particular treatment has on an outcome.

A designed experiment applies a treatment to individuals (referred to as experimental units or subjects ) and attempts to isolate the effects of the treatment on a response variable .

For a nice example of a designed experiment, check out this article from National Public Radio about the effect of exercise on fitness.

So let's look at a couple examples.

Visit this link from Science Daily , from July 8th, 2008. It talks about the relationship between Post-Traumatic Stress Disorder (PTSD) and heart disease. After reading the article carefully, try to decide whether it was an observational study or a designed experiment

What was it?

This was a tricky one. It was actually an observational study . The key is that the researchers didn't force the veterans to have PTSD, they simply observed the rate of heart disease for those soldiers who have PTSD and the rate for those who do not.

Visit this link from the Gallup Organization , from June 17th, 2008. It looks at what Americans' top concerns were at that point. Read carefully and think of the how the data were collected. Do you think this was an observational study or a designed experiment? Why?

Think carefully about which you think it was, and just as important - why? When you're ready, click the link below.

If you were thinking that this was an observational study , you were right!The key here is that the individuals sampled were just asked what was important to them. The study didn't try to impose certain conditions on people for a set amount of time and see if those conditions affected their responses.

This last example is regarding the "low-carb" Atkins diet, and how it compares with other diets. Read through this summary of a report in the New England Journal of Medicine and see if you can figure out whether it's an observational study or a designed experiment.

As expected, this was a designed experiment , but do you know why? The key here is they forced individuals to maintain a certain diet, and then compared the participants' health at the end.

Probably the biggest difference between observational studies and designed experiments is the issue of association versus causation . Since observational studies don't control any variables, the results can only be associations . Because variables are controlled in a designed experiment, we can have conclusions of causation .

Look back over the three examples linked above and see if all three reported their results correctly. You'll often find articles in newspapers or online claiming one variable caused a certain response in another, when really all they had was an association from doing an observational study.

The discussion of the differences between observational studies and designed experiments may bring up an interesting question - why are we worried so much about the difference?

We already mentioned the key at the end of the previous page, but it bears repeating here:

Observational studies only allow us to claim association ,not causation .

The primary reason behind this is something called a lurking variable (sometimes also termed a confounding factor , among other similar terms).

A lurking variable is a variable that affects both of the variables of interest, but is either not known or is not acknowledged.

Consider the following example, from The Washington Post:

Coffee may have health benefits and may not pose health risks for many people

By Carolyn Butler Tuesday, December 22, 2009

Of all the relationships in my life, by far the most on-again, off-again has been with coffee: From that initial, tentative dalliance in college to a serious commitment during my first real reporting job to breaking up altogether when I got pregnant, only to fail miserably at quitting my daily latte the second time I was expecting. More recently the relationship has turned into full-blown obsession and, ironically, I often fall asleep at night dreaming of the delicious, satisfying cup of joe that awaits, come morning.

[...] Rest assured: Not only has current research shown that moderate coffee consumption isn't likely to hurt you, it may actually have significant health benefits. "Coffee is generally associated with a less health-conscious lifestyle -- people who don't sleep much, drink coffee, smoke, drink alcohol," explains Rob van Dam, an assistant professor in the departments of nutrition and epidemiology at the Harvard School of Public Health. He points out that early studies failed to account for such issues and thus found a link between drinking coffee and such conditions as heart disease and cancer, a link that has contributed to java's lingering bad rep. "But as more studies have been conducted-- larger and better studies that controlled for healthy lifestyle issues --the totality of efforts suggests that coffee is a good beverage choice."

Source: Washington Post

What is this article telling us? If you look at the parts in bold, you can see that Professor van Dam is describing a lurking variable: lifestyle. In past studies, this variable wasn't accounted for. Researchers in the past saw the relationship between coffee and heart disease, and came to the conclusion that the coffee was causing the heart disease.

But since those were only observational studies, the researchers could only claim an association . In that example, the lifestyle choices of individuals was affecting both their coffee use and other risks leading to heart disease. So "lifestyle" would be an example of a lurking variable in that example.

For more on lurking variables, check out this link from The Math Forum and this one from The Psychology Wiki . Both give further examples and illustrations.

With all the problems of lurking variables, there are many good reasons to do an observational study. For one, a designed experiment may be impractical or even unethical (imagine a designed experiment regarding the risks of smoking). Observational studies also tend to cost much less than designed experiments, and it's often possible to obtain a much larger data set than you would with a designed experiment. Still, it's always important to remember the difference in what we can claim as a result of observational studies versus designed experiments.

Types of Observational Studies

There are three major types of observational studies, and they're listed in your text: cross-sectional studies, case-control studies, and cohort studies.

Cross-sectional Studies

This first type of observational study involves collecting data about individuals at a certain point in time. A researcher concerned about the effect of working with asbestos might compare the cancer rate of those who work with asbestos versus those who do not.

Cross-sectional studies are cheap and easy to do, but they don't give very strong results. In our quick example, we can't be sure that those working with asbestos who don't report cancer won't eventually develop it. This type of study only gives a bit of the picture, so it is rarely used by itself. Researchers tend to use a cross-sectional study to first determine if their might be a link, and then later do another study (like one of the following) to further investigate.

Case-control Studies

Case-control studies are frequently used in the medical community to compare individuals with a particular characteristic (this group is the case )with individuals who do not have that characteristic (this group is the control ). Researchers attempt to select homogeneous groups, so that on average, all other characteristics of the individuals will be similar, with only the characteristic in question differing.

One of the most famous examples of this type of study is the early research on the link between smoking and lung cancer in the United Kingdom by Richard Doll and A. Bradford Hill. In the 1950's, almost 80% of adults in the UK were smokers, and the connection between smoking and lung cancer had not yet been established. Doll and Hill interviewed about 700 lung cancer patients to try to determine a possible cause.

This type of study is retrospective ,because it asks the individuals to look back and describe their habits(regarding smoking, in this case). There are clear weaknesses in a study like this, because it expects individuals to not only have an accurate memory, but also to respond honestly. (Think about a study concerning drug use and cognitive impairment.) Not only that, we discussed previously that such a study may prove association , but it cannot prove causation .

Cohort Studies

A cohort describes a group of individuals, and so a cohort study is one in which a group of individuals is selected to participate in a study. The group is then observed over a period of time to determine if particular characteristics affect a response variable.

Based on their earlier research, Doll and Hill began one of the largest cohort studies in 1951. The study was again regarding the link between smoking and lung cancer. The study began with 34,439 male British doctors, and followed them for over 50 years. Doll and Hill first reported findings in 1954 in the British Medical Journal , and then continued to report their findings periodically afterward. Their last report was in 2004,again published in the British Medical Journal . This last report reflected on 50 years of observational data from the cohort.

This last type of study is called prospective , because it begins with the group and then collects data over time. Cohort studies are definitely the most powerful of the observational studies,particularly with the quantity and quality of data in a study like the previous one.

Let's look at some examples.

A recent article in the BBC News Health section described a study concerning dementia and "mid-life ills". According to the article, researches followed more than 11,000 people over a period of 12-14 years. They found that smoking, diabetes, and high blood pressure were all factors in the onset of dementia.

What type of observational study was this? Cross-sectional, case-control,or cohort?

Because the researchers tracked the 11,000 participants, this is a cohort study .

In 1993, the National Institute of Environmental Health Sciences funded a study in Iowa regarding the possible relationship between radon levels and the incidence of cancer. The study gathered information from 413 participants who had developed lung cancer and compared those results with 614 participants who did not have lung cancer.

What type of study was this?

This study was retrospective - gathering information about the group of interest (those with cancer) and comparing them with a control group(those without cancer). This is an example of a case-control study .

Thought his may seem similar to a cross-sectional study, it differs in that the individuals are "matched" (with cancer vs. without cancer)and the individuals are expected to look back in time and describe their time spent in the home to determine their radon exposure.

In 2004, researchers published an article in the New England Journal of Medicine regarding the relationship between the mental health of soldiers exposed to combat stress. The study collected information from soldiers in four combat infantry units either before their deployment to Iraq or three to four months after their return from combat duty.

Since this was simply a survey given over a short period of time to try to examine the effect of combat duty, this was a cross-sectional study. Unlike the previous example, it did not ask the participants to delve into their history, nor did it explicitly "match" soldiers with a particular characteristic.

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1.5: Observational Studies and Sampling Strategies

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Observational Studies

Generally, data in observational studies are collected only by monitoring what occurs, what occurs, while experiments require the primary explanatory variable in a study be assigned for each subject by the researchers. Making causal conclusions based on experiments is often reasonable. However, making the same causal conclusions based on observational data can be treacherous and is not recommended. Thus, observational studies are generally only sufficient to show associations.

Exercise \(\PageIndex{1}\)

Suppose an observational study tracked sunscreen use and skin cancer, and it was found that the more sunscreen someone used, the more likely the person was to have skin cancer. Does this mean sunscreen causes skin cancer?

No. See the paragraph following the exercise for an explanation.

Some previous research tells us that using sunscreen actually reduces skin cancer risk, so maybe there is another variable that can explain this hypothetical association between sunscreen usage and skin cancer. One important piece of information that is absent is sun exposure. If someone is out in the sun all day, she is more likely to use sunscreen and more likely to get skin cancer. Exposure to the sun is unaccounted for in the simple investigation.

Sun exposure is what is called a confounding variable (also called a lurking variable, confounding factor, or a confounder), which is a variable that is correlated with both the explanatory and response variables. While one method to justify making causal conclusions from observational studies is to exhaust the search for confounding variables, there is no guarantee that all confounding variables can be examined or measured. In the same way, the county data set is an observational study with confounding variables, and its data cannot easily be used to make causal conclusions.

Exercise \(\PageIndex{2}\)

Figure 1.9 shows a negative association between the homeownership rate and the percentage of multi-unit structures in a county. However, it is unreasonable to conclude that there is a causal relationship between the two variables. Suggest one or more other variables that might explain the relationship visible in Figure 1.9.

Answers will vary. Population density may be important. If a county is very dense, then this may require a larger fraction of residents to live in multi-unit structures. Additionally, the high density may contribute to increases in property value, making homeownership infeasible for many residents.

Observational studies come in two forms: prospective and retrospective studies. A prospective study identifies individuals and collects information as events unfold. For instance, medical researchers may identify and follow a group of similar individuals over many years to assess the possible influences of behavior on cancer risk. One example of such a study is The Nurses Health Study, started in 1976 and expanded in 1989. This prospective study recruits registered nurses and then collects data from them using questionnaires. Retrospective studies collect data after events have taken place, e.g. researchers may review past events in medical records. Some data sets, such as county, may contain both rospectively- and retrospectively-collected variables. Local governments prospectively collect some variables as events unfolded (e.g. retails sales) while the federal government retrospectively collected others during the 2010 census (e.g. county population counts).

Three Sampling Methods

Almost all statistical methods are based on the notion of implied randomness. If observational data are not collected in a random framework from a population, these statistical methods are not reliable. Here we consider three random sampling techniques: simple, stratified, and cluster sampling. Figure 1.14 provides a graphical representation of these techniques.

Simple random sampling is probably the most intuitive form of random sampling. Consider the salaries of Major League Baseball (MLB) players, where each player is a member of one of the league's 30 teams. To take a simple random sample of 120 baseball players and their salaries from the 2010 season, we could write the names of that season's 828 players onto slips of paper, drop the slips into a bucket, shake the bucket around until we are sure the names are all mixed up, then draw out slips until we have the sample of 120 players. In general, a sample is referred to as "simple random" if each case in the population has an equal chance of being included in the nal sample and knowing that a case is included in a sample does not provide useful information about which other cases are included.

Stratified sampling is a divide-and-conquer sampling strategy. The population is divided into groups called strata . The strata are chosen so that similar cases are grouped together, then a second sampling method, usually simple random sampling, is employed within each stratum. In the baseball salary example, the teams could represent the strata; some teams have a lot more money (we're looking at you, Yankees). Then we might randomly sample 4 players from each team for a total of 120 players.

Stratified sampling is especially useful when the cases in each stratum are very similar with respect to the outcome of interest. The downside is that analyzing data from a stratified sample is a more complex task than analyzing data from a simple random sample. The analysis methods introduced in this book would need to be extended to analyze data collected using stratified sampling.

Example \(\PageIndex{1}\)

Why would it be good for cases within each stratum to be very similar?

We might get a more stable estimate for the subpopulation in a stratum if the cases are very similar. These improved estimates for each subpopulation will help us build a reliable estimate for the full population.

A cluster sample is much like a two-stage simple random sample. We break up the population into many groups, called clusters . Then we sample a fixed number of clusters and collect a simple random sample within each cluster. This technique is similar to stratified sampling in its process, except that there is no requirement in cluster sampling to sample from every cluster. Stratified sampling requires observations be sampled from every stratum.

Sometimes cluster sampling can be a more economical random sampling technique than the alternatives. Also, unlike stratified sampling, cluster sampling is most helpful when there is a lot of case-to-case variability within a cluster but the clusters themselves don't look very different from one another. For example, if neighborhoods represented clusters, then this sampling method works best when the neighborhoods are very diverse. A downside of cluster sampling is that more advanced analysis techniques are typically required, though the methods in this book can be extended to handle such data.

Example \(\PageIndex{3}\)

Suppose we are interested in estimating the malaria rate in a densely tropical portion of rural Indonesia. We learn that there are 30 villages in that part of the Indonesian jungle, each more or less similar to the next. Our goal is to test 150 individuals for malaria. What sampling method should be employed?

