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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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Making statistics intuitive

What is an Observational Study: Definition & Examples

By Jim Frost 10 Comments

What is an Observational Study?

An observational study uses sample data to find correlations in situations where the researchers do not control the treatment, or independent variable, that relates to the primary research question. The definition of an observational study hinges on the notion that the researchers only observe subjects and do not assign them to the control and treatment groups. That’s the key difference between an observational study vs experiment. These studies are also known as quasi-experiments and correlational studies .

True experiments assign subject to the experimental groups where the researchers can manipulate the conditions. Unfortunately, random assignment is not always possible. For these cases, you can conduct an observational study.

In this post, learn about the types of observational studies, why they are susceptible to confounding variables, and how they compare to experiments. I’ll close this post by reviewing a published observational study about vitamin supplement usage.

Observational Study Definition

In an observational study, the researchers only observe the subjects and do not interfere or try to influence the outcomes. In other words, the researchers do not control the treatments or assign subjects to experimental groups. Instead, they observe and measure variables of interest and look for relationships between them. Usually, researchers conduct observational studies when it is difficult, impossible, or unethical to assign study participants to the experimental groups randomly. If you can’t randomly assign subjects to the treatment and control groups, then you observe the subjects in their self-selected states.

Observational Study vs Experiment

Randomized experiments provide better results than observational studies. Consequently, you should always use a randomized experiment whenever possible. However, if randomization is not possible, science should not come to a halt. After all, we still want to learn things, discover relationships, and make discoveries. For these cases, observational studies are a good alternative to a true experiment. Let’s compare the differences between an observational study vs. an experiment.

Random assignment in an experiment reduces systematic differences between experimental groups at the beginning of the study, which increases your confidence that the treatments caused any differences between groups you observe at the end of the study. In contrast, an observational study uses self-formed groups that can have pre-existing differences, which introduces the problem of confounding variables. More on that later!

In a randomized experiment, randomization tends to equalize confounders between groups and, thereby, prevents problems. In my post about random assignment , I describe that process as an elegant solution for confounding variables. You don’t need to measure or even know which variables are confounders, and randomization will still mitigate their effects. Additionally, you can use control variables in an experiment to keep the conditions as consistent as possible. For more detail about the differences, read Observational Study vs. Experiment .

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 .

Related posts : Experimental Design: Definition and Examples , Randomized Controlled Trials (RCTs) , and Control Groups in Experiments

Observational Study Examples

Consider using an observational study when random assignment for an experiment is problematic. This approach allows us to proceed and draw conclusions about effects even though we can’t control the independent variables. The following observational study examples will help you understand when and why to use them.

For example, if you’re studying how depression affects performance of an activity, it’s impossible to assign subjects to the depression and control group randomly. However, you can have subjects with and without depression perform the activity and compare the results in an observational study.

Or imagine trying to assign subjects to cigarette smoking and non-smoking groups randomly?! However, you can observe people in both groups and assess the differences in health outcomes in an observational study.

Suppose you’re studying a treatment for a disease. Ideally, you recruit a group of patients who all have the disease, and then randomly assign them to the treatment and control group. However, it’s unethical to withhold the treatment, which rules out a control group. Instead, you can compare patients who voluntarily do not use the medicine to those who do use it.

In all these observational study examples, the researchers do not assign subjects to the experimental groups. Instead, they observe people who are already in these groups and compare the outcomes. Hence, the scientists must use an observational study vs. an experiment.

Types of Observational Studies

The observational study definition states that researchers only observe the outcomes and do not manipulate or control factors . Despite this limitation, there various types of observational studies.

The following experimental designs are three standard types of observational studies.

• Cohort Study : A longitudinal observational study that follows a group who share a defining characteristic. These studies frequently determine whether exposure to risk factor affects an outcome over time.
• Case-Control Study : A retrospective observational study that compares two existing groups—the case group with the condition and the control group without it. Researchers compare the groups looking for potential risk factors for the condition.
• Cross-Sectional Study : Takes a snapshot of a moment in time so researchers can understand the prevalence of outcomes and correlations between variables at that instant.

Qualitative research studies are usually observational in nature, but they collect non-numeric data and do not perform statistical analyses.

Retrospective studies must be observational.

Later in this post, we’ll closely examine a quantitative observational study example that assesses vitamin supplement consumption and how that affects the risk of death. It’s possible to use random assignment to place each subject in either the vitamin treatment group or the control group. However, the study assesses vitamin consumption in 40,000 participants over the course of two decades. It’s unrealistic to enforce the treatment and control protocols over such a long time for so many people!

Drawbacks of Observational Studies

While observational studies get around the inability to assign subjects randomly, this approach opens the door to the problem of confounding variables. A confounding variable, or confounder, correlates with both the experimental groups and the outcome variable. Because there is no random process that equalizes the experimental groups in an observational study, confounding variables can systematically differ between groups when the study begins. Consequently, confounders can be the actual cause for differences in outcome at the end of the study rather than the primary variable of interest. If an experiment does not account for confounding variables, confounders can bias the results and create spurious correlations .

Performing an observational study can decrease the internal validity of your study but increase the external validity. Learn more about internal and external validity .

Let’s see how this works. Imagine an observational study that compares people who take vitamin supplements to those who do not. People who use vitamin supplements voluntarily will tend to have other healthy habits that exist at the beginning of the study. These healthy habits are confounding variables. If there are differences in health outcomes at the end of the study, it’s possible that these healthy habits actually caused them rather than the vitamin consumption itself. In short, confounders confuse the results because they provide alternative explanations for the differences.

Despite the limitations, an observational study can be a valid approach. However, you must ensure that your research accounts for confounding variables. Fortunately, there are several methods for doing just that!

Learn more about Correlation vs. Causation: Understanding the Differences .

Accounting for Confounding Variables in an Observational Study

Because observational studies don’t use random assignment, confounders can be distributed disproportionately between conditions. Consequently, experimenters need to know which variables are confounders, measure them, and then use a method to account for them. It involves more work, and the additional measurements can increase the costs. And there’s always a chance that researchers will fail to identify a confounder, not account for it, and produce biased results. However, if randomization isn’t an option, then you probably need to consider an observational study.

Trait matching and statistically controlling confounders using multivariate procedures are two standard approaches for incorporating confounding variables.

Related post : Causation versus Correlation in Statistics

Matching in Observational Studies

Matching is a technique that involves selecting study participants with similar characteristics outside the variable of interest or treatment. Rather than using random assignment to equalize the experimental groups, the experimenters do it by matching observable characteristics. For every participant in the treatment group, the researchers find a participant with comparable traits to include in the control group. Matching subjects facilitates valid comparisons between those groups. The researchers use subject-area knowledge to identify characteristics that are critical to match.

For example, a vitamin supplement study using matching will select subjects who have similar health-related habits and attributes. The goal is that vitamin consumption will be the primary difference between the groups, which helps you attribute differences in health outcomes to vitamin consumption. However, the researchers are still observing participants who decide whether they consume supplements.

Matching has some drawbacks. The experimenters might not be aware of all the relevant characteristics they need to match. In other words, the groups might be different in an essential aspect that the researchers don’t recognize. For example, in the hypothetical vitamin study, there might be a healthy habit or attribute that affects the outcome that the researchers don’t measure and match. These unmatched characteristics might cause the observed differences in outcomes rather than vitamin consumption.

Learn more about Matched Pairs Design: Uses & Examples .

Using Multiple Regression in Observational Studies

Random assignment and matching use different methods to equalize the experimental groups in an observational study. However, statistical techniques, such as multiple regression analysis , don’t try to equalize the groups but instead use a model that accounts for confounding variables. These studies statistically control for confounding variables.

In multiple regression analysis, including a variable in the model holds it constant while you vary the variable/treatment of interest. For information about this property, read my post When Should I Use Regression Analysis?

As with matching, the challenge is to identify, measure, and include all confounders in the regression model. Failure to include a confounding variable in a regression model can cause omitted variable bias to distort your results.

Next, we’ll look at a published observational study that uses multiple regression to account for confounding variables.

Related post : Independent and Dependent Variables in a Regression Model

Vitamin Supplement Observational Study Example

Murso et al. (2011)* use a longitudinal observational study that ran 22 years to assess differences in death rates for subjects who used vitamin supplements regularly compared to those who did not use them. This study used surveys to record the characteristics of approximately 40,000 participants. The surveys asked questions about potential confounding variables such as demographic information, food intake, health details, physical activity, and, of course, supplement intake.

Because this is an observational study, the subjects decided for themselves whether they were taking vitamin supplements. Consequently, it’s safe to assume that supplement users and non-users might be different in other ways. From their article, the researchers found the following pre-existing differences between the two groups:

Supplement users had a lower prevalence of diabetes mellitus, high blood pressure, and smoking status; a lower BMI and waist to hip ratio, and were less likely to live on a farm. Supplement users had a higher educational level, were more physically active and were more likely to use estrogen replacement therapy. Also, supplement users were more likely to have a lower intake of energy, total fat, and monounsaturated fatty acids, saturated fatty acids and to have a higher intake of protein, carbohydrates, polyunsaturated fatty acids, alcohol, whole grain products, fruits, and vegetables.

Whew! That’s a long list of differences! Supplement users were different from non-users in a multitude of ways that are likely to affect their risk of dying. The researchers must account for these confounding variables when they compare supplement users to non-users. If they do not, their results can be biased.

This example illustrates a key difference between an observational study vs experiment. In a randomized experiment, the randomization would have equalized the characteristics of those the researchers assigned to the treatment and control groups. Instead, the study works with self-sorted groups that have numerous pre-existing differences!

Using Multiple Regression to Statistically Control for Confounders

To account for these initial differences in the vitamin supplement observational study, the researchers use regression analysis and include the confounding variables in the model.

The researchers present three regression models. The simplest model accounts only for age and caloric intake. Next, are two models that include additional confounding variables beyond age and calories. The first model adds various demographic information and seven health measures. The second model includes everything in the previous model and adds several more specific dietary intake measures. Using statistical significance as a guide for specifying the correct regression model , the researchers present the model with the most variables as the basis for their final results.