A simple random sample would likely draw individuals from all 30 villages, which could make data collection extremely expensive. Stratified sampling would be a challenge since it is unclear how we would build strata of similar individuals. However, cluster sampling seems like a very good idea. First, we might randomly select half the villages, then randomly select 10 people from each. This would probably reduce our data collection costs substantially in comparison to a simple random sample and would still give us reliable information.

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An introduction to different types of study design

Posted on 6th April 2021 by Hadi Abbas

Study designs are the set of methods and procedures used to collect and analyze data in a study.

Broadly speaking, there are 2 types of study designs: descriptive studies and analytical studies.

Descriptive studies

• Describes specific characteristics in a population of interest
• The most common forms are case reports and case series
• In a case report, we discuss our experience with the patient’s symptoms, signs, diagnosis, and treatment
• In a case series, several patients with similar experiences are grouped.

Analytical Studies

Analytical studies are of 2 types: observational and experimental.

Observational studies are studies that we conduct without any intervention or experiment. In those studies, we purely observe the outcomes.  On the other hand, in experimental studies, we conduct experiments and interventions.

Observational studies

Observational studies include many subtypes. Below, I will discuss the most common designs.

Cross-sectional study:

• This design is transverse where we take a specific sample at a specific time without any follow-up
• It allows us to calculate the frequency of disease ( p revalence ) or the frequency of a risk factor
• This design is easy to conduct
• For example – if we want to know the prevalence of migraine in a population, we can conduct a cross-sectional study whereby we take a sample from the population and calculate the number of patients with migraine headaches.

Cohort study:

• We conduct this study by comparing two samples from the population: one sample with a risk factor while the other lacks this risk factor
• It shows us the risk of developing the disease in individuals with the risk factor compared to those without the risk factor ( RR = relative risk )
• Prospective : we follow the individuals in the future to know who will develop the disease
• Retrospective : we look to the past to know who developed the disease (e.g. using medical records)
• This design is the strongest among the observational studies
• For example – to find out the relative risk of developing chronic obstructive pulmonary disease (COPD) among smokers, we take a sample including smokers and non-smokers. Then, we calculate the number of individuals with COPD among both.

Case-Control Study:

• We conduct this study by comparing 2 groups: one group with the disease (cases) and another group without the disease (controls)
• This design is always retrospective
•  We aim to find out the odds of having a risk factor or an exposure if an individual has a specific disease (Odds ratio)
•  Relatively easy to conduct
• For example – we want to study the odds of being a smoker among hypertensive patients compared to normotensive ones. To do so, we choose a group of patients diagnosed with hypertension and another group that serves as the control (normal blood pressure). Then we study their smoking history to find out if there is a correlation.

Experimental Studies

• Also known as interventional studies
• Can involve animals and humans
• Pre-clinical trials involve animals
• Clinical trials are experimental studies involving humans
• In clinical trials, we study the effect of an intervention compared to another intervention or placebo. As an example, I have listed the four phases of a drug trial:

I:  We aim to assess the safety of the drug ( is it safe ? )

II: We aim to assess the efficacy of the drug ( does it work ? )

III: We want to know if this drug is better than the old treatment ( is it better ? )

IV: We follow-up to detect long-term side effects ( can it stay in the market ? )

• In randomized controlled trials, one group of participants receives the control, while the other receives the tested drug/intervention. Those studies are the best way to evaluate the efficacy of a treatment.

Finally, the figure below will help you with your understanding of different types of study designs.

References (pdf)

You may also be interested in the following blogs for further reading:

An introduction to randomized controlled trials

Case-control and cohort studies: a brief overview

Cohort studies: prospective and retrospective designs

Prevalence vs Incidence: what is the difference?

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No Comments on An introduction to different types of study design

you are amazing one!! if I get you I’m working with you! I’m student from Ethiopian higher education. health sciences student

Very informative and easy understandable

You are my kind of doctor. Do not lose sight of your objective.

Wow very erll explained and easy to understand

I’m Khamisu Habibu community health officer student from Abubakar Tafawa Balewa university teaching hospital Bauchi, Nigeria, I really appreciate your write up and you have make it clear for the learner. thank you

well understood,thank you so much

Well understood…thanks

Simply explained. Thank You.

Thanks a lot for this nice informative article which help me to understand different study designs that I felt difficult before

That’s lovely to hear, Mona, thank you for letting the author know how useful this was. If there are any other particular topics you think would be useful to you, and are not already on the website, please do let us know.

it is very informative and useful.

thank you statistician

Fabulous to hear, thank you John.

Thanks for this information

Thanks so much for this information….I have clearly known the types of study design Thanks

That’s so good to hear, Mirembe, thank you for letting the author know.

Very helpful article!! U have simplified everything for easy understanding

I’m a health science major currently taking statistics for health care workers…this is a challenging class…thanks for the simified feedback.

That’s good to hear this has helped you. Hopefully you will find some of the other blogs useful too. If you see any topics that are missing from the website, please do let us know!

Hello. I liked your presentation, the fact that you ranked them clearly is very helpful to understand for people like me who is a novelist researcher. However, I was expecting to read much more about the Experimental studies. So please direct me if you already have or will one day. Thank you

Dear Ay. My sincere apologies for not responding to your comment sooner. You may find it useful to filter the blogs by the topic of ‘Study design and research methods’ – here is a link to that filter: https://s4be.cochrane.org/blog/topic/study-design/ This will cover more detail about experimental studies. Or have a look on our library page for further resources there – you’ll find that on the ‘Resources’ drop down from the home page.

However, if there are specific things you feel you would like to learn about experimental studies, that are missing from the website, it would be great if you could let me know too. Thank you, and best of luck. Emma

Great job Mr Hadi. I advise you to prepare and study for the Australian Medical Board Exams as soon as you finish your undergrad study in Lebanon. Good luck and hope we can meet sometime in the future. Regards ;)

You have give a good explaination of what am looking for. However, references am not sure of where to get them from.

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Expertise-based Randomized Controlled Trials

This blog summarizes the concepts of Expertise-based randomized controlled trials with a focus on the advantages and challenges associated with this type of study.

A well-designed cohort study can provide powerful results. This blog introduces prospective and retrospective cohort studies, discussing the advantages, disadvantages and use of these type of study designs.

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Observational Versus Experimental Studies: What’s the Evidence for a Hierarchy?

John concato.

Department of Internal Medicine, Yale University School of Medicine, New Haven, Connecticut 06510, and the Clinical Epidemiology Research Center, West Haven Veterans Affairs Medical Center, West Haven, Connecticut 06516

Summary: The tenets of evidence-based medicine include an emphasis on hierarchies of research design (i.e., study architecture). Often, a single randomized, controlled trial is considered to provide “truth,” whereas results from any observational study are viewed with suspicion. This paper describes information that contradicts and discourages such a rigid approach to evaluating the quality of research design. Unless a more balanced strategy evolves, new claims of methodological authority may be just as problematic as the traditional claims of medical authority that have been criticized by proponents of evidence-based medicine.

INTRODUCTION

Evidence-based medicine classifies studies into grades of evidence based on research architecture. 1 , 2 This hierarchical approach to study design has been promoted widely in individual reports, meta-analyses, consensus statements, and educational materials for clinicians. For example, a prominent publication 3 reserved the highest grade for “at least one properly randomized, controlled trial,” and the lowest grade for descriptive studies (e.g., case series) and expert opinion. Observational studies, including cohort and case-control, fall into intermediate levels (Table ​ (Table1). 1 ). Although the quality of studies is sometimes evaluated within each grade, each category is considered methodologically superior to level(s) below it.

“Grades of Evidence” Rating the Purported Quality of Study Design 3

The ascendancy of randomized, controlled trials (experimental studies) to become the “gold standard” strategy for assessing the effectiveness of therapeutic agents 4 – 6 was based in part on a landmark paper 7 comparing published articles that used randomized and historical control trial designs. The corresponding results found that the agent being tested was considered effective in 44 of 56 (79%) historical controlled trials, but only 10 of 50 (20%) randomized, controlled trials. The authors concluded “biases in patient selection may irretrievably weight the outcome of historical controlled trials in favor of new therapies.” 7

Although the cited article 7 compared randomized, controlled trials to historical controlled trials only, contemporary criticisms of observational studies also include cohort studies with concurrent (nonhistorical) selection of control subjects as well as case-control designs. 8 A possibility exists, however, that data based on “weaker” forms of observational studies can be used mistakenly to criticize all observational research. The premise of this paper is that evidence-based medicine has contributed to the development of a rigid hierarchy of research design that underestimates the limitations of randomized, controlled trials, and overstates the limitations of observational studies.

WHY USE A HIERARCHY OF RESEARCH DESIGN?

A hierarchy of types of research design would be desirable for providing a “checklist” to evaluate clinical studies, but the complexity of medical research suggests that such approaches are overly simplistic. Although randomization protects against certain types of bias that can threaten the validity of a study (i.e., obtaining the correct answer to the question posed, among the study participants involved), a corresponding randomized, controlled trials protocol may restrict the sample of patients selected, the intervention delivered, or the outcome(s) measured, impairing the so-called generalizability of a study (i.e., the extent to which it applies to patients in the “real world”). For example, a randomized, controlled trial may exclude older patients, it may administer therapy in a manner that is difficult to replicate in actual practice, or it may use short-term or surrogate endpoints. In addition, numerous problems can occur when randomized, controlled trials are conducted improperly. Conversely, if properly-conducted observational studies can overcome threats to validity (using strategies discussed later in this paper), and if such studies incorporate more relevant clinical features, then corresponding results would likely be very generalizable to practicing clinicians. Yet, the conventional wisdom suggests that observational studies consistently provide biased results compared with randomized, controlled trials, regardless of the type of observational study or how well it was conducted. The remainder of this paper will focus on these issues.

EVIDENCE AGAINST A RIGID HIERARCHY

A recent study recognized that systematic reviews and meta-analyses offered an opportunity to test the implicit assumptions of grades (or levels) of evidence and similar hierarchies of research design. 9 We identified particular exposure-outcome associations that were studied with both randomized, controlled trials as well as cohort or case-control studies. The major distinctions of our approach (compared with prior research), however, were that we evaluated observational studies that used concurrent (not historical) control subjects, and we focused on summary results rather than individual study findings. The variation in point estimates of exposure-outcome associations provided data to confirm or refute the assumptions regarding observational studies, as well as the strengths and limitations of a “design hierarchy.”

Our methods involved identifying meta-analyses published in five major journals ( Annals of Internal Medicine , British Medical Journal , Journal of the American Medical Association , Lancet , and New England Journal of Medicine ) from 1991 to 1995, using searches of MEDLINE, with the terms “meta-analysis, ” “meta-analyses,” “pooling,” “combining,” “overview,” and “aggregation.” Additional references were found in Current Contents , supplemented by manual searches of the relevant journals. The meta-analyses identified via this process were then classified by consensus as including clinical trials only, observational studies only, or both. Clinical trials were defined as studies that used randomized interventions; observational studies included cohort or case-control designs. Meta-analyses were excluded if they were based on cohort studies with historical control subjects, or clinical trials with nonrandom assignment of interventions, or if they did not report results in the format of a point estimate (e.g., relative risk, odds ratio) and confidence intervals. The remaining meta-analyses were then reviewed, and the original studies cited in the bibliographies were retrieved.

The search strategy yielded 102 citations for meta-analyses, mainly involving (as expected) randomized, controlled trials only. Data for five clinical topics 10 – 15 met our eligibility criteria and provided sufficient data for analysis, involving 99 original articles and 1,871,681 total study subjects. The summary (pooled) point estimates are presented in Table ​ Table2, 2 , and the ranges of the point estimates are displayed in Figure 1 . For example, the relationship between treatment of hypertension and the first occurrence of stroke (i.e., primary prevention) was examined in meta-analyses of 14 randomized, controlled trials 15 and seven cohort studies. 10 The pooled results from randomized, controlled trials ( N = 36,894) found a point estimate of 0.58 (95% confidence interval 0.50–0.67); the pooled results from observational studies ( N = 405,511) found an adjusted point estimate of 0.62 (95% confidence interval 0.60–0.65). Results for other associations (Table ​ (Table2) 2 ) were also similar, based on data from randomized, controlled trials and observational studies. In another example, the effectiveness of bacillus Calmette-Guerin (BCG) vaccine against tuberculosis was examined in a meta-analysis 11 that included 13 randomized trials ( N = 359,922 subjects) with a pooled relative risk of 0.49 (95% confidence interval 0.34–0.70), and 10 case-control studies ( N = 6511 subjects) with a pooled odds ratio of 0.50 (95% confidence interval 0.39–0.65).

Range of relative risks or odds ratios, based on the following types of research design: bacillus Calmette-Guerin vaccine and tuberculosis (13 randomized, controlled trials and 10 case-control studies), screening mammography and breast cancer mortality (eight randomized, controlled trials and four case-control studies), treatment of hyperlipidemia and traumatic death among men (four randomized, controlled trials and 14 cohort studies), treatment of hypertension and stroke among men (11 randomized, controlled trials and seven cohort studies), treatment of hypertension and coronary heart disease among men (13 randomized, controlled trials and nine cohort studies). Filled circles, randomized, controlled trials; open circles, observational studies. (Reproduced with permission.)

Total Number of Subjects and Summary Estimates for the Impact of Five Interventions (“Clinical Topics”) Based on Type of Research Design

CHD = coronary heart disease; CI = confidence interval; RCT = randomized, controlled trial.