It’s instructive to compare the raw results and the final regression results.

Raw results

The raw differences in death risks for consumers of folic acid, vitamin B6, magnesium, zinc, copper, and multivitamins are NOT statistically significant. However, the raw results show a significant reduction in the death risk for users of B complex, C, calcium, D, and E.

However, those are the raw results for the observational study, and they do not control for the long list of differences between the groups that exist at the beginning of the study. After using the regression model to control for the confounding variables statistically, the results change dramatically.

Adjusted results

Of the 15 supplements that the study tracked in the observational study, researchers found consuming seven of these supplements were linked to a statistically significant INCREASE in death risk ( p-value < 0.05): multivitamins (increase in death risk 2.4%), vitamin B6 (4.1%), iron (3.9%), folic acid (5.9%), zinc (3.0%), magnesium (3.6%), and copper (18.0%). Only calcium was associated with a statistically significant reduction in death risk of 3.8%.

In short, the raw results suggest that those who consume supplements either have the same or lower death risks than non-consumers. However, these results do not account for the multitude of healthier habits and attributes in the group that uses supplements.

In fact, these confounders seem to produce most of the apparent benefits in the raw results because, after you statistically control the effects of these confounding variables, the results worsen for those who consume vitamin supplements. The adjusted results indicate that most vitamin supplements actually increase your death risk!

This research illustrates the differences between an observational study vs experiment. Namely how the pre-existing differences between the groups allow confounders to bias the raw results, making the vitamin consumption outcomes look better than they really are.

In conclusion, if you can’t randomly assign subjects to the experimental groups, an observational study might be right for you. However, be aware that you’ll need to identify, measure, and account for confounding variables in your experimental design.

Jaakko Mursu, PhD; Kim Robien, PhD; Lisa J. Harnack, DrPH, MPH; Kyong Park, PhD; David R. Jacobs Jr, PhD; Dietary Supplements and Mortality Rate in Older Women: The Iowa Women’s Health Study ; Arch Intern Med . 2011;171(18):1625-1633.

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Reader Interactions

December 30, 2023 at 5:05 am

I see, but our professor required us to indicate what year it was put into the article. May you tell me what year was this published originally? <3

December 30, 2023 at 3:40 pm

December 29, 2023 at 10:46 am

Hi, may I use your article as a citation for my thesis paper? If so, may I know the exact date you published this article? Thank you!

December 29, 2023 at 2:13 pm

Definitely feel free to cite this article! 🙂

When citing online resources, you typically use an “Accessed” date rather than a publication date because online content can change over time. For more information, read Purdue University’s Citing Electronic Resources .

November 18, 2021 at 10:09 pm

Love your content and has been very helpful!

Can you please advise the question below using an observational data set:

I have three years of observational GPS data collected on athletes (2019/2020/2021). Approximately 14-15 athletes per game and 8 games per year. The GPS software outputs 50+ variables for each athlete in each game, which we have narrowed down to 16 variables of interest from previous research.

2 factors 1) Period (first half, second half, and whole game), 2) Position (two groups with three subgroups in each – forwards (group 1, group 2, group 3) and backs (group 1, group 2, group 3))

16 variables of interest – all numerical and scale variables. Some of these are correlated, but not all.

My understanding is that I can use a oneway ANOVA for each year on it’s own, using one factor at a time (period or position) with post hoc analysis. This is fine, if data meets assumptions and is normally distributed. This tells me any significant interactions between variables of interest with chosen factor. For example, with position factor, do forwards in group 1 cover more total running distance than forwards in group 2 or backs in group 3.

However, I want to go deeper with my analysis. If I want to see if forwards in group 1 cover more total running distance in period 1 than backs in group 3 in the same period, I need an additional factor and the oneway ANOVA does not suit. Therefore I can use a twoway ANOVA instead of 2 oneway ANOVA’s and that solves the issue, correct?

This is complicated further by looking to compare 2019 to 2020 or 2019 to 2021 to identify changes over time, which would introduce a third independent variable.

I believe this would require a threeway ANOVA for this observational data set. 3 factors – Position, Period, and Year?

Are there any issues or concerns you see at first glance?

I appreciate your time and consideration.

April 12, 2021 at 2:02 pm

Could an observational study use a correlational design.

e.g. measuring effects of two variables on happiness, if you’re not intervening.

April 13, 2021 at 12:14 am

Typically, with observational studies, you’d want to include potential confounders, etc. Consequently, I’ve seen regression analysis used more frequently for observational studies to be able to control for other things because you’re not using randomization. You could use correlation to observe the relationship. However, you wouldn’t be controlling for potential confounding variables. Just something to consider.

April 11, 2021 at 1:28 pm

Hi, If I am to administer moderate doses of coffee for a hypothetical experiment, does it raise ethical concerns? Can I use random assignment for it?

April 11, 2021 at 4:06 pm

I don’t see any inherent ethical problems here as long as you describe the participant’s experience in the experiment including the coffee consumption. They key with human subjects is “informed consent.” They’re agreeing to participate based on a full and accurate understanding of what participation involves. Additionally, you as a researcher, understand the process well enough to be able to ensure their safety.

In your study, as long as subject know they’ll be drinking coffee and agree to that, I don’t see a problem. It’s a proven safe substance for the vast majority of people. If potential subjects are aware of the need to consume coffee, they can determine whether they are ok with that before agreeing to participate.

June 17, 2019 at 4:51 am

Really great article which explains observational and experimental study very well. It presents broad picture with the case study which helped a lot in understanding the core concepts. Thanks

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What is Observational Study Design and Types

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Table of Contents

Most people think of a traditional experimental design when they consider research and published research papers. There is, however, a type of research that is more observational in nature, and it is appropriately referred to as “observational studies.”

There are many valuable reasons to utilize an observational study design. But, just as in research experimental design, different methods can be used when you’re considering this type of study. In this article, we’ll look at the advantages and disadvantages of an observational study design, as well as the 3 types of observational studies.

What is Observational Study Design?

An observational study is when researchers are looking at the effect of some type of intervention, risk, a diagnostic test or treatment, without trying to manipulate who is, or who isn’t, exposed to it.

This differs from an experimental study, where the scientists are manipulating who is exposed to the treatment, intervention, etc., by having a control group, or those who are not exposed, and an experimental group, or those who are exposed to the intervention, treatment, etc. In the best studies, the groups are randomized, or chosen by chance.

Any evidence derived from systematic reviews is considered the best in the hierarchy of evidence, which considers which studies are deemed the most reliable. Next would be any evidence that comes from randomized controlled trials. Cohort studies and case studies follow, in that order.

Cohort studies and case studies are considered observational in design, whereas the randomized controlled trial would be an experimental study.

Let’s take a closer look at the different types of observational study design.

The 3 types of Observational Studies

The different types of observational studies are used for different reasons. Selecting the best type for your research is critical to a successful outcome. One of the main reasons observational studies are used is when a randomized experiment would be considered unethical. For example, a life-saving medication used in a public health emergency. They are also used when looking at aetiology, or the cause of a condition or disease, as well as the treatment of rare conditions.

Case Control Observational Study

Researchers in case control studies identify individuals with an existing health issue or condition, or “cases,” along with a similar group without the condition, or “controls.” These two groups are then compared to identify predictors and outcomes. This type of study is helpful to generate a hypothesis that can then be researched.

Cohort Observational Study

This type of observational study is often used to help understand cause and effect. A cohort observational study looks at causes, incidence and prognosis, for example. A cohort is a group of people who are linked in a particular way, for example, a birth cohort would include people who were born within a specific period of time. Scientists might compare what happens to the members of the cohort who have been exposed to some variable to what occurs with members of the cohort who haven’t been exposed.

Cross Sectional Observational Study

Unlike a cohort observational study, a cross sectional observational study does not explore cause and effect, but instead looks at prevalence. Here you would look at data from a particular group at one very specific period of time. Researchers would simply observe and record information about something present in the population, without manipulating any variables or interventions. These types of studies are commonly used in psychology, education and social science.

Advantages and Disadvantages of Observational Study Design

Observational study designs have the distinct advantage of allowing researchers to explore answers to questions where a randomized controlled trial, or RCT, would be unethical. Additionally, if the study is focused on a rare condition, studying existing cases as compared to non-affected individuals might be the most effective way to identify possible causes of the condition. Likewise, if very little is known about a condition or circumstance, a cohort study would be a good study design choice.

A primary advantage to the observational study design is that they can generally be completed quickly and inexpensively. A RCT can take years before the data is compiled and available. RCTs are more complex and involved, requiring many more logistics and details to iron out, whereas an observational study can be more easily designed and completed.

The main disadvantage of observational study designs is that they’re more open to dispute than an RCT. Of particular concern would be confounding biases. This is when a cohort might share other characteristics that affect the outcome versus the outcome stated in the study. An example would be that people who practice good sleeping habits have less heart disease. But, maybe those who practice effective sleeping habits also, in general, eat better and exercise more.

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Home » Observational Research – Methods and Guide

Observational Research – Methods and Guide

Table of Contents

Observational Research

Definition:

Observational research is a type of research method where the researcher observes and records the behavior of individuals or groups in their natural environment. In other words, the researcher does not intervene or manipulate any variables but simply observes and describes what is happening.

Observation

Observation is the process of collecting and recording data by observing and noting events, behaviors, or phenomena in a systematic and objective manner. It is a fundamental method used in research, scientific inquiry, and everyday life to gain an understanding of the world around us.

Types of Observational Research

Observational research can be categorized into different types based on the level of control and the degree of involvement of the researcher in the study. Some of the common types of observational research are:

Naturalistic Observation

In naturalistic observation, the researcher observes and records the behavior of individuals or groups in their natural environment without any interference or manipulation of variables.

Controlled Observation

In controlled observation, the researcher controls the environment in which the observation is taking place. This type of observation is often used in laboratory settings.