The results of our investigation contradict the idea of a “fixed” hierarchy of study design in clinical research. Importantly, another publication 16 addressing the same general question found “little evidence that estimates of treatment effects in observational studies reported after 1984 are either consistently larger than or qualitatively different from those obtained in randomized, controlled trials.” In addition, an evaluation 17 of the literature on screening mammography found similar results to ours on that particular topic. Thus, contrary to prevailing beliefs, average results from well-designed observational (cohort and case-control) studies did not systematically overestimate the magnitude of exposure-outcome associations reported in randomized, controlled trials. Rather, the summary results from randomized, controlled trials and observational studies were remarkably similar for each clinical question addressed.

Another finding, also contrary to current perceptions, was that observational studies individually demonstrated less variability (heterogeneity) in point estimates compared to the variability in point estimates observed in randomized, controlled trials on the same topic ( FIG. 1 ). Indeed, only among randomized, controlled trials did individual studies report results that were opposite to the direction of the pooled point estimate, representing a “paradoxical” finding (e.g., treatment of hypertension was associated with higher rates of coronary heart disease in several clinical trials).

One possible explanation for the finding that observational studies were less prone to heterogeneity in results (compared with randomized, controlled trials) is that each observational study is more likely to include a broad representation of the at-risk population. In addition, less opportunity exists for differences in the management of subjects “across” observational studies. For example, although general agreement exists that physicians do not use therapeutic agents in a uniform way, an observational study would generally include patients with a wider spectrum of severity (regarding the disease of interest), more comorbid ailments, and treatments that were tailored for each individual patient. In contrast, randomized, controlled trials may have distinct groups of patients based on specific inclusion and exclusion criteria, and the experimental protocol for therapy may not be representative of clinical practice. Therefore, randomized, controlled trials often have limited generalizability.

ADDITIONAL EVIDENCE AGAINST A RIGID HIERARCHY

At the time of our previous study, 9 investigations had already shown that observational cohort studies often produce results similar to those of randomized, controlled trials, when using similar criteria to assemble study participants and suitable methodological precautions. For example, an analysis of 18 randomized and nonrandomized studies in health services research found that treatment effects may differ based on research design but that “one method does not give a consistently greater effect than the other.” 18 In that assessment, results were found to be most similar when exclusion criteria across studies were comparable, and when prognostic factors were accounted for in observational studies. In addition, a specific strategy used to strengthen observational studies (called a “restrictive cohort” design 19 ) adapts principles of randomized, controlled trials to 1) identify a zero-time for determining patient eligibility and baseline prognostic risk, 2) use inclusion and exclusion criteria similar to clinical trials, 3) adjust for differences in baseline susceptibility for the outcome, and 4) use similar statistical strategies (e.g., intention-to-treat) as in randomized, controlled trials. When these procedures were used in a cohort study 19 evaluating the benefit of beta blockers after recovery from myocardial infarction, the restricted cohort produced results consistent with corresponding findings from the Beta-Blocker Heart Attack Trial. 20

A second line of evidence supporting our contention that research design should not be considered a rigid hierarchy is also available in the literature of other scientific disciplines that carry out subject-based intervention trials. Examples include a comprehensive review of psychological, educational, and behavioral treatment research 21 ; the findings from this review did not support a contention that observational studies overestimate effects relative to randomized, controlled trials.

Further evidence against a rigid hierarchy is based on results from the trials themselves. For example, a review of more than 200 randomized, controlled trials found numerous individual trials that were supportive, equivocal, or nonsupportive for each of 36 clinical topics. 22 Several publications have discussed various aspects of randomized, controlled trials in neurology. 23 – 28 Recent publications indicate that randomized, controlled trials continue to generate conflicting results, e.g., addressing the question of whether therapy with monoclonal antibodies improve outcomes among patients with septic shock. 29 , 30 In addition, results of “large, simple” randomized, controlled trials contribute to the evidence of contradictory results from randomized, controlled trials; one report found that results of meta-analyses based on randomized, controlled trials were often discordant with findings from large, simple trials on the same clinical topic. 31 Regardless of the reasons that individual randomized, controlled trials produce heterogeneous results, the available evidence indicates that a single randomized trial (or only one observational study) cannot be expected to provide a gold standard result for all clinical situations.

EXAMPLES FROM THE LITERATURE AND IMPLICATIONS FOR CLINICAL CARE

Vitamin e and coronary heart disease.

The Heart Outcomes Prevention Evaluation (HOPE) study, 32 a randomized, controlled trial, was cited as helping to “restrain earlier observational claims that vitamin E lowers the risk of cardiovascular disease.” 33 A review of this topic illustrates the methodological issues involved. Several observational studies 34 – 36 found a “positive” association; in contrast, the HOPE study suggested that vitamin E has no effect on cardiovascular outcomes. Yet, a thorough examination of randomized, controlled trials on this topic provides a more complete assessment. Although two randomized, controlled trials 37 , 38 also found no effect on mortality, two other randomized, controlled trials 39 , 40 found decreased mortality associated with vitamin E. Thus, data from clinical trials are themselves contradictory, and selecting one randomized, controlled trial as a gold standard to criticize observational studies is overly simplistic.

This clinical topic was used to support the statement that “…society expects us to evaluate new healthcare interventions by the most scientifically sound and rigorous methods available. Although observational studies often are cheaper, quicker, and less difficult to carry out, we should not lose sight of one simple fact: ignorance calls for careful experimentation. This means high-quality randomized, controlled trials, not observations that reflect personal choices and beliefs.” 33 An alternative, more rigorous, and less dogmatic approach would be to compare published studies based on components of their research design, whether randomized or observational (Table ​ (Table3), 3 ), and not make a priori judgments regarding a single randomized, controlled trial constituting a gold standard.

Foci for Comparison of Observational and Experimental Study Designs: Example of Vitamin E and Coronary Disease

Hormone replacement therapy and coronary heart disease

Another example of this controversy involves hormone replacement therapy disease for postmenopausal women. In summary, observational studies (such as the Nurses Health Study 41 ) suggested a protective benefit of hormones; whereas randomized, controlled trials (including the Women’s Health Initiative 42 and the Heart and Estrogen/Progestin Replacement Study 43 ) pointed to no benefit, or even harm. Rather than assume the randomized, controlled trials inherently reveal “truth,” potential explorations for the discordant findings could be explored. First, it should be noted that results of randomized, controlled trials and observational studies are remarkably consistent for most outcomes in studies of hormone replacement therapy, including stroke, breast cancer, colorectal cancer, hip fracture, and pulmonary embolism. The outcome of coronary artery disease has received most attention, and has been described as an anomaly. 44

An assessment of this topic described plausible methodological and biological explanations for the differences in findings. 44 For example, available data indicate that women with higher socioeconomic status are more likely to be hormone replacement therapy users and less likely to have coronary artery disease, suggesting that the observational studies were vulnerable to “healthy user bias” (or “confounding”) in this context. (Confounding, as a general term, occurs when a third variable, socioeconomic status in this situation, is related to both the exposure [hormone therapy] and outcome [coronary artery disease] variables for the association of interest. The exposure variable [hormone therapy] would then be described as a “marker” for the confounding variable, rather than actually causing the outcome.) In addition, the randomized, controlled trials themselves have been criticized for having bias. 45

Another issue involves incomplete capture of early clinical events. 44 Observational studies typically enroll participants who have been taking hormone replacement therapy for some time, whereas randomized clinical trials initiate therapy in nonusers. Accordingly, clinical events that occur soon after initiating the medication would be captured by randomized, controlled trials, but typical observational studies assess what is likely to happen when patients remain on therapy for an extended period of time (patients initiating therapy recently would account for a very small proportion of the overall population). Other explanations for discordant results involve differences in protocols among observational studies and randomized, controlled trials. For example, daily combinations of estrogen and progestin were administered in Women’s Health Initiative 42 and Heart and Estrogen/Progestin Replacement Study, 43 compared with estrogen alone or combined regimens for 10–14 days per month in observational studies such as the Nurses Health Study. 41

These differences are not “fatal flaws” of observational studies, unless a rigid opinion is adopted that designates randomized, controlled trials as infallible. Most of the issues raised involve either methodological differences without a definite “winner” (e.g., examining early vs late clinical events), or true biological differences (e.g., in patients or protocols). Regarding the issue of confounding (e.g., healthy user bias, as described previously), methods are available 19 to measure and adjust for such variables.

A MORE BALANCED VIEW OF OBSERVATIONAL AND EXPERIMENTAL EVIDENCE

Given that randomized, controlled trials have not and often cannot be done for many clinical interventions, much of the clinical care provided in neurology (and all other specialties in medicine) would necessarily be considered unsubstantiated, if observational studies are discounted from consideration. The available evidence suggests, however, that observational studies can be conducted with sufficient rigor to replicate the results of randomized, controlled trials. The key issue is designing appropriate observational studies, usually with suitable (observational) cohort or case-control architecture; a methodological task for investigators to complete and reviewers to evaluate.

Despite the consistency of our results 9 (involving five clinical topics and 99 separate studies), as well as confirmatory evidence available in the literature, 16 – 18 we believe that the role of observational studies may vary in different situations. For example, different exposures (e.g., surgical operations and other invasive therapies) may be more prone to selection bias in observational investigations than the drugs and noninvasive tests examined in our report, 9 and “softer” outcomes (e.g., functional status) may be assessed more readily in randomized, controlled trials. In addition, we emphasized the potential risk associated with poorly done observational studies; for example, to promote ineffective “alternative” therapies. 46

Finally, a point of emphasis involves the general belief that randomization is necessary to balance known and (especially) unknown potential factors that can cause biased estimates of treatment effects through confounding. Given that unknown factors, by definition, would not be recognized by clinicians, a bias in assigning treatment would not occur according to those factors. Although such factors could be associated with outcome, they would not be associated with exposure, and therefore would not be confounding variables and would not affect the validity of results.

Randomized, controlled trials will (and should) remain a prominent tool in clinical research, but the results of a single randomized, controlled trial, or only one observational study, should be interpreted cautiously. If a randomized, controlled trial is later determined to be “wrong” in its conclusions, evidence from both other trials and well designed cohort or case-control studies can and should be used to establish the “right” answers.

The issues raised in this paper are not intended to diminish the important role that randomized, controlled trials play in clinical medicine (e.g., for evaluating interventions or for satisfying regulatory criteria). Yet, the popular belief that randomized, controlled trials inherently produce gold standard results, and that all observational studies are inferior, does a disservice to patient care, clinical investigation, and education of health care professionals. We should recognize the potential problem we face, that “the justification for why studies are included or excluded from the evidence base can rest on competing claims of methodologic authority that look little different from the traditional claims of medical authority that proponents of evidence-based medicine have criticized…interpretive decisions by old pre-evidence-based medicine experts may be replaced by interpretive decisions from a new group of experts with evidence-based medicine credentials…” 47 A more balanced and scientifically justified approach is to evaluate the strengths and limitations of well done experimental and observational studies, recognizing the attributes of each type of design.

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observational study vs natural experiment

I'm reading about observational studies and natural experiments.

It's unclear whether there is a conceptual difference between the two terms.

What is the difference between an observational study and a natural experiment?

• observational-study

• $\begingroup$ do you want examples? or do you have examples in mind? eg "I heard about study X and it was called experimental, but then study Y was called observational. why are they not called the same thing?" - it's better if you provide "X" and "Y" because you can get an answer in a domain you understand $\endgroup$ –  probabilityislogic Apr 27, 2019 at 9:46

The difference is that a natural experiment is an observational study that, in which out so happens that there is something that is effectively a randomization.

A nice clean example is studying the effects of suddenly having lots of money by comparing lottery winners against a representative random sample of people that bought tickets for the same drawing. Effectively, someone else did the randomization for you and it very clearly had nothing to do with some characteristic of the people whether they won or not.

It gets trickier when you take human decisions (e.g. political decisions for introducing or adjusting a minimum wage), because the decisions could very well have been not independent of other factors that might affect outcomes.

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• Research article
• Open access
• Published: 06 December 2021

Comparative effectiveness and safety of pharmaceuticals assessed in observational studies compared with randomized controlled trials

• Yoon Duk Hong 1 ,
• Jeroen P. Jansen 2 , 3 ,
• John Guerino 4 ,
• Marc L. Berger 5 ,
• William Crown 6 ,
• Wim G. Goettsch 7 , 8 ,
• C. Daniel Mullins 1 ,
• Richard J. Willke 9 &
• Lucinda S. Orsini 10  

BMC Medicine volume  19 , Article number:  307 ( 2021 ) Cite this article

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7 Citations

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Metrics details

There have been ongoing efforts to understand when and how data from observational studies can be applied to clinical and regulatory decision making. The objective of this review was to assess the comparability of relative treatment effects of pharmaceuticals from observational studies and randomized controlled trials (RCTs).

We searched PubMed and Embase for systematic literature reviews published between January 1, 1990, and January 31, 2020, that reported relative treatment effects of pharmaceuticals from both observational studies and RCTs. We extracted pooled relative effect estimates from observational studies and RCTs for each outcome, intervention-comparator, or indication assessed in the reviews. We calculated the ratio of the relative effect estimate from observational studies over that from RCTs, along with the corresponding 95% confidence interval (CI) for each pair of pooled RCT and observational study estimates, and we evaluated the consistency in relative treatment effects.