Participant Observation

In participant observation, the researcher becomes an active participant in the group or situation being observed. The researcher may interact with the individuals being observed and gather data on their behavior, attitudes, and experiences.

Structured Observation

In structured observation, the researcher defines a set of behaviors or events to be observed and records their occurrence.

Unstructured Observation

In unstructured observation, the researcher observes and records any behaviors or events that occur without predetermined categories.

Cross-Sectional Observation

In cross-sectional observation, the researcher observes and records the behavior of different individuals or groups at a single point in time.

Longitudinal Observation

In longitudinal observation, the researcher observes and records the behavior of the same individuals or groups over an extended period of time.

Data Collection Methods

Observational research uses various data collection methods to gather information about the behaviors and experiences of individuals or groups being observed. Some common data collection methods used in observational research include:

Field Notes

This method involves recording detailed notes of the observed behavior, events, and interactions. These notes are usually written in real-time during the observation process.

Audio and Video Recordings

Audio and video recordings can be used to capture the observed behavior and interactions. These recordings can be later analyzed to extract relevant information.

Surveys and Questionnaires

Surveys and questionnaires can be used to gather additional information from the individuals or groups being observed. This method can be used to validate or supplement the observational data.

Time Sampling

This method involves taking a snapshot of the observed behavior at pre-determined time intervals. This method helps to identify the frequency and duration of the observed behavior.

Event Sampling

This method involves recording specific events or behaviors that are of interest to the researcher. This method helps to provide detailed information about specific behaviors or events.

Checklists and Rating Scales

Checklists and rating scales can be used to record the occurrence and frequency of specific behaviors or events. This method helps to simplify and standardize the data collection process.

Observational Data Analysis Methods

Observational Data Analysis Methods are:

Descriptive Statistics

This method involves using statistical techniques such as frequency distributions, means, and standard deviations to summarize the observed behaviors, events, or interactions.

Qualitative Analysis

Qualitative analysis involves identifying patterns and themes in the observed behaviors or interactions. This analysis can be done manually or with the help of software tools.

Content Analysis

Content analysis involves categorizing and counting the occurrences of specific behaviors or events. This analysis can be done manually or with the help of software tools.

Time-series Analysis

Time-series analysis involves analyzing the changes in behavior or interactions over time. This analysis can help identify trends and patterns in the observed data.

Inter-observer Reliability Analysis

Inter-observer reliability analysis involves comparing the observations made by multiple observers to ensure the consistency and reliability of the data.

Multivariate Analysis

Multivariate analysis involves analyzing multiple variables simultaneously to identify the relationships between the observed behaviors, events, or interactions.

Event Coding

This method involves coding observed behaviors or events into specific categories and then analyzing the frequency and duration of each category.

Cluster Analysis

Cluster analysis involves grouping similar behaviors or events into clusters based on their characteristics or patterns.

Latent Class Analysis

Latent class analysis involves identifying subgroups of individuals or groups based on their observed behaviors or interactions.

Social network Analysis

Social network analysis involves mapping the social relationships and interactions between individuals or groups based on their observed behaviors.

The choice of data analysis method depends on the research question, the type of data collected, and the available resources. Researchers should choose the appropriate method that best fits their research question and objectives. It is also important to ensure the validity and reliability of the data analysis by using appropriate statistical tests and measures.

Applications of Observational Research

Observational research is a versatile research method that can be used in a variety of fields to explore and understand human behavior, attitudes, and preferences. Here are some common applications of observational research:

• Psychology : Observational research is commonly used in psychology to study human behavior in natural settings. This can include observing children at play to understand their social development or observing people’s reactions to stress to better understand how stress affects behavior.
• Marketing : Observational research is used in marketing to understand consumer behavior and preferences. This can include observing shoppers in stores to understand how they make purchase decisions or observing how people interact with advertisements to determine their effectiveness.
• Education : Observational research is used in education to study teaching and learning in natural settings. This can include observing classrooms to understand how teachers interact with students or observing students to understand how they learn.
• Anthropology : Observational research is commonly used in anthropology to understand cultural practices and beliefs. This can include observing people’s daily routines to understand their culture or observing rituals and ceremonies to better understand their significance.
• Healthcare : Observational research is used in healthcare to understand patient behavior and preferences. This can include observing patients in hospitals to understand how they interact with healthcare professionals or observing patients with chronic illnesses to better understand their daily routines and needs.
• Sociology : Observational research is used in sociology to understand social interactions and relationships. This can include observing people in public spaces to understand how they interact with others or observing groups to understand how they function.
• Ecology : Observational research is used in ecology to understand the behavior and interactions of animals and plants in their natural habitats. This can include observing animal behavior to understand their social structures or observing plant growth to understand their response to environmental factors.
• Criminology : Observational research is used in criminology to understand criminal behavior and the factors that contribute to it. This can include observing criminal activity in a particular area to identify patterns or observing the behavior of inmates to understand their experience in the criminal justice system.

Observational Research Examples

Here are some real-time observational research examples:

• A researcher observes and records the behaviors of a group of children on a playground to study their social interactions and play patterns.
• A researcher observes the buying behaviors of customers in a retail store to study the impact of store layout and product placement on purchase decisions.
• A researcher observes the behavior of drivers at a busy intersection to study the effectiveness of traffic signs and signals.
• A researcher observes the behavior of patients in a hospital to study the impact of staff communication and interaction on patient satisfaction and recovery.
• A researcher observes the behavior of employees in a workplace to study the impact of the work environment on productivity and job satisfaction.
• A researcher observes the behavior of shoppers in a mall to study the impact of music and lighting on consumer behavior.
• A researcher observes the behavior of animals in their natural habitat to study their social and feeding behaviors.
• A researcher observes the behavior of students in a classroom to study the effectiveness of teaching methods and student engagement.
• A researcher observes the behavior of pedestrians and cyclists on a city street to study the impact of infrastructure and traffic regulations on safety.

How to Conduct Observational Research

Here are some general steps for conducting Observational Research:

• Define the Research Question: Determine the research question and objectives to guide the observational research study. The research question should be specific, clear, and relevant to the area of study.
• Choose the appropriate observational method: Choose the appropriate observational method based on the research question, the type of data required, and the available resources.
• Plan the observation: Plan the observation by selecting the observation location, duration, and sampling technique. Identify the population or sample to be observed and the characteristics to be recorded.
• Train observers: Train the observers on the observational method, data collection tools, and techniques. Ensure that the observers understand the research question and objectives and can accurately record the observed behaviors or events.
• Conduct the observation : Conduct the observation by recording the observed behaviors or events using the data collection tools and techniques. Ensure that the observation is conducted in a consistent and unbiased manner.
• Analyze the data: Analyze the observed data using appropriate data analysis methods such as descriptive statistics, qualitative analysis, or content analysis. Validate the data by checking the inter-observer reliability and conducting statistical tests.
• Interpret the results: Interpret the results by answering the research question and objectives. Identify the patterns, trends, or relationships in the observed data and draw conclusions based on the analysis.
• Report the findings: Report the findings in a clear and concise manner, using appropriate visual aids and tables. Discuss the implications of the results and the limitations of the study.

When to use Observational Research

Here are some situations where observational research can be useful:

• Exploratory Research: Observational research can be used in exploratory studies to gain insights into new phenomena or areas of interest.
• Hypothesis Generation: Observational research can be used to generate hypotheses about the relationships between variables, which can be tested using experimental research.
• Naturalistic Settings: Observational research is useful in naturalistic settings where it is difficult or unethical to manipulate the environment or variables.
• Human Behavior: Observational research is useful in studying human behavior, such as social interactions, decision-making, and communication patterns.
• Animal Behavior: Observational research is useful in studying animal behavior in their natural habitats, such as social and feeding behaviors.
• Longitudinal Studies: Observational research can be used in longitudinal studies to observe changes in behavior over time.
• Ethical Considerations: Observational research can be used in situations where manipulating the environment or variables would be unethical or impractical.

Purpose of Observational Research

Observational research is a method of collecting and analyzing data by observing individuals or phenomena in their natural settings, without manipulating them in any way. The purpose of observational research is to gain insights into human behavior, attitudes, and preferences, as well as to identify patterns, trends, and relationships that may exist between variables.

The primary purpose of observational research is to generate hypotheses that can be tested through more rigorous experimental methods. By observing behavior and identifying patterns, researchers can develop a better understanding of the factors that influence human behavior, and use this knowledge to design experiments that test specific hypotheses.

Observational research is also used to generate descriptive data about a population or phenomenon. For example, an observational study of shoppers in a grocery store might reveal that women are more likely than men to buy organic produce. This type of information can be useful for marketers or policy-makers who want to understand consumer preferences and behavior.

In addition, observational research can be used to monitor changes over time. By observing behavior at different points in time, researchers can identify trends and changes that may be indicative of broader social or cultural shifts.

Overall, the purpose of observational research is to provide insights into human behavior and to generate hypotheses that can be tested through further research.

Advantages of Observational Research

There are several advantages to using observational research in different fields, including:

• Naturalistic observation: Observational research allows researchers to observe behavior in a naturalistic setting, which means that people are observed in their natural environment without the constraints of a laboratory. This helps to ensure that the behavior observed is more representative of the real-world situation.
• Unobtrusive : Observational research is often unobtrusive, which means that the researcher does not interfere with the behavior being observed. This can reduce the likelihood of the research being affected by the observer’s presence or the Hawthorne effect, where people modify their behavior when they know they are being observed.
• Cost-effective : Observational research can be less expensive than other research methods, such as experiments or surveys. Researchers do not need to recruit participants or pay for expensive equipment, making it a more cost-effective research method.
• Flexibility: Observational research is a flexible research method that can be used in a variety of settings and for a range of research questions. Observational research can be used to generate hypotheses, to collect data on behavior, or to monitor changes over time.
• Rich data : Observational research provides rich data that can be analyzed to identify patterns and relationships between variables. It can also provide context for behaviors, helping to explain why people behave in a certain way.
• Validity : Observational research can provide high levels of validity, meaning that the results accurately reflect the behavior being studied. This is because the behavior is being observed in a natural setting without interference from the researcher.