Thirty systematic reviews across 7 therapeutic areas were identified from the literature. We analyzed 74 pairs of pooled relative effect estimates from RCTs and observational studies from 29 reviews. There was no statistically significant difference (based on the 95% CI) in relative effect estimates between RCTs and observational studies in 79.7% of pairs. There was an extreme difference (ratio < 0.7 or > 1.43) in 43.2% of pairs, and, in 17.6% of pairs, there was a significant difference and the estimates pointed in opposite directions.

Conclusions

Overall, our review shows that while there is no significant difference in the relative risk ratios between the majority of RCTs and observational studies compared, there is significant variation in about 20% of comparisons. The source of this variation should be the subject of further inquiry to elucidate how much of the variation is due to differences in patient populations versus biased estimates arising from issues with study design or analytical/statistical methods.

Peer Review reports

Health care decision makers, particularly regulators but also health technology assessment agencies, have depended upon evidence from randomized clinical trials (RCTs) to assess drug effectiveness and to make comparisons among treatment options. Widespread adoption of the RCT was the hallmark of progress in clinical research in the twentieth century and accelerated the development and approval of new therapeutics; confidence in RCTs derived from their experimental nature, designs to minimize bias, rigorous data quality, and analytic approaches that supported causal inference.

In the last 30 years, we have witnessed an explosion of observational real-world data (RWD) and evidence (RWE) derived from RWD that has supplemented our understanding of the benefits and risks of treatments in broader populations of patients. RWE has been largely leveraged by regulators to assess the safety of marketed products and for new drug approvals when RCTs are infeasible, such as in rare diseases, oncology, or for long-term adverse effects. RCTs often do not have sufficient sample size to detect rare adverse events or long enough follow-up to detect long-term adverse effects. In such cases, regulatory decisions are often supplemented by RWE. However, leveraging of RWE has been much more slowly embraced in comparison to the adoption of RCTs for a variety of reasons. Imputation of causality is less certain in the absence of randomization and RWD can be much sparser and often requires extensive curation before it can be analyzed. Thus, skepticism about the robustness of observational RWD studies has made decision makers cautious in relying solely upon it to render judgments about the availability and appropriate use of new therapeutics, particularly by regulatory bodies.

Moreover, observational studies examining the effectiveness of treatments in similar populations have not always provided results consistent with RCTs. Despite many studies finding similar treatment effect estimates from RCTs and RWD analyses [ 1 , 2 , 3 ], other analyses have documented wide variation in results from RWD analyses within the same therapeutic areas [ 4 ], including analyses using propensity score-based methods [ 5 ]. Nonetheless, public interest has grown in the routine leveraging of RWD to promote the creation of a learning healthcare system, and regulatory bodies and other decision makers are exploring ways to expand their use of RWE. This is partly due to increasing acknowledgement of the value of RWE, such as its ability to better reflect actual environments in which the interventions are used.

One promising approach to understanding the sources of variability between RCT and observational study results is to compare estimates obtained from RWD analyses that attempt to emulate the eligibility criteria, endpoints, and other features of trials as closely as possible. A small number of RWD analyses have generated findings similar to previous RCTs [ 6 , 7 ], and the findings of other RWD analyses have been consistent with subsequent RCTs [ 8 ]. In a small number of cases, RCTs and RWD studies have been published simultaneously [ 9 ]. This has the advantage of not knowing the RCT estimate when conducting the RWD study. There have been disagreements between observational RWD analyses and RCTs that were based upon avoidable errors in the RWD analysis design [ 7 , 10 ]. This has led to a focus on the importance of research design in observational RWD analyses attempting to draw causal inferences regarding treatment effects [ 11 , 12 , 13 ].Emulation studies can improve understanding of when observational studies may reliably generate results consistent with RCTs; however, not all RCTs can be feasibly emulated using RWD due to limitations in observational datasets. Existing sources of observational data, such as health insurance claims and electronic health records (EHRs), may not routinely capture the intervention, indication, inclusion and exclusion criteria, and/or endpoints used in RCTs [ 14 ].

The objective of this paper is to provide further evidence on the comparability of RCTs and observational studies when the latter use a range of study designs and were not designed to emulate RCTs. We aim to quantify the extent of the difference in treatment effect estimates between RCTs and observational studies. We go beyond previous comparisons of RCTs and observational studies, with a focus purely on pharmaceuticals, and provide a systematic landscape review of the (in)consistency between RCT and observational study treatment effect estimates. The reasons for the variation in relative treatment effects are not assessed in this review but should be the subject of further study.

Eligibility criteria

Inclusion criteria.

Study design:

Published systematic literature reviews designed to compare relative treatment effects from observational studies with the corresponding effects from RCTs; or

Published systematic literature reviews that reported subgroup analyses stratified by RCT and observational study design; and

Observational studies included in these reviews have to be retrospective or prospective cohort studies, or case-control studies

Population: Human subjects

Intervention(s) and comparator(s): Any active or placebo-controlled pharmaceutical or biopharmaceutical intervention

Outcome(s):

Efficacy/effectiveness or safety outcomes

Pooled relative treatment effect estimates for both observational studies and RCTs

Exclusion criteria

Systematic reviews that compared absolute outcomes, such as event rates, between non-comparative observational studies and RCTs

Non-pharmaceutical-based studies, e.g., surgical procedures, traditional medicine, vitamin/herbal supplements, etc.

Non-English language

Abstracts or conference proceedings

Search strategy

We searched PubMed and Embase to identify relevant systematic literature reviews published between January 1, 1990, and January 31, 2020. Anglemeyer et al.’s search strategy [ 1 ] was used as a template to develop the search strategy, which included a wide range of MeSH terms and relevant keywords. We updated Anglemeyer et al.’s systematic review hedge and used the more recent CADTH systematic review/meta-analysis hedge, created in 2016, in both PubMed and Embase [ 15 ]. We restricted our search to focus on pharmaceuticals only. PubMed and Embase were searched for the following concepts: pharmaceuticals, study methodology, and comparisons (filters: Humans and English language). The PubMed search strategy which was adapted for use in Embase can be found in Additional File 1 .

Study selection

After removing duplicate references, three authors (JG, YH and LO) screened the titles and abstracts to identify relevant reviews. Once complete, LO verified the screening for accuracy. Following the title and abstract screen, full text articles were obtained for all potentially relevant reviews. Full text articles were then assessed to determine if they meet the selection criteria for final inclusion in the review.

Data extraction

A pilot extraction was first done by two authors (JG and YH) on a sample of three articles using a standardized extraction table. This was done to test the standardized extraction table and to ensure consistency between the authors performing the data extraction. JG and YH then independently extracted information from each review using the standardized extraction table. A third author (LO) verified the extraction for accuracy and identified any discrepancies. These discrepancies were discussed until resolved.

We focused on primary outcomes reported in the reviews and extracted information summarizing the scope of each of the identified systematic reviews. Extracted information included the following: review objective, population, disease/therapeutic area, interventions, outcome(s), number of included RCTs and observational studies, pooled relative treatment effect estimates for RCTs and observational studies along with the 95% confidence intervals (95% CI), and measures of heterogeneity.

Based on the extracted information, we calculated the ratio of the relative treatment effect estimate from observational studies over the relative treatment effect estimate from RCTs (e.g., RR obs /RR rct ), along with the corresponding 95% CI obtained via a Monte Carlo simulation for each pair of pooled RCT and observational study estimates. Outcomes for which the relative treatment effect was not expressed with a relative risk (RR), odds ratio (OR), or hazard ratio (HR) were excluded from our analysis.

We expressed differences in pooled effect estimates with the following measures: ratios that were < 1, > 1, or = 1, ratios indicating an “extreme difference” (< 0.70 or > 1.43) [ 16 ] and absence of an extreme difference. We evaluated (in)consistency between pooled RCT and observational study estimates with the following measures: presence of opposite direction of effect, RCT effect estimate outside the 95% CI of the observational study estimate, and vice versa, statistically significant difference between RCT and observational study estimates, and statistically significant difference along with opposite direction of effect. Statistically significant difference was determined by examining the 95% CI of the ratio of the relative treatment effect estimates from observational studies and RCTs derived from the Monte Carlo simulation. We examined differences in relative effect measures from observational studies and RCTs by outcome type and therapeutic area.

To test the robustness of our findings, we conducted two sensitivity analyses. As some reviews assessed more than one endpoint and contributed more than one pair of pooled relative treatment effects from RCTs and observational studies to our analysis, we repeated the analysis with one endpoint per review, i.e., a single pair of pooled relative treatment effects from RCTs and observational studies from each review, selecting the most frequently used endpoints for inclusion whenever possible. Additionally, as some studies were included in more than one review, we repeated the analysis ensuring that there is no overlap of data between the included reviews, i.e., ensuring that each study was included in only one review included in our analysis. Details on the sensitivity analyses are included in Additional File 2 . All analyses were conducted using RStudio, version 1.3.1073 (©2009-2020 RStudio, PBC).

Literature search

Our search on PubMed and Embase yielded 3798 unique citations after removing duplicates. After screening titles and abstracts, we identified 93 full text articles for further review. Of these, 30 reviews met our inclusion criteria (Fig. 1 ).

Diagram depicting literature screening process 

Included systematic reviews

The characteristics of the included reviews and the pairs of pooled relative treatment effects from RCTs and observational studies reported in the reviews are summarized in Table 1 . Thirty systematic reviews across 7 therapeutic areas (cardiovascular disease [15/30], infectious disease [6/30], oncology [3/30], mental health [2/30], immune-inflammatory [1/30], metabolic disease [1/30], and other [2/30]) were identified from the literature. These reviews included 519 RCTs and observational studies and provided 79 pairs of pooled relative treatment effects from RCTs and observational studies across multiple interventions, comparators, and outcomes. Five pairs were excluded from our assessment because they concerned continuous outcomes ( n = 1) or no pooled effect estimate was reported for observational studies ( n = 4). As a result, 74 pairs of pooled relative treatment effects from RCTs and observational studies from 29 reviews were available for assessment of consistency.

Ratio of relative effect measures from observational studies and RCTs

Figure 2 presents the scatterplot of relative effect measures from observational studies and RCTs across the 74 pairs of pooled relative treatment effects with the 95% CI bars. The ratio of the relative effect measure from observational studies over the corresponding relative effect measure from RCTs ranged from 0.09 to 6.50 (median = 0.92, interquartile range = 0.69–1.27). The ratio was greater than 1, i.e., the relative effect was larger in observational studies in 31 of the 74 pairs (41.9%). The ratio was less than 1, i.e., the relative effect was larger in RCTs in 42 of the 74 pairs (56.8%), and the ratio was equal to 1 in one of the 74 pairs (1.4%). The ratio was greater than 1.43 in 12 of the 74 pairs (16.2%) and less than 0.7 in 20 of the 74 pairs (27.0%) indicating an extreme difference. There was an absence of an extreme difference (0.7 ≤ ratio ≤ 1.43) in 42 of the 74 pairs (56.8%; Table 2 ). Sensitivity analyses including only one endpoint from each review and ensuring no overlap of data between the included reviews resulted in similar findings (Table 2 ). Scatterplots of relative effect measures from observational studies and RCTs by outcome type and therapeutic area can be found in Additional File 3 : Figures S1 and S2.

Relative effect measures (RR, OR, HR) from observational studies (y-axis) versus corresponding relative effect measures from randomized controlled trials (x-axis) across 74 pairs of pooled relative treatment effects 

Consistency of relative effect measures from observational studies and RCTs

In 30 of the 74 pairs (40.5%), effect estimates from observational studies and RCTs pointed in opposite directions of effect. The RCT point estimate was outside the 95% CI of the observational study in 35 of the 74 pairs (47.3%) and the observational study point estimate was outside the 95% CI of the RCT in 27 of the 74 pairs (36.5%). There was a statistically significant difference between relative effect estimates from observational studies and RCTs in 15 of the 74 pairs (20.3%). In 13 of the 74 pairs (17.6%), there was a statistically significant difference and the effect estimates of observational studies and RCTs pointed in opposite directions (Table 3 ). The results remained fairly consistent when the sensitivity analyses were conducted (Table 3 ).

Our analysis of 29 reviews comparing results of RCTs and observational studies of pharmaceuticals showed, on average, no significant differences in their relative risk ratios across all studies, but also considerable study-by-study variability. The median ratio of the relative effect measure from observational studies to RCTs was 0.92, indicating just slightly lower effectiveness/safety estimates in observational studies than corresponding RCTs. This is in fact somewhat higher than the 0.80 ratio recently found in meta-research comparing effect estimates of randomized clinical trials that use routinely collected data (i.e., from traditional observational study sources such as registries, electronic health records, or administrative claims) for outcome ascertainment with traditional trials not using routinely collected data [ 47 ]. However, whether judging by the frequency of “extreme” differences (43.2%) or statistically significant differences in opposite directions (17.6%), one could not claim that observational study results consistently replicated RCT results on a study-by-study basis in our sample.

There are a number of reasons that any given observational study result may not replicate an RCT comparing the same treatments. First, it may not have been the intent of the observational study researchers to match a specific clinical trial—they may have intentionally studied a different treatment population, setting, or protocol in order to complement or test the RCT findings. In such cases, there would be variation in effect estimates due to estimating a different causal effect. Even if the researcher does attempt to match a specific RCT, the data may not have been available to closely match it, since patient histories, test results, etc., used for RCT inclusion criteria may not be observed, or outcomes may not be captured the same way. Even given similar data, non-randomized studies have the potential for selection/channeling bias into treatment determined by factors unobservable in either type of study, and analytic attempts to correct for such confounding may have limited success. In some cases, treatment conditions may differ enough between the RCT and real-world practice that replication of results should not be expected, e.g., due to careful safety monitoring that affects subsequent treatment in RCTs. Finally, it is possible that other pharmacoepidemiologic principles, beyond the study design considerations we already mentioned, were violated in the individual RWD studies, which could have caused disagreement between their results and the RCTs. While variation in treatment effect estimates due to estimating a different causal effect in a different study population is expected and valid, biased estimates arising from issues with study design or analytical methods may be problematic.