Disadvantages of Observational Research

While observational research has many advantages, it also has some limitations and disadvantages. Here are some of the disadvantages of observational research:

• Observer bias: Observational research is prone to observer bias, which is when the observer’s own beliefs and assumptions affect the way they interpret and record behavior. This can lead to inaccurate or unreliable data.
• Limited generalizability: The behavior observed in a specific setting may not be representative of the behavior in other settings. This can limit the generalizability of the findings from observational research.
• Difficulty in establishing causality: Observational research is often correlational, which means that it identifies relationships between variables but does not establish causality. This can make it difficult to determine if a particular behavior is causing an outcome or if the relationship is due to other factors.
• Ethical concerns: Observational research can raise ethical concerns if the participants being observed are unaware that they are being observed or if the observations invade their privacy.
• Time-consuming: Observational research can be time-consuming, especially if the behavior being observed is infrequent or occurs over a long period of time. This can make it difficult to collect enough data to draw valid conclusions.
• Difficulty in measuring internal processes: Observational research may not be effective in measuring internal processes, such as thoughts, feelings, and attitudes. This can limit the ability to understand the reasons behind behavior.

About the author

Muhammad Hassan

Researcher, Academic Writer, Web developer

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What Are Observational Studies?

Observational studies are research studies in which researchers collect information from participants or look at data that was already collected. 

In observational studies, researchers follow groups of people over a period of time. Depending on the study, groups may include healthy people, people with cancer, or people who are at high risk for developing cancer, such as those with a family history.  

How observational studies help cancer research

Observational studies can help researchers learn more about cancer and suggest paths for future research that may lead to insights such as:

• how specific cancers form, grow, and spread
• genes that cause cancer to develop at a high rate within certain groups
• exposures or behaviors that may increase the risk of cancer
• clues to help prevent cancer
• clues that lead to new treatments
• patterns and trends of new cancer cases 
• the experiences of people who have had cancer in the past

Types of observational studies 

There are different types of observational studies. Two examples include natural history and longitudinal studies.

Natural history studies look at certain conditions in people with cancer or people who are at a high risk of developing cancer. Researchers often collect information about a person’s and their family’s medical history, as well as blood, saliva, and tumor samples that may be studied to learn more about how cancer develops or how it responds to treatment.

Longitudinal studies gather data on people over time, often to see whether those with different exposures have different cancer outcomes. Examples include those with different kinds of diets, smoking history, or other traits.

What to expect

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What you do as part of an observational study depends on the study. In some studies, you may be asked to fill out surveys or questionnaires. Researchers may ask about your medical history and that of your family. They might ask you to provide tissue samples, such as blood, saliva, or—if you have cancer—your tumor. Some studies may require your medical records. For some studies you might be asked to come in for an in-person visit with the research team.

Depending on the study, you might provide samples and information just once, or many times over the course of the study. 

Possible risks and benefits

There are few risks to taking part in an observational research study. One possible risk is the accidental release of information from your health records. To prevent this from happening, there are security measures in place to protect your privacy. A benefit of taking part in an observational study is knowing that you will help doctors learn more about cancer. These studies help create a foundation that can lead to further research that may help people with cancer in the future. Or they might help people in the future avoid cancer.

Your rights 

Before you join a study, the research team will make sure you understand:

• why the study is being done
• what will happen during the study
• how it may affect your daily life

Once you understand the study and decide to take part, you will be asked to sign a consent form. But even after you sign the form, you can change your mind and leave the study at any time.

Costs and expenses

Most observational studies are free to those who take part. As you think about joining an observational study, be sure to ask the study team about costs.

Observational Studies

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Department of Statistics, The Wharton School, University of Pennsylvania, Philadelphia, USA

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Two Simple Models for Observational Studies

Opportunities, devices, and instruments, causal inference in randomized experiments.

• Sensitivity
• Treatment Effects

Table of contents (12 chapters)

Front matter.

Paul R. Rosenbaum

Randomized Experiments

Overt bias in observational studies, sensitivity to hidden bias, models for treatment effects, known effects, multiple reference groups in case-referent studies, multiple control groups, coherence and focused hypotheses, constructing matched sets and strata, planning an observational study, some strategic issues, back matter, authors and affiliations, bibliographic information.

Book Title : Observational Studies

Authors : Paul R. Rosenbaum

Series Title : Springer Series in Statistics

DOI : https://doi.org/10.1007/978-1-4757-3692-2

Publisher : Springer New York, NY

eBook Packages : Springer Book Archive

Copyright Information : Springer Science+Business Media New York 2002

Hardcover ISBN : 978-0-387-98967-9 Published: 08 January 2002

Softcover ISBN : 978-1-4419-3191-7 Published: 01 December 2010

eBook ISBN : 978-1-4757-3692-2 Published: 17 April 2013

Series ISSN : 0172-7397

Series E-ISSN : 2197-568X

Edition Number : 2

Number of Pages : XIV, 377

Number of Illustrations : 6 b/w illustrations

Topics : Statistical Theory and Methods , Statistics for Life Sciences, Medicine, Health Sciences , Statistics for Social Sciences, Humanities, Law , Statistics for Business, Management, Economics, Finance, Insurance

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10 Observational Research Examples

Dave Cornell (PhD)

Dr. Cornell has worked in education for more than 20 years. His work has involved designing teacher certification for Trinity College in London and in-service training for state governments in the United States. He has trained kindergarten teachers in 8 countries and helped businessmen and women open baby centers and kindergartens in 3 countries.

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Chris Drew (PhD)

This article was peer-reviewed and edited by Chris Drew (PhD). The review process on Helpful Professor involves having a PhD level expert fact check, edit, and contribute to articles. Reviewers ensure all content reflects expert academic consensus and is backed up with reference to academic studies. Dr. Drew has published over 20 academic articles in scholarly journals. He is the former editor of the Journal of Learning Development in Higher Education and holds a PhD in Education from ACU.

Observational research involves observing the actions of people or animals, usually in their natural environments.

For example, Jane Goodall famously observed chimpanzees in the wild and reported on their group behaviors. Similarly, many educational researchers will conduct observations in classrooms to gain insights into how children learn.

Examples of Observational Research

1. jane goodall’s research.

Jane Goodall is famous for her discovery that chimpanzees use tools. It is one of the most remarkable findings in psychology and anthropology .

Her primary method of study involved simply entering the natural habitat of her research subjects, sitting down with pencil and paper, and making detailed notes of what she observed.

Those observations were later organized and transformed into research papers that provided the world with amazing insights into animal behavior.

When she first discovered that chimpanzees use twigs to “fish” for termites, it was absolutely stunning. The renowned Louis Leakey proclaimed: “we must now redefine tool, redefine man, or accept chimps as humans.”

2. Linguistic Development of Children

Answering a question like, “how do children learn to speak,” can only be answered by observing young children at home.

By the time kids get to first grade, their language skills have already become well-developed, with a vocabulary of thousands of words and the ability to use relatively complex sentences.

Therefore, a researcher has to conduct their study in the child’s home environment. This typically involves having a trained data collector sit in a corner of a room and take detailed notes about what and how parents speak to their child.

Those observations are later classified in a way that they can be converted into quantifiable measures for statistical analysis.

For example, the data might be coded in terms of how many words the parents spoke, degree of sentence complexity, or emotional dynamic of being encouraging or critical. When the data is analyzed, it might reveal how patterns of parental comments are linked to the child’s level of linguistic development.

Related Article: 15 Action Research Examples

3. Consumer Product Design  

Before Apple releases a new product to the market, they conduct extensive analyses of how the product will be perceived and used by consumers.

The company wants to know what kind of experience the consumer will have when using the product. Is the interface user-friendly and smooth? Does it fit comfortably in a person’s hand?

Is the overall experience pleasant?

So, the company will arrange for groups of prospective customers come to the lab and simply use the next iteration of one of their great products. That lab will absolutely contain a two-way mirror and a team of trained observers sitting behind it, taking detailed notes of what the test groups are doing. The groups might even be video recorded so their behavior can be observed again and again.

That will be followed by a focus group discussion , maybe a survey or two, and possibly some one-on-one interviews.  

4. Satellite Images of Walmart

Observational research can even make some people millions of dollars. For example, a report by NPR describes how stock market analysts observe Walmart parking lots to predict the company’s earnings.

The analysts purchase satellite images of selected parking lots across the country, maybe even worldwide. That data is combined with what they know about customer purchasing habits, broken down by time of day and geographic region.

Over time, a detailed set of calculations are performed that allows the analysts to predict the company’s earnings with a remarkable degree of accuracy .

This kind of observational research can result in substantial profits.

5. Spying on Farms

Similar to the example above, observational research can also be implemented to study agriculture and farming.

By using infrared imaging software from satellites, some companies can observe crops across the globe. The images provide measures of chlorophyll absorption and moisture content, which can then be used to predict yields. Those images also allow analysts to simply count the number of acres being planted for specific crops across the globe.

In commodities such as wheat and corn, that prediction can lead to huge profits in the futures markets.

It’s an interesting application of observational research with serious monetary implications.

6. Decision-making Group Dynamics  

When large corporations make big decisions, it can have serious consequences to the company’s profitability, or even survival.

Therefore, having a deep understanding of decision-making processes is essential. Although most of us think that we are quite rational in how we process information and formulate a solution, as it turns out, that’s not entirely true.

Decades of psychological research has focused on the function of statements that people make to each other during meetings. For example, there are task-masters, harmonizers, jokers, and others that are not involved at all.

A typical study involves having professional, trained observers watch a meeting transpire, either from a two-way mirror, by sitting-in on the meeting at the side, or observing through CCTV.

By tracking who says what to whom, and the type of statements being made, researchers can identify weaknesses and inefficiencies in how a particular group engages the decision-making process.

See More: Decision-Making Examples

7. Case Studies

A case study is an in-depth examination of one particular person. It is a form of observational research that involves the researcher spending a great deal of time with a single individual to gain a very detailed understanding of their behavior.