Details in these reviews were typically insufficient to distinguish among these possible explanations, without detailed review of the individual studies, which we did not attempt here. However, some reviews did attempt to explain the differences they found. For example, in the review by Gandhi et al. (2015) [ 24 ], which compared dual-antiplatelet therapy (DAPT) to mono-antiplatelet therapy (MAPT) following transcatheter aortic valve implantation, there was a statistically significant difference in pooled relative treatment effect estimates from observational studies and RCTs. The primary outcome was more likely to occur in the DAPT group than in the MAPT group in the observational studies (OR 3.02; 95% CI 1.91–4.76); however, no statistically significant difference was found between DAPT and MAPT in the RCTs (OR 0.98; 95% CI 0.46–2.11). The authors explained that the RCTs ( n = 2) and observational studies ( n = 2) included in this review had variable patient inclusion/exclusion criteria and there were differences in the type of prosthetic aortic valve used, which may have introduced selection bias [ 24 ].

To allow for better use of individual observational studies to inform decision-making, their ability to replicate RCT results needs to become more reliable, and the “target trial” approach seems to be a path forward. Several systematic efforts using sophisticated observational data research designs to emulate multiple RCTs are underway [ 48 , 49 ]. These efforts are intended to provide regulatory bodies and other decision makers with empirical evidence to support the development of a framework for assessing when and under what circumstances observational RWE can be used to support a wider range of regulatory decisions. RCT DUPLICATE is a collaboration between the Food and Drug Administration (FDA), Brigham and Women’s Hospital and Harvard Medical School Division of Pharmacoepidemiology, to replicate 30 completed Phase III or IV trials and to predict the results of seven ongoing Phase IV trials using Medicare and commercial claims data [ 50 ]. The RCT DUPLICATE team has recently reported results for its first 10 trials [ 51 ]. They report hazard ratio estimates within the 95% CI of the corresponding trial for 8 of 10 emulations.

The Multi-Regional Clinical Trials Center and OptumLabs are leading another effort called Observational Patient Evidence for Regulatory Approval and Understanding Disease (OPERAND) which extends the trial emulation activity and relaxes the inclusion/exclusion criteria of the trials to examine treatment effects in the broader patient population treated in routine care [ 52 ]. The FDA has also funded the Yale University-Mayo Clinic Center of Excellence in Regulatory Science and Innovation to predict the results of three to four ongoing safety trials using OptumLabs claims data [ 53 ].

It is important to understand that clinical trials emulation efforts are being conducted solely to improve understanding of when observational studies may be expected to produce robust results. Bartlett and colleagues [ 14 ] found that in a review of 220 clinical trials published in high impact medical journals in 2017, 15% could potentially be emulated using data available from medical claims or EHRs. For example, the inclusion/exclusion criteria for many oncology trials require data on genetic markers and progression free survival unavailable in EHRs. The estimate by Bartlett and colleagues may prove to be an underestimate as the ability to link different types of observational data continues to improve. Nevertheless, it is reasonable to assume that it is not possible to emulate most trials with existing observational datasets.

These efforts are critical to advance our understanding of the strengths and limitations of observational RWE, identifying issues with study design, endpoint definition, data quality, and analytical methodology that may impact the consistency of findings between RWE and RCTs. While much attention has focused on differences in study populations between observational studies and RCTs as the reason for the inconsistency in effect estimates, emerging evidence suggests that issues with study design (e.g., establishing time zero of exposure) may be equally if not more important [ 7 ]. Therefore, the results of these efforts will not provide definitive guidance to decision makers but they emphasize how even subtle differences in study design and endpoint definition can impact absolute estimates of treatment effect. Moreover, RWE studies are answering a different question than RCTs, i.e., “Does it work?” verses “Can it Work?” The former is important to a variety of stakeholders beyond regulators. Hence, they should not be expected to provide results identical to RCTs.

In conclusion, although our review shows no average significant difference in the relative risk ratios between published RCTs and observational studies, there is substantial study-to-study variation. It was impractical to review all individual observational study designs and examine their potential biases, but future work should elucidate how much of the variation is due to differences in study populations versus biased estimates arising from issues with study design or analytical methods. As more target trial replication attempts are conducted and published, more systematic evidence will emerge on the reliability of this approach and on the potential for observational studies to more routinely inform healthcare decisions.

Availability of data and materials

The data analyzed in this study are included in this published article.

Abbreviations

Confidence interval

Dual-antiplatelet therapy

Electronic health record

Food and Drug Administration

Hazard ratio

Mono-antiplatelet therapy

Observational Patient Evidence for Regulatory Approval and Understanding Disease

Randomized controlled trial

Real-world data

• Real-world evidence

Relative risk

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YH contributed to the extraction, analysis, and interpretation of the data and contributed to the drafting and revising the manuscript. JJ contributed to the design of the study, was a major contributor to the analysis and interpretation of the data, and contributed to the revision of the manuscript. JG contributed to the coordination of the study, extraction of the data, and drafting of the manuscript. MB, WC, and RW contributed to the design of the study, interpretation of the results, drafting of the manuscript, and revision of the manuscript for important intellectual content. WG and CDM contributed to the design of the study, interpretation of the results, and revision of the manuscript for important intellectual content. LO coordinated the study and contributed to the data extraction, interpretation of the results, drafting the manuscript, and revising the manuscript for important intellectual content. All authors read and approved the final manuscript.

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Yoon Duk Hong, John Guerino, Marc L. Berger, William Crown, Richard J. Willke, Wim G. Goettsch, and Lucinda S. Orsini have no conflicts of interest to report. Jeroen P. Jansen is a part-time employee of Precision Medicine Group (PMG) (PRECISIONheor) and has stock options from Precision Medicine Group. PMG provides contracted research services to pharmaceutical and biotech industry. C. Daniel Mullins has received consulting fees from AstraZeneca, Bayer, Incyte, Merck, Pfizer, and Takeda and has received support from Bayer and Pfizer for attending meetings and/or travel.

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Figures S1 & S2. Figure S1. Relative effect measures from observational studies versus corresponding relative effect measures from randomized controlled trials by outcome type. Figure S2. Relative effect measures from observational studies versus corresponding relative effect measures from randomized controlled trials by therapeutic area.

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Hong, Y.D., Jansen, J.P., Guerino, J. et al. Comparative effectiveness and safety of pharmaceuticals assessed in observational studies compared with randomized controlled trials. BMC Med 19 , 307 (2021). https://doi.org/10.1186/s12916-021-02176-1

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Table of Contents

• How We Sleep
• Sleep Disorders and Disruptions
• Sleep in the Family

Mental Health and Sleep

Bedtime rituals and sleep.

• Sleep Aids and Sleep

Your Diet and Sleep

• Your Environment and Sleep

How did you sleep last night?

No matter how you answer this question, you may rest easier knowing that you are not alone. 

Sleep issues can affect people of all ages and impact many parts of our lives.

While the science and nature of sleep is too complex to sum up on a single page, taking a peek at sleep statistics nationwide can help you understand key aspects of sleep health, as well as how widespread sleep issues are.

Our Sleep Cycles

Understanding the science of sleep  , in a normal sleep period, a person experiences four to six sleep cycles trusted source national center for biotechnology information the national center for biotechnology information advances science and health by providing access to biomedical and genomic information. view source . .

• While cycles vary in length, each sleep cycle can last about 90 minutes . 
• Rapid eye movement (REM) sleep makes up 20% to 25% of total sleep in healthy adults.
• As you cycle through non-rapid eye movement (NREM) sleep, various bodily functions slow down or stop altogether. Metabolism drops by around 15% Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source and both heart rate and blood pressure go down. 
• Stage 3 NREM sleep or “deep sleep” is believed to be the most critical stage of sleep   for regenerating your body and brain. Deep sleep decreases across the lifespan, with one receiving less deep sleep as they age. 
• On average, we spend about two hours per night dreaming Trusted Source National Institute of Neurological Disorders and Stroke (NINDS) NINDS aims to seek fundamental knowledge about the brain and nervous system and to use that knowledge to reduce the burden of neurological disease. View Source , mostly during REM sleep.
• On average, adults sleep on their side 54% of the time Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source , on their back 38% of the time, and on their stomach 7% .

How Much We Sleep 

Are we really getting enough sleep , adults need seven or more hours trusted source national library of medicine, biotech information the national center for biotechnology information advances science and health by providing access to biomedical and genomic information. view source of sleep per night. .

• More than one-third Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source of adults sleep less than seven hours per night, on average.
• Among all states, Hawaii has the highest percentage of adults (43%) who get seven or fewer hours of sleep each night. | Learn more
• Among U.S. counties, Boulder County in Colorado has the lowest percentage of adults (23%) Trusted Source Centers for Disease Control and Prevention (CDC) As the nation’s health protection agency, CDC saves lives and protects people from health threats. View Source who get seven or fewer hours of sleep each night.

7% of adults nap every day. 

• 81% of adults have taken a nap of 10 minutes or longer in the past three months. | Learn more
• The average nap is about one hour .

Work and Sleep 

The link between sleep quality and work performance, insufficient sleep has an estimated economic impact of more than $411 billion trusted source national library of medicine, biotech information the national center for biotechnology information advances science and health by providing access to biomedical and genomic information. view source each year in the united states alone..

• 4.8 of 10 workers say they are regularly tired during the day, and 7 of 10 say they are tired when their work day is done. | Learn more
• Active-duty service m embers are 34% more likely Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source to report insufficient sleep than people with no history of military service.
• 55% of nurses say they experience insomnia. | Learn more 
• Drowsy driving is responsible for more than 6,000 fatal car crashes Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source every year in the United States.
• One study estimates the annual cost of workplace errors and accidents linked to insomnia at $31.1 billion Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source .

Snoring and Sleep

How often do we snore  , about 57% of men, 40% of women, and 27% of children trusted source merck manual first published in 1899 as a small reference book for physicians and pharmacists, the manual grew in size and scope to become one of the most widely used comprehensive medical resources for professionals and consumers. view source snore in the u.s..

• Up to 70%  of snorers have been diagnosed with sleep apnea Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source . 
• Obstructive sleep apnea (OSA) affects around one billion adults worldwide, with 80-90% of cases Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source going undiagnosed.
• A 10% increase Trusted Source JAMA Network JAMA and the Specialty Journals help readers stay up to date with the latest research, author interviews, apps, and learning courses. View Source in body weight may make you six times more likely to have OSA.
• Some 1% of adults ages 40 and older Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source experience central sleep apnea (CSA) .

Insomnia and Sleep

Why we struggle to fall asleep and stay asleep , up to two-thirds of adults occasionally experience insomnia symptoms .

• 10-15% of people Trusted Source UpToDate More than 2 million healthcare providers around the world choose UpToDate to help make appropriate care decisions and drive better health outcomes. UpToDate delivers evidence-based clinical decision support that is clear, actionable, and rich with real-world insights. View Source experience chronic insomnia , a type of insomnia that persists over multiple months 
• Women are 40% more likely Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source to have insomnia than men are. 
• According to a 2020 study Trusted Source Oxford Academic Journals (OUP) OUP publishes the highest quality journals and delivers this research to the widest possible audience. View Source , 58% of a sample of post-9/11 veterans screened positive for insomnia. 
• Up to 75% of older adults experience symptoms of insomnia. According to a 2019 study, incidences of multiple physical and psychiatric disorders are higher in older adults with insomnia Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source than those without. 

Other Sleep Disorders

Common sleep disorders keeping us up  , according to estimates, 50 million to 70 million people trusted source national heart, lung, and blood institute (nhlbi) the nhlbi is the nation's leader in the prevention and treatment of heart, lung, blood and sleep disorders. view source have ongoing sleep disorders . the most common among them are insomnia, sleep apnea, and narcolepsy..

• Sleep disorders Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source affect 39% to 47% of perimenopausal women and 35% to 60% percent of postmenopausal women.
• 76% of adults with a sleep issue or disorder share a household with at least one other person who does. 42% of people agree that sleep issues run in their family. | Learn more
• You may exert as much as 250 pounds of force when you grind your teeth. | Learn more

• Restless legs syndrome (RLS) Trusted Source Medline Plus MedlinePlus is an online health information resource for patients and their families and friends. View Source affects 5% to 10% of adults and 2% to 4% of children. | Learn more
• 1 in every 2,000 adults Trusted Source Medline Plus MedlinePlus is an online health information resource for patients and their families and friends. View Source has narcolepsy. In the U.S., that equates to about 165,950 people.
• People with irritable bowel syndrome (IBS) are 37.6% more likely Trusted Source Saudi Journal of Gastroenterology View Source than others to have a sleep disorder.
• 66% of adults say they have talked in their sleep Trusted Source Sleep Medicine Research Sleep Medicine Research (Sleep Med Res) is an official journal of the Korean Society of Sleep Medicine (KSSM), Asian Society of Sleep Medicine (ASSM), Korean Society of Sleep Research, and Korean Society of Sleep and Breathing. View Source .
• 23% of adults say they have had a sleepwalking episode Trusted Source Centers for Disease Control and Prevention (CDC) As the nation’s health protection agency, CDC saves lives and protects people from health threats. View Source .
• 8% of people have had an episode of sleep paralysis Trusted Source Sleep Medicine Research Sleep Medicine Research (Sleep Med Res) is an official journal of the Korean Society of Sleep Medicine (KSSM), Asian Society of Sleep Medicine (ASSM), Korean Society of Sleep Research, and Korean Society of Sleep and Breathing. View Source .