The researcher may take extensive notes, conduct interviews with the individual, or take video recordings of behavior for further study.

Case studies give a level of detailed information that is not available when studying large groups of people. That level of detail can often provide insights into a phenomenon that could lead to the development of a new theory or help a researcher identify new areas of research.

Researchers sometimes have no choice but to conduct a case study in situations in which the phenomenon under study is “rare and unusual” (Lee & Saunders, 2017). Because the condition is so uncommon, it is impossible to find a large enough sample of cases to study with quantitative methods.

Go Deeper: Pros and Cons of Case Study Research

8. Infant Attachment

One of the first studies on infant attachment utilized an observational research methodology . Mary Ainsworth went to Uganda in 1954 to study maternal practices and mother/infant bonding.  

Ainsworth visited the homes of 26 families on a bi-monthly basis for 2 years, taking detailed notes and interviewing the mothers regarding their parenting practices.

Her notes were then turned into academic papers and formed the basis for the Strange Situations test that she developed for the laboratory setting.

The Strange Situations test consists of 8 situations, each one lasting no more than a few minutes. Trained observers are stationed behind a two-way mirror and have been trained to make systematic observations of the baby’s actions in each situation.

9. Ethnographic Research  

Ethnography is a type of observational research where the researcher becomes part of a particular group or society.

The researcher’s role as data collector is hidden and they attempt to immerse themselves in the community as a regular member of the group.

By being a part of the group and keeping one’s purpose hidden, the researcher can observe the natural behavior of the members up-close. The group will behave as they would naturally and treat the researcher as if they were just another member. This can lead to insights into the group dynamics , beliefs, customs and rituals that could never be studied otherwise.

10. Time and Motion Studies

Time and motion studies involve observing work processes in the work environment. The goal is to make procedures more efficient, which can involve reducing the number of movements needed to complete a task.

Reducing the movements necessary to complete a task increases efficiency, and therefore improves productivity. A time and motion study can also identify safety issues that may cause harm to workers, and thereby help create a safer work environment.

The two most famous early pioneers of this type of observational research are Frank and Lillian Gilbreth.  

Lilian was a psychologist that began to study the bricklayers of her husband Frank’s construction company. Together, they figured out a way to reduce the number of movements needed to lay bricks from 18 to 4 (see original video footage here ).

The couple became quite famous for their work during the industrial revolution and

Lillian became the only psychologist to appear on a postage stamp (in 1884).

Why do Observational Research?

Psychologists and anthropologists employ this methodology because:

• Psychologists find that studying people in a laboratory setting is very artificial. People often change their behavior if they know it is going to be analyzed by a psychologist later.
• Anthropologists often study unique cultures and indigenous peoples that have little contact with modern society. They often live in remote regions of the world, so, observing their behavior in a natural setting may be the only option.
• In animal studies , there are lots of interesting phenomenon that simply cannot be observed in a laboratory, such as foraging behavior or mate selection. Therefore, observational research is the best and only option available.

Read Also: Difference Between Observation and Inference

Observational research is an incredibly useful way to collect data on a phenomenon that simply can’t be observed in a lab setting. This can provide insights into human behavior that could never be revealed in an experiment (see: experimental vs observational research ).

Researchers employ observational research methodologies when they travel to remote regions of the world to study indigenous people, try to understand how parental interactions affect a child’s language development, or how animals survive in their natural habitats.

On the business side, observational research is used to understand how products are perceived by customers, how groups make important decisions that affect profits, or make economic predictions that can lead to huge monetary gains.

Ainsworth, M. D. S. (1967). Infancy in Uganda . Baltimore: Johns Hopkins University Press.

Ainsworth, M. D. S., Blehar, M., Waters, E., & Wall, S. (1978). Patterns of attachment: A

psychological study of the Strange Situation. Hillsdale: Erlbaum.

Crowe, S., Cresswell, K., Robertson, A., Huby, G., Avery, A., & Sheikh, A. (2011). The case study approach. BMC Medical Research Methodology , 11 , 100. https://doi.org/10.1186/1471-2288-11-100

d’Apice, K., Latham, R., & Stumm, S. (2019). A naturalistic home observational approach to children’s language, cognition, and behavior. Developmental Psychology, 55 (7),1414-1427. https://doi.org/10.1037/dev0000733

Lee, B., & Saunders, M. N. K. (2017).  Conducting Case Study Research for Business and Management Students.  SAGE Publications.

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Observational Research: What is, Types, Pros & Cons + Example

Researchers can gather customer data in a variety of ways, including surveys, interviews, and research. But not all data can be collected by asking questions because customers might not be conscious of their behaviors. 

It is when observational research comes in. This research is a way to learn about people by observing them in their natural environment. This kind of research helps researchers figure out how people act in different situations and what things in the environment affect their actions.

This blog will teach you about observational research, including types and observation methods. Let’s get started.

What is observational research?

Observational research is a broad term for various non-experimental studies in which behavior is carefully watched and recorded.

The goal of this research is to describe a variable or a set of variables. More broadly, the goal is to capture specific individual, group, or setting characteristics.

Since it is non-experimental and uncontrolled, we cannot draw causal research conclusions from it. The observational data collected in research studies is frequently qualitative observation , but it can also be quantitative or both (mixed methods).

Types of observational research

Conducting observational research can take many different forms. There are various types of this research. These types are classified below according to how much a researcher interferes with or controls the environment.

Naturalistic observation

Taking notes on what is seen is the simplest form of observational research. A researcher makes no interference in naturalistic observation. It’s just watching how people act in their natural environments. 

Importantly, there is no attempt to modify factors in naturalistic observation, as there would be when comparing data between a control group and an experimental group.

Case studiesCase studies

A case study is a sort of observational research that focuses on a single phenomenon. It is a naturalistic observation because it captures data in the field. But case studies focus on a specific point of reference, like a person or event, while other studies may have a wider scope and try to record everything that happens in the researcher’s eyes. 

For example, a case study of a single businessman might try to find out how that person deals with a certain disease’s ups and down or loss.

Participant observation

Participant observation is similar to naturalistic observation, except that the researcher is a part of the natural environment they are studying. In such research, the researcher is also interested in rituals or cultural practices that can only be evaluated by sharing experiences. 

For example, anyone can learn the basic rules of table Tennis by going to a game or following a team. Participant observation, on the other hand, lets people take part directly to learn more about how the team works and how the players relate to each other.

It usually includes the researcher joining a group to watch behavior they couldn’t see from afar. Participant observation can gather much information, from the interactions with the people being observed to the researchers’ thoughts.

Controlled observation

A more systematic structured observation entails recording the behaviors of research participants in a remote place. Case-control studies are more like experiments than other types of research, but they still use observational research methods. When researchers want to find out what caused a certain event, they might use a case-control study.

Longitudinal observation

This observational research is one of the most difficult and time-consuming because it requires watching people or events for a long time. Researchers should consider longitudinal observations when their research involves variables that can only be seen over time. 

After all, you can’t get a complete picture of things like learning to read or losing weight in a single observation. Longitudinal studies keep an eye on the same people or events over a long period of time and look for changes or patterns in behavior.

Observational research methods

When doing this research, there are a few observational methods to remember to ensure that the research is done correctly. Along with other research methods, let’s learn some key research methods of it:

Have a clear objective

For an observational study to be helpful, it needs to have a clear goal. It will help guide the observations and ensure they focus on the right things.

Get permission

Get permission from your participants. Getting explicit permission from the people you will be watching is essential. It means letting them know that they will be watched, the observation’s goal, and how their data will be used.

Unbiased observation

It is important to make sure the observations are fair and unbiased. It can be done by keeping detailed notes of what is seen and not putting any personal meaning on the data.

Hide your observers

In the observation method, keep your observers hidden. The participants should be unaware of the observers to avoid potential bias in their actions.

Documentation

It is important to document the observations clearly and straightforwardly. It will allow others to examine the information and confirm the observational research findings.

Data analysis

Data analysis is the last method. The researcher will analyze the collected data to draw conclusions or confirm a hypothesis.

Pros and cons of observational research

Observational studies are a great way to learn more about how your customers use different parts of your business. There are so many pros and cons of observational research. Let’s have a look at them.

• It provides a practical application for a hypothesis. In other words, it can help make research more complete.
• You can see people acting alone or in groups, such as customers. So, you can answer a number of questions about how people act as customers.
• There is a chance of researcher bias in observational research. Experts say that this can be a very big problem.
• Some human activities and behaviors can be difficult to understand. We are unable to see memories or attitudes. In other words, there are numerous situations in which observation alone is inadequate.

Example of observational research

The researcher observes customers buying products in a mall. Assuming the product is soap, the researcher will observe how long the customer takes to decide whether he likes the packaging or comes to the mall with his decision already made based on advertisements.

If the customer takes their time making a decision, the researcher will conclude that packaging and information on the package affect purchase behavior. If a customer makes a quick decision, the decision is likely predetermined. 

As a result, the researcher will recommend more and better advertisements in this case. All of these findings were obtained through simple observational research.

How to conduct observational research with QuestionPro?

QuestionPro can help with observational research by providing tools to collect and analyze data. It can help in the following ways:

Define the research goals and question types you want to answer with your observational study . Use QuestionPro’s customizable survey templates and questions to do a survey that fits your research goals and gets the necessary information. 

You can distribute the survey to your target audience using QuestionPro’s online platform or by sending a link to the survey. 

With QuestionPro’s real-time data analysis and reporting features, you can collect and look at the data as people fill out the survey. Use the advanced analytics tools in QuestionPro to see and understand the data and find insights and trends. 

If you need to, you can export the data from QuestionPro into the analysis tools you like to use. Draw conclusions from the collected and analyzed data and answer the research questions that were asked at the beginning of the research.

For a deeper understanding of human behaviors and decision-making processes, explore the realm of Behavioral Research .

To summarize, observational research is an effective strategy for collecting data and getting insights into real-world phenomena. When done right, this research can give helpful information and help people make decisions. 

QuestionPro is a valuable tool that can help with observational research by letting you create online surveys, analyze data in real time, make surveys your own, keep your data safe, and use advanced analytics tools.

To do this research with QuestionPro, researchers need to define their research goals, do a survey that matches their goals, send the survey to participants, collect and analyze the data, visualize and explain the results, export data if needed, and draw conclusions from the data collected.

By keeping in mind what has been said above, researchers can use QuestionPro to help with their observational research and gain valuable data. Try out QuestionPro today!

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Frequently Asked Questions (FAQ)

Observational research is a method in which researchers observe and systematically record behaviors, events, or phenomena without directly manipulating them.

There are three main types of observational research: naturalistic observation, participant observation, and structured observation.

Naturalistic observation involves observing subjects in their natural environment without any interference.

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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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A complete negative history of glucagon-like peptide 1 receptor agonist (GLP-1 RA) dispensing was required before the index date. Other exclusions include missing sex and no follow-up time. GLP-1 RA refers to both GLP-1 RA and GLP-1 RA/gastric inhibitory polypeptide agonist medications.

a Index event: first GLP-1 dispensed between May 2022 to September 2023.

The y-axis represents the event probability (1-survival probability [eg, probability of being event-free]).

Bars represent mean changes in body weight from baseline to the time point among the propensity score matched population of patients still receiving treatment. The whiskers represent 95% CIs.

Points represent point estimates; the whiskers represent 95% CIs. Panels A-C contain hazard ratios for achieving 5% or greater, 10% or greater, and 15% or greater weight loss for patients receiving tirzepatide vs semaglutide among propensity score matched populations. Hazard ratios greater than 1 indicate higher likelihood of reaching weight loss threshold with tirzepatide. Panels D-F contain absolute differences in body weight change at 3 months, 6 months, and 12 months for patients receiving tirzepatide vs semaglutide among propensity score matched populations still receiving treatment at the time point. Negative differences indicate greater weight loss with tirzepatide.

eDefinitions

eFigure 1. Distribution of initiation time by group

eFigure 2. Distribution of follow-up time by initiation date

eTable 1. Weight Availability at t: On-Treatment Analyses

eTable 2. Weight Availability at t: ITT Analyses

eTable 3. Characteristics of Matched Patients with Available vs. Missing Follow-up Weight.

eFigure 3. Proportion of at-risk patients achieving weight loss targets by one year for on treatment and intention to treat analyses

eFigure 4. Hazard ratio comparing tirzepatide vs semaglutide for achieving weight loss targets under different analytic approaches

eFigure 5. Mean change in body weight for tirzepatide and semaglutide groups under on treatment and modified intention to treat analyses

eFigure 6. Difference in percent change in body weight comparing tirzepatide to semaglutide under different analytic approaches

eFigure 7. Event probabilities of weight loss, accounting for censoring, for patients on liraglutide and semaglutide

eFigure 8. Hazard ratios comparing liraglutide to semaglutide

eFigure 9. Mean change in body weight for liraglutide and semaglutide

eFigure 10. Difference in percent change in body weight comparing liraglutide to semaglutide

eFigure 11. Weight loss event probabilities, accounting for censoring, for patients with T2D

eFigure 12. Weight loss event probabilities, accounting for censoring, for patients without T2D

eFigure 13. Hazard ratio comparing tirzepatide to semaglutide for different populations

eFigure 14. Difference in percent change in body weight comparing tirzepatide to semaglutide for different populations

eTable 4. Gastrointestinal Adverse Event Rates Per 1000 person-years

Data Sharing Statement

• Effect of Tirzepatide in Chinese Adults With Obesity JAMA Original Investigation May 31, 2024 This randomized clinical trial investigates the safety and efficacy of treatment with once-weekly tirzepatide for weight reduction in Chinese adults with overweight or obesity without diabetes over a 52-week period. Lin Zhao, MD; Zhifeng Cheng, MD; Yibing Lu, MD; Ming Liu, MD; Hong Chen, MD; Min Zhang, MD; Rui Wang, MD; Yuan Yuan, PhD; Xiaoying Li, MD

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Rodriguez PJ , Goodwin Cartwright BM , Gratzl S, et al. Semaglutide vs Tirzepatide for Weight Loss in Adults With Overweight or Obesity. JAMA Intern Med. Published online July 08, 2024. doi:10.1001/jamainternmed.2024.2525

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Semaglutide vs Tirzepatide for Weight Loss in Adults With Overweight or Obesity

• 1 Truveta Inc, Bellevue, Washington
• 2 Center for Cardiovascular Analytics, Research and Data Science (CARDS), Providence Heart Institute, Providence Health System, Portland, Oregon
• Original Investigation Effect of Tirzepatide in Chinese Adults With Obesity Lin Zhao, MD; Zhifeng Cheng, MD; Yibing Lu, MD; Ming Liu, MD; Hong Chen, MD; Min Zhang, MD; Rui Wang, MD; Yuan Yuan, PhD; Xiaoying Li, MD JAMA

Question   How does weight loss differ between patients receiving tirzepatide compared with semaglutide among a clinical population of adults with overweight or obesity?

Findings   In this cohort study of 18 386 propensity-score matched patients initiating tirzepatide or semaglutide labeled for type 2 diabetes, discontinuation was common; most achieved weight loss of 5% or greater within 1 year of treatment.

Meaning   Although most adults with overweight or obesity experienced 5% or greater weight loss with treatment, the benefit was greater with tirzepatide.

Importance   Although tirzepatide and semaglutide were shown to reduce weight in randomized clinical trials, data from head-to-head comparisons in populations with overweight or obesity are not yet available.

Objective   To compare on-treatment weight loss and rates of gastrointestinal adverse events (AEs) among adults with overweight or obesity receiving tirzepatide or semaglutide labeled for type 2 diabetes (T2D) in a clinical setting.

Design, Setting, and Participants   In this cohort study, adults with overweight or obesity receiving semaglutide or tirzepatide between May 2022 and September 2023 were identified using electronic health record (EHR) data linked to dispensing information from a collective of US health care systems. On-treatment weight outcomes through November 3, 2023, were assessed. Adults with overweight or obesity and regular care in the year before initiation, no prior glucagon-like peptide 1 receptor agonist receptor agonist use, a prescription within 60 days prior to initiation, and an available baseline weight were identified. The analysis was completed on April 3, 2024.

Exposures   Tirzepatide or semaglutide in formulations labeled for T2D, on or off label.

Main Outcomes and Measures   On-treatment weight change in a propensity score–matched population, assessed as hazard of achieving 5% or greater, 10% or greater, and 15% or greater weight loss, and percentage change in weight at 3, 6, and 12 months. Hazards of gastrointestinal AEs were compared.

Results   Among 41 222 adults meeting the study criteria (semaglutide, 32 029; tirzepatide, 9193), 18 386 remained after propensity score matching. The mean (SD) age was 52.0 (12.9) years, 12 970 were female (70.5%), 14 182 were white (77.1%), 2171 Black (11.8%), 354 Asian (1.9%), 1679 were of other or unknown race, and 9563 (52.0%) had T2D. The mean (SD) baseline weight was 110 (25.8) kg. Follow-up was ended by discontinuation for 5140 patients (55.9%) receiving tirzepatide and 4823 (52.5%) receiving semaglutide. Patients receiving tirzepatide were significantly more likely to achieve weight loss (≥5%; hazard ratio [HR], 1.76, 95% CI, 1.68, 1.84; ≥10%; HR, 2.54; 95% CI, 2.37, 2.73; and ≥15%; HR, 3.24; 95% CI, 2.91, 3.61). On-treatment changes in weight were larger for patients receiving tirzepatide at 3 months (difference, −2.4%; 95% CI −2.5% to −2.2%), 6 months (difference, −4.3%; 95% CI, −4.7% to −4.0%), and 12 months (difference, −6.9%; 95% CI, −7.9% to −5.8%). Rates of gastrointestinal AEs were similar between groups.

Conclusions and Relevance   In this population of adults with overweight or obesity, use of tirzepatide was associated with significantly greater weight loss than semaglutide. Future study is needed to understand differences in other important outcomes.

Overweight and obesity are highly prevalent conditions associated with increased morbidity and mortality. 1 - 3 Historically, pharmacologic treatments for weight reduction (antiobesity medications [AOMs]) have been limited in number, not particularly well-tolerated, and modest in impacts on weight. 4 , 5 However, newer therapies, including the glucagon-like peptide 1 receptor agonist (GLP-1 RA) semaglutide and the dual GLP-1 RA/gastric inhibitory polypeptide (GIP) agonist tirzepatide, have demonstrated substantial weight reduction in patients with obesity, with and without type 2 diabetes (T2D), in randomized clinical trials (RCTs). 6 - 10

While tirzepatide produces greater weight loss than semaglutide in patients with T2D, 11 data from head-to-head trials comparing these therapies in patients with overweight or obesity are not yet available. Further, it remains unclear whether the magnitude of weight loss in clinical settings mirrors that in RCTs, given well-described differences between these populations. 12 - 14 Finally, because these medications are costly and insurance coverage is limited for patients without T2D, actual adherence may differ from clinical trials, potentially attenuating the treatment effect.

Accordingly, we aimed to compare on-treatment weight change between tirzepatide and semaglutide (injectable) labeled for T2D in a large clinical population. We quantified differences in (1) likelihood of achieving 5% or greater, 10% or greater, and 15% or greater weight loss, and (2) percentage change in body weight at 3, 6, and 12 months with treatment.

New users of tirzepatide or semaglutide with overweight or obesity (regardless of T2D) were included in the study. The first dispensation of tirzepatide or semaglutide was considered the treatment initiation date and served as the study index date. New users were defined as those having no previous dispensation of any GLP-1 RA or GLP-1 RA/GIP agonist (henceforth referred to as GLP-1 RA for brevity). Only adult patients with regular interactions with the health care system and an available baseline weight were included (see Study Population below). Patients were followed up for weight loss and gastrointestinal adverse events (AEs) until the first of discontinuation of therapy, GLP-1 RA switching, administrative censoring, or study end (November 3, 2023).