Common Sleep Disruptions 

How interrupted sleep can affect you  , nighttime disruptions may cause sleep fragmentation and reduce time spent in the deep sleep. .

• Noise may disrupt sleep and increase production of the hormones adrenaline and cortisol, as well as increase heart rate and blood pressure Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source .
• 69% of men ages 40 and older and 76% of women in that age group get up to go to the bathroom at least once per night Trusted Source National Center for Biotechnology Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source .
• 41% of primary care patients Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source say that they experienced night sweats at least once a month.
• 95% of adults lose at least an hour of sleep to pain in a given week. | Learn more  
• 63% of adults Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source with heartburn say it has affected their ability to sleep well.
• Premenstrual syndrome (PMS) makes women at least two times as likely Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source to report insomnia-like symptoms before and during their period.

Pregnancy and Sleep

The relationship between pregnancy and sleep  , around 50% of people who are pregnant trusted source national library of medicine, biotech information the national center for biotechnology information advances science and health by providing access to biomedical and genomic information. view source experience insomnia-like symptoms..

• Sleeping on the left side with the legs slightly curled is considered the best sleeping position for pregnancy. 
• As many as 50% of pregnant people Trusted Source UpToDate More than 2 million healthcare providers around the world choose UpToDate to help make appropriate care decisions and drive better health outcomes. UpToDate delivers evidence-based clinical decision support that is clear, actionable, and rich with real-world insights. View Source snore, with snoring typically getting worse during the third trimester. 
• Obstructive sleep apnea affects as many as 1 in 5 people Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source during pregnancy.
• Restless legs syndrome affects up to one third of people Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source during their third trimester. 
• Having a sleep disorder during pregnancy may increase the odds of a premature birth by 40%. | Learn more

Children and Sleep 

The importance of sleep during adolescence , babies spend more than half trusted source national library of medicine, biotech information the national center for biotechnology information advances science and health by providing access to biomedical and genomic information. view source of their time sleeping..

• Babies up to 1 year old need 12 to 16 hours of sleep each day, including naps.
• Babies born prematurely may spend around 90% of their day asleep.
• Sudden infant death syndrome (SIDS) is a leading cause of death for babies younger than 1 year old.
• More than twice as many Trusted Source Centers for Disease Control and Prevention (CDC) As the nation’s health protection agency, CDC saves lives and protects people from health threats. View Source of SIDS deaths occur among non-Hispanic Black, American Indian, and Alaska Native babies per 100,000 live births than non-Hispanic white babies.  | Learn more  

20% to 30% of children have trouble falling asleep or staying asleep Trusted Source UpToDate More than 2 million healthcare providers around the world choose UpToDate to help make appropriate care decisions and drive better health outcomes. UpToDate delivers evidence-based clinical decision support that is clear, actionable, and rich with real-world insights. View Source . 

Ages 1 to 2 need 11 to 14 hours of sleep. Ages 3 to 5 need 10 to 13 hours of sleep. Ages 6 to 12 need 9 to 12 hours of sleep.

• According to estimates, 10% to 50% of children ages 3 to 6 have occasional nightmares Trusted Source American Academy of Sleep Medicine (AASM) AASM sets standards and promotes excellence in sleep medicine health care, education, and research. View Source .
• Kids share a bedroom in 70% of households with two or more children. | Learn more  
• 30.8% of parents and guardians say their school-age children are not getting enough sleep. | Learn more  
• As many as 70% of children with ADHD have mild to severe sleeping problems Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source .
• Children who lose 39 minutes of sleep or more Trusted Source JAMA Network JAMA and the Specialty Journals help readers stay up to date with the latest research, author interviews, apps, and learning courses. View Source have a harder time coping at school and typically feel worse than those getting enough sleep.

For teens between the ages of 13 to 19, average total sleep per night drops by 40 to 50 minutes Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source .

• 58% of middle schoolers and 72% of high school students get less than the recommended amount Trusted Source Centers for Disease Control and Prevention (CDC) As the nation’s health protection agency, CDC saves lives and protects people from health threats. View Source of sleep for their age.
• 78% of adults say getting enough sleep is more important than being successful at a video game, compared to 60% of adolescents. | Learn more  
• Adolescents push their bedtime back by 16 minutes Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source for every 30 minutes they spend playing video games.

Parents and Sleep 

Coping with sleep loss as a new parent, sleep satisfaction and duration significantly decrease immediately after the birth of a child , with the effects lasting up to 6 years. trusted source oxford academic journals (oup) oup publishes the highest quality journals and delivers this research to the widest possible audience. view source  .

• New mothers are shown to lose 62 minutes of sleep on average, compared to 13 minutes for new fathers. 
• 43% of single parents Trusted Source Centers for Disease Control and Prevention (CDC) As the nation’s health protection agency, CDC saves lives and protects people from health threats. View Source sleep less than seven hours per night, compared to 33% of adults in two-parent homes and 31% of adults with no children.
• 36% of parents Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source bed share with their children between the ages of 2-5.

The connection between our emotional health and sleep

40% of people with insomnia trusted source jama jama, published continuously since 1883, is an international peer-reviewed general medical journal. view source may have a diagnosable mental health condition ..

• 70% of adults with seasonal affective disorder (SAD) feel tired in the winter, compared to 44% of those without it | Learn more
• Individuals with SAD who practice light therapy during the winter are 36% less likely Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source to experience a depressive episode.
• 83% of adults Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source with depression may have at least one symptom of insomnia.
• 54% of adults say stress and anxiety were the top reasons they have trouble falling asleep. Sunday was the night of the week in which they had the most trouble falling asleep. | Learn more
• As much as 91% of adults Trusted Source CNS Drugs View Source with post-traumatic stress disorder (PTSD) have symptoms of insomnia.
• 80% of people with PTSD have nightmares within three months of experiencing trauma.
• Wildfires can cause as much as 135 hours of lost sleep per year for adults. 77% of adults who have lost sleep to wildfires cite anxiety as the reason. | Learn more

How bedtime rituals impact our sleep  

Watching tv is the top bedtime ritual among u.s. adults, with 53% of people saying they do it before bed. .

• 50% of people who watch TV before bed get less than seven hours of sleep. | Learn more
• 53% of adults sleep with their bedroom windows closed and 61% sleep with their door closed. | Learn more
• 58% of adults who shower or bathe before bed say that doing so helps them sleep | Learn more
• Adults in the U.S. spend 3 hours, 30 minutes on social media before bed every night. YouTube is the most popular social media platform used before bed, with 74% of SleepFoundation.org survey respondents using it. | Learn more

Melatonin and Sleep 

Understanding the sleep hormone  , 88% of adults who take melatonin say it helps them fall asleep faster..

• 56% of adults have consumed at least one sleep aid in the past month, 49% of adults have used melatonin , which is the most popular sleep aid. | Learn more
• The average melatonin dosage for adults is 4.8 milligrams. 71% of adults take 5 milligrams of melatonin or less. | Learn more
• On average, adults who take melatonin do it 211 days each year. 39% of adults take melatonin every day. 
• Melatonin use increased 425% between 1999 and 2018 Trusted Source JAMA Network JAMA and the Specialty Journals help readers stay up to date with the latest research, author interviews, apps, and learning courses. View Source among adults.
• 88% of melatonin products Trusted Source JAMA Network JAMA and the Specialty Journals help readers stay up to date with the latest research, author interviews, apps, and learning courses. View Source are inaccurately labeled. An analysis of melatonin supplements found that they may include 347% more melatonin per dose than what is on the label. 
• 46% of parents report giving melatonin to their children under 13 to help them fall asleep. | Learn More

Sleep Aids and Sleep 

The prevalence and influence of sleep aids, 8% of adults say they took medication to help them sleep at least four times trusted source centers for disease control and prevention (cdc) as the nation’s health protection agency, cdc saves lives and protects people from health threats. view source in the past week..

• 58% of respondents with seasonal affective disorder use sleep aids, compared to 26% of those without it, according to a SleepFoundation.org survey.
• 79% of adults who take prescription sleep medication experience a residual effect Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source such as oversleeping, feeling groggy, or having a hard time concentrating the next day.
• 20% of adults Trusted Source American Academy of Sleep Medicine (AASM) AASM sets standards and promotes excellence in sleep medicine health care, education, and research. View Source use marijuana or cannabidiol (CBD) as a sleep aid .
• 23% of adults have taken Benadryl, or diphenhydramine , as a sleep aid in the past month.
• Sleep trackers are projected to become a $11.2 billion business by 2028 .
• 28% of adults Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source use a cellphone to track their sleep. These include sleep-tracker apps.

Sex and Sleep 

The effects of sleep on sex, insomnia may be a risk factor for sexual dysfunction , with both insufficient and disrupted sleep linked to a higher risk of erectile dysfunction. .

• Getting an extra hour of sleep for women can increase the interest in partnered sexual activity up to 14% Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source . 
• 30% of couples who started sleeping in separate beds cited their sleep habits as the reason. | Learn more
• 8% of patients Trusted Source American Academy of Sleep Medicine (AASM) AASM sets standards and promotes excellence in sleep medicine health care, education, and research. View Source at a sleep disorders center reported experiencing symptoms of sexsomnia, also known as sleep sex. 
• More than 70% Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source of people report experiencing a sex dream at least once in their life. 

What you eat can impact your sleep  

Diet and nutrition affect the quality of your sleep and your food choices can make it easier or harder to get the sleep you need..

• Drinking more than two servings of alcohol per day for men and more than one serving per day for women can decrease sleep quality by 39% Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source .
• 51% of adults who sleep more than normal over Thanksgiving cite overeating and alcohol consumption as the reason. | Learn more
• On average, adults snack before bedtime 3.9 nights each week . Adults who snack on seeds and nuts before bed sleep 32 minutes more , on average, than those who snack on chips, crackers, or pretzels. | Learn more
• Eating within two hours before bed is linked to later bedtimes, trouble falling and staying asleep, and obesity.

Caffeine and Sleep

How coffee and caffeine can hurt our sleep  , most adults can safely consume up to 400 milligrams trusted source national library of medicine, biotech information the national center for biotechnology information advances science and health by providing access to biomedical and genomic information. view source of caffeine each day, which is roughly equivalent to four cups of coffee . .

• 94% of Americans drink caffeinated beverages, with 64% drinking them every day. | Learn More  
• 71% of SleepFoundation.org survey respondents drink coffee every day. 70% think caffeine has an impact on their sleep.
• Top sleep issues reported by caffeine-drinkers include daytime sleepiness, fatigue, and insomnia. 30% of survey respondents reported experiencing anxiety. 
• The half-life of caffeine, or the time required for just half of it to be eliminated from the body, is usually 4 to 6 hours , but can be anywhere from 2 to 12 hours.
• Coffee promotes alertness and reduces tiredness by blocking adenosine receptors and preventing its sleep-promoting effects. However, once the effects wear off, people can experience sleepiness and muscle fatigue Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source .

Daylight Saving Time and Sleep

Is dst affecting our sleep, the majority of americans support abolishing daylight saving time (dst) , but legislation to make dst permanent year-round has not been enacted. while it is believed major sleep disruptions are less likely to occur when dst ends, one study trusted source national library of medicine, biotech information the national center for biotechnology information advances science and health by providing access to biomedical and genomic information. view source found that in the week after the november time change, people experienced the following:.

• 115% increase in difficulty falling asleep
• 103% increase in excessive daytime sleepiness
• 64% increase in difficulty staying asleep
• 34% increase in sleep dissatisfaction

Your environment and sleep

Optimizing your bedroom for better sleep , researchers have established a link between using blue light emitting devices before bed and increases in the amount of time it takes someone to fall asleep..

• The median household has five electronic devices, and 18% of homes are hyper-connected Trusted Source Pew Research Center Pew Research Center is a nonpartisan fact tank that informs the public about the issues, attitudes, and trends shaping the world. View Source , containing 10 or more devices.
• 57% of teens Trusted Source National Library of Medicine, Biotech Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source who use technology in the bedroom, such as a television or smartphone, suffer from sleep problems. 

The best room temperature for sleep is approximately 65 degrees . 

• Your core body temperature generally hovers around 98.6 degrees, but fluctuates by about 2 degrees throughout the night.
• Infants more sensitive to changes in temperature may benefit from a bedroom that is warmer, up to 69 degrees . 
• A sleeping environment that is too warm can interfere with the body’s thermoregulation and affect the time spent in different sleep stages Trusted Source National Center for Biotechnology Information The National Center for Biotechnology Information advances science and health by providing access to biomedical and genomic information. View Source . A cold bedroom temperature is not considered to be as detrimental to sleep quality.

Got a hot tip? Pitch us your story idea, share your expertise with SleepFoundation.org, or let us know about your sleep experiences right here .

About Our Editorial Team

Eric Suni, Staff Writer

Medically Reviewed by

Kimberly Truong, Sleep Medicine Physician PhD

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Long COVID or Post-COVID Conditions

Some people who have been infected with the virus that causes COVID-19 can experience long-term effects from their infection, known as Long COVID or Post-COVID Conditions (PCC). Long COVID is broadly defined as signs, symptoms, and conditions that continue or develop after acute COVID-19 infection. This definition  of Long COVID was developed by the Department of Health and Human Services (HHS) in collaboration with CDC and other partners.