This study used a subset of Truveta data. Truveta provides access to continuously updated and linked electronic health record (EHR) from a collective of US health care systems, including structured information on demographics (age, sex, health system–reported race and ethnicity), encounters, diagnoses, vital signs (eg, weight, body mass index [BMI, calculated as weight in kilograms divided by height in meters squared], blood pressure), medication requests (prescriptions), laboratory and diagnostic tests and results (eg, hemoglobin A1c [HbA 1c ] tests and values), and procedures. In addition to EHR data for care delivered within Truveta constituent health care systems, medication dispensing and social drivers of health (SDOH) information are made available through linked third-party data. Medication dispense (via e-prescribing data) includes fills for prescriptions written both within and outside constituent health care systems, providing greater observability into patients’ medication history. Medication dispense histories are updated at encounters, and include fill dates, NDC or RxNorm codes, quantity dispensed, and days of medication supplied. SDOH data include individual income and education.

Data are normalized into a common data model through syntactic and semantic normalization. Truveta data are then deidentified by expert determination under the Health Insurance Portability and Accountability Act Privacy Rule and therefore exempt from institutional review board approval. Data for this study were accessed on November 3, 2023, using Truveta Studio.

This retrospective observational cohort study followed the Strengthening the Reporting of Observational Studies in Epidemiology ( STROBE ) reporting guidelines. 15 The analysis was completed on April 3, 2024.

We identified adults first dispensed tirzepatide or semaglutide labeled for T2D (as brand names Mounjaro [Eli Lilly] or Ozempic [Novo Nordisk], respectively) between May 1, 2022 (the month of tirzepatide approval) and September 30, 2023, and who had overweight (BMI ≥27 or a diagnosis code indicating BMI ≥27) or obesity (BMI ≥30 or a diagnosis code for obesity) in the year before their index date. An overweight threshold BMI of 27 or greater was used to mirror clinical trials in patients with overweight or obesity. 6 , 7 , 9 , 10 We required a complete negative history of GLP-1 RA use. To improve outcome observability, we limited our analysis to patients with regular interactions with the health care system during the year prior to their index date, defined as at least 1 encounter, observation, or medication request in each consecutive 6-month period preceding the index date. We required a GLP-1 RA prescription and a baseline weight measurement in the 60 days before the index date. A 60-day window was selected because insurance denials and appeal processes for these medications may result in unusually long times between medication prescribing and filling. Of note, the GLP-1 RA prescribed was not required to match the medication first dispensed, given that drug shortages during the study period 16 , 17 may have resulted in substitutions. Patients were categorized according to the medication dispensed. Additional exclusions were made for patients with missing sex and those with no follow-up time. The number of patients meeting the inclusion criteria determined the sample size. Codes for all definitions used in this study are provided in Supplement 1 (eDefinitions).

We relied on brand as a proxy for target dose. The standard full dose is 0.5 mg for semaglutide labeled for T2D and 5.0 mg for tirzepatide (labeled exclusively for T2D at the time of this analysis). The standard dose escalation schedule for both drugs is 4 weeks.

Patients were classified as having T2D if they had a T2D diagnosis, were prescribed, administered, or dispensed insulin or a dipeptidyl peptidase 4 (DPP-4) inhibitor, or had an HbA 1c level of 7.5% or greater in the 2 years before their index date. Baseline patient demographics, clinical comorbidities, use of other antidiabetic medication (ADM) and AOM, and history of bariatric surgery in the 2 years before the index date were assessed. Several steps were taken to standardize weight data, including the removal of apparent data entry or unit conversion errors (detailed in eMethods 1.1 in Supplement 2 ). The most recent weight within the 60 days before the index date was considered the baseline value.

Our primary estimand of interest was on-treatment weight loss. Therefore, patients were censored at the first of either treatment discontinuation (≥30 days without medication on hand), GLP-1 RA switching (change to a different medication; brand changes were allowed), last encounter, or study end (November 3, 2023). Analyses assumed unobserved weights for at-risk patients were missing at random, and therefore conditional on observed information only. Although relationships with unobserved variables cannot be tested, we assessed characteristics of patients with vs without any follow-up weight.

Propensity scores were used to balance treatment groups on measured variables. Propensity scores estimated the probability of initiating tirzepatide, compared to semaglutide, as a function of demographic, clinical, and utilization characteristics (eMethods 1.2 in Supplement 2 ). Patients were then matched using 1:1 nearest neighbor propensity score (PS) matching. Balance was assessed by standardized mean differences, with an acceptable threshold of 0.1. To provide further control for residual confounding, age, presence of T2D (eg, on-label use), and baseline weight were included as covariates in all parametric and semiparametric models.

Percentage change in body weight was calculated as (follow-up weight − baseline weight)/baseline weight. Probabilities of achieving 5% or greater, 10% or greater, and 15% or greater weight loss within 1 year, accounting for censoring, were extracted from Kaplan-Meier models. Relative differences in the hazard of achieving 5% or greater, 10% or greater, and 15% or greater weight loss for those receiving tirzepatide compared with semaglutide were estimated using Cox proportional hazards models with a robust variance estimator. 18 Survival methods were used to accommodate censoring rates in this clinical dataset.

For weight change at 3, 6, and 12 months, only the subpopulation still at risk (not yet censored) at the time point of interest was evaluated. The weight value nearest to the time point, within 45 days, was considered the outcome value. For at-risk patients without a weight value in this window, multiple imputation was used to impute weight change using information on all measured covariates and outcomes from the full at-risk population. Within each ( m  = 10) imputed dataset of at-risk patients at the time point of interest, propensity score matching was reapplied, and differences in percentages of weight loss were estimated using linear models. Estimates were then pooled across imputations using Rubin rules. 19 Details on missingness and imputation are provided in eMethods 1.3 to 1.5 in Supplement 2 (eTable 1, eTable 2, eTable 3 in Supplement 2 ).

Several sensitivity analyses were performed to test the robustness of findings. First, we replicated all analyses using inverse probability of treatment weighting (IPTW), rather than propensity score matching. Second, we conducted stratified analyses for patients with and without T2D (eg, on-label vs off-label use), replicating the full process described for each stratum. Third, we conducted a modified intention-to-treat (ITT) analysis, where censoring time ignored discontinuation and switching. This analysis included all available follow-up weights regardless of whether the patient was receiving treatment. Finally, analyses were replicated excluding patients with missing weight values (complete case analysis). We also conducted a sensitivity analysis comparing liraglutide to semaglutide as validation.

Moderate to severe gastrointestinal AE (bowel obstruction, cholecystitis, cholelithiasis, gastroenteritis, gastroparesis, and pancreatitis) were identified from EHR data. Mild AEs, such as nausea and vomiting, were not included given the expectation of inconsistent capture in EHR data. The incidence rate of each gastrointestinal AE per 1000 person-years at risk was calculated, using the previously described censoring approach. Patients with a history of the specific AE in the year before index were excluded from analyses of the specific AE. Differences in the hazard of each AE between tirzepatide and semaglutide were estimated using Cox proportional hazards models.

Analyses were conducted in R statistical software (version 4.2.3; R Foundation) using the following packages: rlang, 20 arrow, 21 dplyr, 22 tidyr, 23 lubridate, 24 forcats, 25 table1, 26 cobalt, 27 MatchIt, 28 WeightIt, 29 mice, 30 MatchThem, 31 survey, 32 survival, 33 ggsurvfit, 34 broom, 35 ggplot2, 36 and xtable. 37

In total, 41 222 patients met our inclusion criteria (tirzepatide: 9193; semaglutide: 32 029) ( Figure 1 ). Prior to propensity score matching, patients who initiated tirzepatide, compared with semaglutide, were younger and a higher proportion were female, White, and had evidence of college education ( Table ). Patients who initiated tirzepatide had a lower prevalence of T2D and most other comorbidities. Despite demographic and clinical differences, mean (SD) baseline weight was similar between groups (tirzepatide: 110 [25.7] kg; semaglutide: 109 [25.2] kg), with measurement occurring an average (median) of 9.3 (4.0; interquartile range [IQR], 14-1; difference, 13) days before treatment initiation. The 1:1 propensity score matched cohort included 18 386 patients, with standardized mean differences for all variables lower than 0.1.

The mean (median) duration of on-treatment follow-up was 165 (129; IQR, 75-231; difference, 156) days. Follow-up was ended by discontinuation for 9963 (54.2%) (tirzepatide: 5140 [55.9%]; semaglutide: 4823 [52.5%]), medication switching for 153 (0.8%) (tirzepatide: 124 [1.3%]; semaglutide: 29 [0.3%]), and administrative censoring for 8270 (45.0%) (tirzepatide: 3929 [42.7%]; semaglutide: 4341 [47.2%]). The mean (median) duration of follow-up with administrative censoring alone (modified ITT analysis) was 257 (256) days. Distributions of initiation and follow-up times are provided in eFigure 1 and eFigure 2 in Supplement 2 .

Overall, 31 419 (76%) patients had at least 1 on-treatment follow-up weight and 35 097 (85%) had at least 1 follow-up weight during observation (eTable 3 in Supplement 2 ). The mean (median) days between weight observations on-treatment was 37.6 (27) (during observation, 62.5 [50]) for tirzepatide and 37.6 (27) (during observation, 59.1 [46]) for semaglutide.

Among the matched population at risk (undergoing treatment), 81.8% (95% CI, 79.8%-83.7%) receiving tirzepatide vs 66.5% (95% CI, 64.3%-68.7%) receiving semaglutide achieved 5% or greater weight loss, 62.1% (95% CI, 59.7%-64.3%) vs 37.1% (95% CI, 34.6%-39.4%) achieved 10% or greater weight loss, and 42.3% (95% CI, 39.8%-44.6%) vs 18.1% (95% CI, 16.1%-20.0%) achieved 15% or greater weight loss within 365 days ( Figure 2 ). HRs comparing tirzepatide with semaglutide were 1.76 (95% CI, 1.68-1.84) for 5% or greater weight loss, 2.54 (95% CI, 2.37-2.73) for 10% weighor greatert loss and 3.24 (95% CI, 2.91-3.61) for 15% or greater weight loss ( Figure 3 ).