People call Long COVID by many names, including Post-COVID Conditions, long-haul COVID, post-acute COVID-19, long-term effects of COVID, and chronic COVID. The term post-acute sequelae of SARS CoV-2 infection (PASC) is also used to refer to a subset of Long COVID.

What You Need to Know

• Long COVID is a real illness and can result in chronic conditions that require comprehensive care. There are resources available .
• Long COVID can include a wide range of ongoing health problems; these conditions can last weeks, months, or years.
• Long COVID occurs more often in people who had severe COVID-19 illness, but anyone who has been infected with the virus that causes COVID-19 can experience it.
• People who are not vaccinated against COVID-19 and become infected may have a higher risk of developing Long COVID compared to people who have been vaccinated.
• People can be reinfected with SARS-CoV-2, the virus that causes COVID-19, multiple times. Each time a person is infected or reinfected with SARS-CoV-2, they have a risk of developing Long COVID.
• While most people with Long COVID have evidence of infection or COVID-19 illness, in some cases, a person with Long COVID may not have tested positive for the virus or known they were infected.
• CDC and partners are working to understand more about who experiences Long COVID and why, including whether groups disproportionately impacted by COVID-19 are at higher risk.

In July 2021, Long COVID was added as a recognized condition that could result in a disability under the Americans with Disabilities Act (ADA). Learn more: Guidance on “Long COVID” as a Disability Under the ADA .

About Long COVID

Long COVID is a wide range of new, returning, or ongoing health problems that people experience after being infected with the virus that causes COVID-19. Most people with COVID-19 get better within a few days to a few weeks after infection, so at least 4 weeks after infection is the start of when Long COVID could first be identified. Anyone who was infected can experience Long COVID. Most people with Long COVID experienced symptoms days after first learning they had COVID-19, but some people who later experienced Long COVID did not know when they got infected.

There is no test that determines if your symptoms or condition is due to COVID-19. Long COVID is not one illness. Your healthcare provider considers a diagnosis of Long COVID based on your health history, including if you had a diagnosis of COVID-19 either by a positive test or by symptoms or exposure, as well as based on a health examination.

Science behind Long COVID

RECOVER: Researching COVID to Enhance Recovery

People with Long COVID may experience many symptoms.

People with Long COVID can have a wide range of symptoms that can last weeks, months, or even years after infection. Sometimes the symptoms can even go away and come back again. For some people, Long COVID can last weeks, months, or years after COVID-19 illness and can sometimes result in disability.

Long COVID may not affect everyone the same way. People with Long COVID may experience health problems from different types and combinations of symptoms that may emerge, persist, resolve, and reemerge over different lengths of time. Though most patients’ symptoms slowly improve with time, speaking with your healthcare provider about the symptoms you are experiencing after having COVID-19 could help determine if you might have Long COVID.

People who experience Long COVID most commonly report:

General symptoms ( Not a Comprehensive List)

• Tiredness or fatigue that interferes with daily life
• Symptoms that get worse after physical or mental effort (also known as “ post-exertional malaise ”)

Respiratory and heart symptoms

• Difficulty breathing or shortness of breath
• Fast-beating or pounding heart (also known as heart palpitations)

Neurological symptoms

• Difficulty thinking or concentrating (sometimes referred to as “brain fog”)
• Sleep problems
• Dizziness when you stand up (lightheadedness)
• Pins-and-needles feelings
• Change in smell or taste
• Depression or anxiety

Digestive symptoms

• Stomach pain

Other symptoms

• Joint or muscle pain
• Changes in menstrual cycles

Symptoms that are hard to explain and manage

Some people with Long COVID have symptoms that are not explained by tests or easy to manage.

People with Long COVID may develop or continue to have symptoms that are hard to explain and manage. Clinical evaluations and results of routine blood tests, chest X-rays, and electrocardiograms may be normal. The symptoms are similar to those reported by people with myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS) and other poorly understood chronic illnesses that may occur after other infections. People with these unexplained symptoms may be misunderstood by their healthcare providers, which can result in a delay in diagnosis and receiving the appropriate care or treatment.

Review these tips to help prepare for a healthcare provider appointment for Long COVID.

Health conditions

Some people experience new health conditions after COVID-19 illness.

Some people, especially those who had severe COVID-19, experience multiorgan effects or autoimmune conditions with symptoms lasting weeks, months, or even years after COVID-19 illness. Multi-organ effects can involve many body systems, including the heart, lung, kidney, skin, and brain. As a result of these effects, people who have had COVID-19 may be more likely to develop new health conditions such as diabetes, heart conditions, blood clots, or neurological conditions compared with people who have not had COVID-19.

People experiencing any severe illness may develop health problems

People experiencing any severe illness, hospitalization, or treatment may develop problems such as post-intensive care syndrome (PICS).

PICS refers to the health effects that may begin when a person is in an intensive care unit (ICU), and which may persist after a person returns home. These effects can include muscle weakness, problems with thinking and judgment, and symptoms of post-traumatic stress disorder  (PTSD), a long-term reaction to a very stressful event. While PICS is not specific to infection with SARS-CoV-2, it may occur and contribute to the person’s experience of Long COVID. For people who experience PICS following a COVID-19 diagnosis, it is difficult to determine whether these health problems are caused by a severe illness, the virus itself, or a combination of both.

People More Likely to Develop Long COVID

Some people may be more at risk for developing Long COVID.

Researchers are working to understand which people or groups of people are more likely to have Long COVID, and why. Studies have shown that some groups of people may be affected more by Long COVID. These are examples and not a comprehensive list of people or groups who might be more at risk than other groups for developing Long COVID:

• People who have experienced more severe COVID-19 illness, especially those who were hospitalized or needed intensive care.
• People who had underlying health conditions prior to COVID-19.
• People who did not get a COVID-19 vaccine.

Health Inequities May Affect Populations at Risk for Long COVID

Some people are at increased risk of getting sick from COVID-19 because of where they live or work, or because they can’t get health care. Health inequities may put some people from racial or ethnic minority groups and some people with disabilities at greater risk for developing Long COVID. Scientists are researching some of those factors that may place these communities at higher risk of getting infected or developing Long COVID.

Preventing Long COVID

The best way to prevent Long COVID is to protect yourself and others from becoming infected. For people who are eligible, CDC recommends staying up to date on COVID-19 vaccination , along with improving ventilation, getting tested for COVID-19 if needed, and seeking treatment for COVID-19 if eligible. Additional preventative measures include avoiding close contact with people who have a confirmed or suspected COVID-19 illness and washing hands  or using alcohol-based hand sanitizer.

Research suggests that people who get a COVID-19 infection after vaccination are less likely to report Long COVID, compared to people who are unvaccinated.

CDC, other federal agencies, and non-federal partners are working to identify further measures to lessen a person’s risk of developing Long COVID. Learn more about protecting yourself and others from COVID-19 .

Living with Long COVID

Living with Long COVID can be hard, especially when there are no immediate answers or solutions.

People experiencing Long COVID can seek care from a healthcare provider to come up with a personal medical management plan that can help improve their symptoms and quality of life. Review these tips  to help prepare for a healthcare provider appointment for Long COVID. In addition, there are many support groups being organized that can help patients and their caregivers.

Although Long COVID appears to be less common in children and adolescents than in adults, long-term effects after COVID-19 do occur in children and adolescents .

Talk to your doctor if you think you or your child has Long COVID. Learn more: Tips for Talking to Your Healthcare Provider about Post-COVID Conditions

Data for Long COVID

Studies are in progress to better understand Long COVID and how many people experience them.

CDC is using multiple approaches to estimate how many people experience Long COVID. Each approach can provide a piece of the puzzle to give us a better picture of who is experiencing Long COVID. For example, some studies look for the presence of Long COVID based on self-reported symptoms, while others collect symptoms and conditions recorded in medical records. Some studies focus only on people who have been hospitalized, while others include people who were not hospitalized. The estimates for how many people experience Long COVID can be quite different depending on who was included in the study, as well as how and when the study collected information.  Estimates of the proportion of people who had COVID-19 that go on to experience Long COVID can vary.

CDC posts data on Long COVID and provides analyses, the most recent of which can be found on the U.S. Census Bureau’s Household Pulse Survey .

CDC and other federal agencies, as well as academic institutions and research organizations, are working to learn more about the short- and long-term health effects associated with COVID-19 , who gets them and why.

Scientists are also learning more about how new variants could potentially affect Long COVID. We are still learning to what extent certain groups are at higher risk, and if different groups of people tend to experience different types of Long COVID. CDC has several studies that will help us better understand Long COVID and how healthcare providers can treat or support patients with these long-term effects. CDC will continue to share information with healthcare providers to help them evaluate and manage these conditions.

CDC is working to:

• Better identify the most frequent symptoms and diagnoses experienced by patients with Long COVID.
• Better understand how many people are affected by Long COVID, and how often people who are infected with COVID-19 develop Long COVID
• Better understand risk factors and protective factors, including which groups might be more at risk, and if different groups experience different symptoms.
• Help understand how Long COVID limit or restrict people’s daily activity.
• Help identify groups that have been more affected by Long COVID, lack access to care and treatment for Long COVID, or experience stigma.
• Better understand the role vaccination plays in preventing Long COVID.
• Collaborate with professional medical groups to develop and offer clinical guidance and other educational materials for healthcare providers, patients, and the public.

Related Pages

• Caring for People with Post-COVID Conditions
• Preparing for Appointments for Post-COVID Conditions
• Researching COVID to Enhance Recovery
• Guidance on “Long COVID” as a Disability Under the ADA

For Healthcare Professionals

• Post-COVID Conditions: Healthcare Providers

Search for and find historical COVID-19 pages and files. Please note the content on these pages and files is no longer being updated and may be out of date.

• Visit archive.cdc.gov for a historical snapshot of the COVID-19 website, capturing the end of the Federal Public Health Emergency on June 28, 2023.
• Visit the dynamic COVID-19 collection  to search the COVID-19 website as far back as July 30, 2021.

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The state of AI in early 2024: Gen AI adoption spikes and starts to generate value

If 2023 was the year the world discovered generative AI (gen AI) , 2024 is the year organizations truly began using—and deriving business value from—this new technology. In the latest McKinsey Global Survey  on AI, 65 percent of respondents report that their organizations are regularly using gen AI, nearly double the percentage from our previous survey just ten months ago. Respondents’ expectations for gen AI’s impact remain as high as they were last year , with three-quarters predicting that gen AI will lead to significant or disruptive change in their industries in the years ahead.

About the authors

This article is a collaborative effort by Alex Singla , Alexander Sukharevsky , Lareina Yee , and Michael Chui , with Bryce Hall , representing views from QuantumBlack, AI by McKinsey, and McKinsey Digital.

Organizations are already seeing material benefits from gen AI use, reporting both cost decreases and revenue jumps in the business units deploying the technology. The survey also provides insights into the kinds of risks presented by gen AI—most notably, inaccuracy—as well as the emerging practices of top performers to mitigate those challenges and capture value.

AI adoption surges

Interest in generative AI has also brightened the spotlight on a broader set of AI capabilities. For the past six years, AI adoption by respondents’ organizations has hovered at about 50 percent. This year, the survey finds that adoption has jumped to 72 percent (Exhibit 1). And the interest is truly global in scope. Our 2023 survey found that AI adoption did not reach 66 percent in any region; however, this year more than two-thirds of respondents in nearly every region say their organizations are using AI. 1 Organizations based in Central and South America are the exception, with 58 percent of respondents working for organizations based in Central and South America reporting AI adoption. Looking by industry, the biggest increase in adoption can be found in professional services. 2 Includes respondents working for organizations focused on human resources, legal services, management consulting, market research, R&D, tax preparation, and training.

Also, responses suggest that companies are now using AI in more parts of the business. Half of respondents say their organizations have adopted AI in two or more business functions, up from less than a third of respondents in 2023 (Exhibit 2).

Gen AI adoption is most common in the functions where it can create the most value

Most respondents now report that their organizations—and they as individuals—are using gen AI. Sixty-five percent of respondents say their organizations are regularly using gen AI in at least one business function, up from one-third last year. The average organization using gen AI is doing so in two functions, most often in marketing and sales and in product and service development—two functions in which previous research  determined that gen AI adoption could generate the most value 3 “ The economic potential of generative AI: The next productivity frontier ,” McKinsey, June 14, 2023. —as well as in IT (Exhibit 3). The biggest increase from 2023 is found in marketing and sales, where reported adoption has more than doubled. Yet across functions, only two use cases, both within marketing and sales, are reported by 15 percent or more of respondents.

Gen AI also is weaving its way into respondents’ personal lives. Compared with 2023, respondents are much more likely to be using gen AI at work and even more likely to be using gen AI both at work and in their personal lives (Exhibit 4). The survey finds upticks in gen AI use across all regions, with the largest increases in Asia–Pacific and Greater China. Respondents at the highest seniority levels, meanwhile, show larger jumps in the use of gen Al tools for work and outside of work compared with their midlevel-management peers. Looking at specific industries, respondents working in energy and materials and in professional services report the largest increase in gen AI use.

Investments in gen AI and analytical AI are beginning to create value

The latest survey also shows how different industries are budgeting for gen AI. Responses suggest that, in many industries, organizations are about equally as likely to be investing more than 5 percent of their digital budgets in gen AI as they are in nongenerative, analytical-AI solutions (Exhibit 5). Yet in most industries, larger shares of respondents report that their organizations spend more than 20 percent on analytical AI than on gen AI. Looking ahead, most respondents—67 percent—expect their organizations to invest more in AI over the next three years.

Where are those investments paying off? For the first time, our latest survey explored the value created by gen AI use by business function. The function in which the largest share of respondents report seeing cost decreases is human resources. Respondents most commonly report meaningful revenue increases (of more than 5 percent) in supply chain and inventory management (Exhibit 6). For analytical AI, respondents most often report seeing cost benefits in service operations—in line with what we found last year —as well as meaningful revenue increases from AI use in marketing and sales.

Inaccuracy: The most recognized and experienced risk of gen AI use

As businesses begin to see the benefits of gen AI, they’re also recognizing the diverse risks associated with the technology. These can range from data management risks such as data privacy, bias, or intellectual property (IP) infringement to model management risks, which tend to focus on inaccurate output or lack of explainability. A third big risk category is security and incorrect use.

Respondents to the latest survey are more likely than they were last year to say their organizations consider inaccuracy and IP infringement to be relevant to their use of gen AI, and about half continue to view cybersecurity as a risk (Exhibit 7).

Conversely, respondents are less likely than they were last year to say their organizations consider workforce and labor displacement to be relevant risks and are not increasing efforts to mitigate them.

In fact, inaccuracy— which can affect use cases across the gen AI value chain , ranging from customer journeys and summarization to coding and creative content—is the only risk that respondents are significantly more likely than last year to say their organizations are actively working to mitigate.

Some organizations have already experienced negative consequences from the use of gen AI, with 44 percent of respondents saying their organizations have experienced at least one consequence (Exhibit 8). Respondents most often report inaccuracy as a risk that has affected their organizations, followed by cybersecurity and explainability.

Our previous research has found that there are several elements of governance that can help in scaling gen AI use responsibly, yet few respondents report having these risk-related practices in place. 4 “ Implementing generative AI with speed and safety ,” McKinsey Quarterly , March 13, 2024. For example, just 18 percent say their organizations have an enterprise-wide council or board with the authority to make decisions involving responsible AI governance, and only one-third say gen AI risk awareness and risk mitigation controls are required skill sets for technical talent.

Bringing gen AI capabilities to bear

The latest survey also sought to understand how, and how quickly, organizations are deploying these new gen AI tools. We have found three archetypes for implementing gen AI solutions : takers use off-the-shelf, publicly available solutions; shapers customize those tools with proprietary data and systems; and makers develop their own foundation models from scratch. 5 “ Technology’s generational moment with generative AI: A CIO and CTO guide ,” McKinsey, July 11, 2023. Across most industries, the survey results suggest that organizations are finding off-the-shelf offerings applicable to their business needs—though many are pursuing opportunities to customize models or even develop their own (Exhibit 9). About half of reported gen AI uses within respondents’ business functions are utilizing off-the-shelf, publicly available models or tools, with little or no customization. Respondents in energy and materials, technology, and media and telecommunications are more likely to report significant customization or tuning of publicly available models or developing their own proprietary models to address specific business needs.

Respondents most often report that their organizations required one to four months from the start of a project to put gen AI into production, though the time it takes varies by business function (Exhibit 10). It also depends upon the approach for acquiring those capabilities. Not surprisingly, reported uses of highly customized or proprietary models are 1.5 times more likely than off-the-shelf, publicly available models to take five months or more to implement.

Gen AI high performers are excelling despite facing challenges

Gen AI is a new technology, and organizations are still early in the journey of pursuing its opportunities and scaling it across functions. So it’s little surprise that only a small subset of respondents (46 out of 876) report that a meaningful share of their organizations’ EBIT can be attributed to their deployment of gen AI. Still, these gen AI leaders are worth examining closely. These, after all, are the early movers, who already attribute more than 10 percent of their organizations’ EBIT to their use of gen AI. Forty-two percent of these high performers say more than 20 percent of their EBIT is attributable to their use of nongenerative, analytical AI, and they span industries and regions—though most are at organizations with less than $1 billion in annual revenue. The AI-related practices at these organizations can offer guidance to those looking to create value from gen AI adoption at their own organizations.

To start, gen AI high performers are using gen AI in more business functions—an average of three functions, while others average two. They, like other organizations, are most likely to use gen AI in marketing and sales and product or service development, but they’re much more likely than others to use gen AI solutions in risk, legal, and compliance; in strategy and corporate finance; and in supply chain and inventory management. They’re more than three times as likely as others to be using gen AI in activities ranging from processing of accounting documents and risk assessment to R&D testing and pricing and promotions. While, overall, about half of reported gen AI applications within business functions are utilizing publicly available models or tools, gen AI high performers are less likely to use those off-the-shelf options than to either implement significantly customized versions of those tools or to develop their own proprietary foundation models.

What else are these high performers doing differently? For one thing, they are paying more attention to gen-AI-related risks. Perhaps because they are further along on their journeys, they are more likely than others to say their organizations have experienced every negative consequence from gen AI we asked about, from cybersecurity and personal privacy to explainability and IP infringement. Given that, they are more likely than others to report that their organizations consider those risks, as well as regulatory compliance, environmental impacts, and political stability, to be relevant to their gen AI use, and they say they take steps to mitigate more risks than others do.

Gen AI high performers are also much more likely to say their organizations follow a set of risk-related best practices (Exhibit 11). For example, they are nearly twice as likely as others to involve the legal function and embed risk reviews early on in the development of gen AI solutions—that is, to “ shift left .” They’re also much more likely than others to employ a wide range of other best practices, from strategy-related practices to those related to scaling.

In addition to experiencing the risks of gen AI adoption, high performers have encountered other challenges that can serve as warnings to others (Exhibit 12). Seventy percent say they have experienced difficulties with data, including defining processes for data governance, developing the ability to quickly integrate data into AI models, and an insufficient amount of training data, highlighting the essential role that data play in capturing value. High performers are also more likely than others to report experiencing challenges with their operating models, such as implementing agile ways of working and effective sprint performance management.

About the research

The online survey was in the field from February 22 to March 5, 2024, and garnered responses from 1,363 participants representing the full range of regions, industries, company sizes, functional specialties, and tenures. Of those respondents, 981 said their organizations had adopted AI in at least one business function, and 878 said their organizations were regularly using gen AI in at least one function. To adjust for differences in response rates, the data are weighted by the contribution of each respondent’s nation to global GDP.

Alex Singla and Alexander Sukharevsky  are global coleaders of QuantumBlack, AI by McKinsey, and senior partners in McKinsey’s Chicago and London offices, respectively; Lareina Yee  is a senior partner in the Bay Area office, where Michael Chui , a McKinsey Global Institute partner, is a partner; and Bryce Hall  is an associate partner in the Washington, DC, office.

They wish to thank Kaitlin Noe, Larry Kanter, Mallika Jhamb, and Shinjini Srivastava for their contributions to this work.

This article was edited by Heather Hanselman, a senior editor in McKinsey’s Atlanta office.

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OpenSAFELY: Effectiveness of COVID-19 vaccination in children and adolescents

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Background Children and adolescents in England were offered BNT162b2 as part of the national COVID-19 vaccine roll out from September 2021. We assessed the safety and effectiveness of first and second dose BNT162b2 COVID-19 vaccination in children and adolescents in England.

Methods With the approval of NHS England, we conducted an observational study in the OpenSAFELY-TPP database, including a) adolescents aged 12-15 years, and b) children aged 5-11 years and comparing individuals receiving i) first vaccination with unvaccinated controls and ii) second vaccination to single-vaccinated controls. We matched vaccinated individuals with controls on age, sex, region, and other important characteristics. Outcomes were positive SARS-CoV-2 test (adolescents only); COVID-19 A&E attendance; COVID-19 hospitalisation; COVID-19 critical care admission; COVID-19 death, with non-COVID-19 death and fractures as negative control outcomes and A&E attendance, unplanned hospitalisation, pericarditis, and myocarditis as safety outcomes.

Results Amongst 820,926 previously unvaccinated adolescents, the incidence rate ratio (IRR) for positive SARS-CoV-2 test comparing vaccination with no vaccination was 0.74 (95% CI 0.72-0.75), although the 20-week risks were similar. The IRRs were 0.60 (0.37-0.97) for COVID-19 A&E attendance, 0.58 (0.38-0.89) for COVID-19 hospitalisation, 0.99 (0.93-1.06) for fractures, 0.89 (0.87-0.91) for A&E attendances and 0.88 (0.81-0.95) for unplanned hospitalisation. Amongst 441,858 adolescents who had received first vaccination IRRs comparing second dose with first dose only were 0.67 (0.65-0.69) for positive SARS-CoV-2 test, 1.00 (0.20-4.96) for COVID-19 A&E attendance, 0.60 (0.26-1.37) for COVID-19 hospitalisation, 0.94 (0.84-1.05) for fractures, 0.93 (0.89-0.98) for A&E attendance and 0.99 (0.86-1.13) for unplanned hospitalisation. Amongst 283,422 previously unvaccinated children and 132,462 children who had received a first vaccine dose, COVID-19-related outcomes were too rare to allow IRRs to be estimated precisely. A&E attendance and unplanned hospitalisation were slightly higher after first vaccination (IRRs versus no vaccination 1.05 (1.01-1.10) and 1.10 (0.95-1.26) respectively) but slightly lower after second vaccination (IRRs versus first dose 0.95 (0.86-1.05) and 0.78 (0.56-1.08) respectively). There were no COVID-19-related deaths in any group. Fewer than seven (exact number redacted) COVID-19-related critical care admissions occurred in the adolescent first dose vs unvaccinated cohort. Among both adolescents and children, myocarditis and pericarditis were documented only in the vaccinated groups, with rates of 27 and 10 cases/million after first and second doses respectively.

Conclusion BNT162b2 vaccination in adolescents reduced COVID-19 A&E attendance and hospitalisation, although these outcomes were rare. Protection against positive SARS-CoV-2 tests was transient.

Competing Interest Statement

BG has received research funding from the Laura and John Arnold Foundation, the NHS National Institute for Health Research (NIHR), the NIHR School of Primary Care Research, NHS England, the NIHR Oxford Biomedical Research Centre, the Mohn-Westlake Foundation, NIHR Applied Research Collaboration Oxford and Thames Valley, the Wellcome Trust, the Good Thinking Foundation, Health Data Research UK, the Health Foundation, the World Health Organisation, UKRI MRC, Asthma UK, the British Lung Foundation, and the Longitudinal Health and Wellbeing strand of the National Core Studies programme; he is a Non-Executive Director at NHS Digital; he also receives personal income from speaking and writing for lay audiences on the misuse of science. BMK is also employed by NHS England working on medicines policy and clinical lead for primary care medicines data. IJD has received unrestricted research grants and holds shares in GlaxoSmithKline (GSK).

Funding Statement

The OpenSAFELY Platform is supported by grants from the Wellcome Trust (222097/Z/20/Z); MRC (MR/V015757/1, MC_PC-20059, MR/W016729/1); NIHR (NIHR135559, COV-LT2-0073), and Health Data Research UK (HDRUK2021.000, 2021.0157). In addition, this research used data assets made available as part of the Data and Connectivity National Core Study, led by Health Data Research UK in partnership with the Office for National Statistics and funded by UK Research and Innovation (grant ref MC_PC_20058). BG has also received funding from: the Bennett Foundation, the Wellcome Trust, NIHR Oxford Biomedical Research Centre, NIHR Applied Research Collaboration Oxford and Thames Valley, the Mohn-Westlake Foundation; all Bennett Institute staff are supported by BG's grants on this work. The views expressed are those of the authors and not necessarily those of the NIHR, NHS England, UK Health Security Agency (UKHSA) or the Department of Health and Social Care.

Funders had no role in the study design, collection, analysis, and interpretation of data; in the writing of the report; and in the decision to submit the article for publication.

Author Declarations

I confirm all relevant ethical guidelines have been followed, and any necessary IRB and/or ethics committee approvals have been obtained.

The details of the IRB/oversight body that provided approval or exemption for the research described are given below:

This study was approved by the Health Research Authority (REC reference 20/LO/0651) and by the London School of Hygeine and Tropical Medicine Ethics Board (reference 21863).

I confirm that all necessary patient/participant consent has been obtained and the appropriate institutional forms have been archived, and that any patient/participant/sample identifiers included were not known to anyone (e.g., hospital staff, patients or participants themselves) outside the research group so cannot be used to identify individuals.

I understand that all clinical trials and any other prospective interventional studies must be registered with an ICMJE-approved registry, such as ClinicalTrials.gov. I confirm that any such study reported in the manuscript has been registered and the trial registration ID is provided (note: if posting a prospective study registered retrospectively, please provide a statement in the trial ID field explaining why the study was not registered in advance).

I have followed all appropriate research reporting guidelines, such as any relevant EQUATOR Network research reporting checklist(s) and other pertinent material, if applicable.

Data Availability

All data were linked, stored and analysed securely using the OpenSAFELY platform, https://www.opensafely.org/ , as part of the NHS England OpenSAFELY COVID-19 service. Data include pseudonymised data such as coded diagnoses, medications and physiological parameters. No free text data was included. All code is shared openly for review and re-use under MIT open license [ https://github.com/opensafely/vaccine-effectiveness-in-kids ]. Detailed pseudonymised patient data is potentially re-identifiable and therefore not shared. Primary care records managed by the GP software provider, TPP were linked to ONS death data and the Index of Multiple Deprivation through OpenSAFELY.

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