The mean on-treatment change in body weight was −5.9% (95% CI, −6.0% to −5.8%) for tirzepatide vs −3.6% (95% CI, −3.7% to −3.4%) for semaglutide at 3 months, −10.1% (95% CI, −10.4% to −9.9%) vs −5.8% (95% CI, −6.0% to −5.5%) at 6 months, and −15.3% (95% CI, −16.0% to −14.5%) vs −8.3% (95% CI, −9% to −7.6%) at 12 months ( Figure 4 ). After adjusting for residual confounding, the absolute difference in weight loss between tirzepatide and semaglutide was −2.4% (95% CI, −2.5% to −2.2), −4.3% (95% CI, −4.7% to −4.0%), and −6.9% (95% CI, −7.9% to −5.8%) at 3, 6, and 12 months receiving treatment, respectively ( Figure 3 ).

Modified ITT analyses resulted in fewer patients achieving weight loss thresholds, smaller weight reductions, and slightly attenuated comparative effect estimates, though tirzepatide remained associated with significantly greater weight loss in all analyses (eResults 2.1, eFigure 3, eFigure 4, eFigure 5, and eFigure 6 in Supplement 2 ). A smaller proportion achieved 5% or greater weight loss within 1 year, 71.1% (95% CI, 69.9%-72.3%) with tirzepatide and 56.4% (95% CI, 55%-57.8%) with semaglutide, resulting in an HR of 1.63 (95% CI, 1.56-1.70). Similarly, mean changes in body weight were smaller: −5.3% (95% CI, −5.4% to −5.2%) for tirzepatide vs −3.3% (95% CI, −3.4% to −3.2%) for semaglutide at 3 months, −8.2% (95% CI, −8.4% to −8.0%) for tirzepatide vs −5.0% (95% CI, −5.1% to −4.8%) for semaglutide at 6 months, and −11.4% (95% CI, −12.0% to −10.8%) for tirzepatide vs −6.2% (95% CI, −6.7% to −5.8%) for semaglutide at 12 months. After adjusting for residual confounding, the difference in weight loss between those receiving tirzepatide vs semaglutide was −2.0% (95% CI, −2.1% to −1.8%) at 3 months, −3.2% (95% CI, −3.5% to −3.0%) at 6 months, and −5.1% (95% CI, −5.8% to −4.3%) at 12 months.

Sensitivity analyses using inverse probability of treatment weighting produced very similar results (eFigure 4 and eFigure 6 in Supplement 2 ). Results of the liraglutide validation analysis are given in eFigure 7, eFigure 8, eFigure 9, and eFigure 10 in Supplement 2 .

In stratified analyses, those without T2D had larger reductions in body weight than those with T2D for tirzepatide and semaglutide alike ( Figure 3 ; eFigures 11 and 12 in Supplement 2 ). Tirzepatide was still associated with significantly greater weight loss in all analyses (eFigure 13 and eFigure 14 in Supplement 2 ).

We observed no significant differences in the risk of any gastrointestinal AEs between those receiving tirzepatide vs semaglutide (eTable 4 in Supplement 2 ).

In this large clinical analysis of US adults with overweight or obesity who initiated tirzepatide or semaglutide treatment, those receiving tirzepatide were more likely to achieve 5% or greater, 10% or greater, and 15% or greater weight loss and experienced larger reductions in body weight at 3, 6, and 12 months. To our knowledge, this study represents the first clinical comparative effectiveness study of tirzepatide and semaglutide in adults with overweight or obesity. Comparative effect estimates were consistent in direction and significance between methodological approaches (propensity score matching, IPTW, modified ITT) and within subgroups of patients with and without T2D. No significant differences in the incidence of gastrointestinal AEs were observed.

Findings in this study are broadly consistent with existing evidence from RCTs. Among placebo-controlled trials of patients with overweight or obesity, treatment with tirzepatide at 10 mg per week resulted in 82% and 96% of individuals with and without T2D achieving 5% or more weight loss by 72 weeks, respectively (efficacy estimands). 9 , 10 Among similarly designed placebo-controlled trials, treatment with semaglutide at 2.4 mg per week resulted in 73% and 92% of individuals with and without T2D achieving 5% or greater body weight by 68 weeks, respectively (efficacy estimands). 6 , 7 While data from head-to-head trials are more limited, a single study that evaluated the glucose-lowering effect of tirzepatide (5 mg per week) compared with semaglutide (1 mg per week) in patients with T2D found that 5% weight loss was achieved by 69% and 58%, respectively. 11 Importantly, a trial comparing tirzepatide to semaglutide in patients with overweight or obesity, but without T2D is underway (SURMOUNT-5, NCT05822830) 38 ; the results, however, are not expected until late 2024.

This study has several strengths. First, the analysis included a large and recent cohort of patients with overweight and obesity evaluated in May 2022 (the month of tirzepatide approval) or later. It is likely that the weight reduction observed in our study was greater than that found in previous clinical studies of GLP-1 RA because such studies ended before semaglutide and/or tirzepatide were available. 39 , 40 Second, estimates were consistent in direction and significance between estimands (on treatment vs modified ITT), subgroups (with vs without T2D), and methodological approaches (propensity score matching, IPTW, complete case analysis). Third, this study included individuals likely ineligible for participation in related RCTs, including those with major depressive disorder. Major depressive disorder was common in our population (4044 patients [22%] had a history in the preceding 2 years) suggesting clinical trials may have excluded many patients using these medications in clinical settings. Finally, use of prescribing and dispensing data allowed us to include populations without T2D, which may not be captured in pharmacy claims data alone given limited insurance coverage for off-label use.

Our study is also subject to several limitations. Unlike many clinical end points, weight loss is directly observable to patients, which may result in informative censoring, with those observing no weight loss being more likely to discontinue or switch drugs. 40 , 41 Whereas a modified ITT analysis inclusive of postdiscontinuation weights showed smaller reductions in weight, differences between tirzepatide and semaglutide were similar. In addition, unmeasured confounding, especially the degree of motivation for weight loss, may exist. A substantial amount of unmeasured confounding, though, would be required to negate the treatment effect estimates observed in this study. This study used clinical EHR data, which has some inherent limitations. Information is collected during routine clinical care, and AEs are likely underreported relative to protocolized, prospective AE ascertainment in clinical trials. Similarly, weight changes are ascertained only when patients return for visits, and therefore observed event times are likely delayed relative to true times. However, we expect misclassification of AE and weight loss occurrence and timing to be nondifferential between groups, given the similarity of follow-up cadence between groups. Our imputation model assumed missingness was conditional on observed information only (eg, missing at random), which may be biased if unmeasured variables contributed to missingness. In addition, we relied on brand as a proxy for target dose because this approach most closely approximates randomization to a treatment arm, where the individual dose received may deviate from the target dose. Patients in both groups may receive doses that are higher or lower than standard full doses. Health system and payer information were unavailable for this analysis. Although the analytic sample included patients in 35 states, the geographic distribution was not representative of the US, which limited generalizability. Finally, this study included medications labeled for T2D only. Future studies are needed to compare versions labeled for weight loss.

Consistent with clinical trials, we found larger weight reductions among those without T2D, compared with those with T2D. 6 , 7 , 9 , 10 The underlying reasons are unclear. Although differential impacts on weight are possible, patients with and without T2D may have differing motivation levels for weight loss and may engage in other weight loss activities differentially. Additional research is needed to understand the complex relationships between motivations and outcomes for patients with and without T2D. Further, most patients in our study discontinued. Additional research on discontinuation is needed, including the role of shortages, adverse events, and costs.

In this large, propensity-matched, cohort study, individuals with overweight or obesity treated with tirzepatide were significantly more likely to achieve clinically meaningful weight loss and larger reductions in body weight compared with those treated with semaglutide. Consistent treatment effect estimates were observed in subgroups with and without T2D. Future work is needed to compare the effect of tirzepatide and semaglutide on other key end points (eg, reduction in major adverse cardiovascular events). 42 , 43

Accepted for Publication: April 14, 2024.

Published Online: July 8, 2024. doi:10.1001/jamainternmed.2024.2525

Open Access: This is an open access article distributed under the terms of the CC-BY-NC-ND License . © 2024 Rodriguez PJ et al. JAMA Internal Medicine .

Corresponding Author: Nicholas L. Stucky, MD, Truveta Inc, 1745 114th Ave SE, Bellevue, WA 98004 ( [email protected] ).

Author Contributions: Drs Rodriguez and Stucky had full access to all of the data in the study and take responsibility for the integrity of the data and the accuracy of the data analysis.

Concept and design: Rodriguez, Baker, Gluckman, Stucky.

Acquisition, analysis, or interpretation of data: Rodriguez, Cartwright, Gratzl, Brar, Gluckman, Stucky.

Drafting of the manuscript: Rodriguez, Gluckman, Stucky.

Critical review of the manuscript for important intellectual content: Rodriguez, Cartwright, Gratzl, Brar, Baker, Stucky.

Statistical analysis: Rodriguez, Cartwright.

Obtained funding: Stucky.

Administrative, technical, or material support: Rodriguez, Gratzl, Baker, Gluckman, Stucky.

Supervision: Gluckman, Stucky.

Conflict of Interest Disclosures: Dr Gluckman reported consulting fees from Premier outside the submitted work. No other disclosures were reported.

Data Sharing Statement: See Supplement 3 .

Additional Contributions: The authors thank Elisabetta Patorno, MD, Brigham and Women’s Hospital, Boston, for her substantial contributions to the study design. Dr Patorno was not compensated.

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Independent risk factors and mortality implications of de novo central nervous system involvement in patients hospitalized with severe covid-19: a retrospective cohort study.

1. Introduction

2. materials and methods, 2.1. study design and participants, 2.2. data collection, 2.3. definitions, 2.4. statistical analysis, 3.1. baseline characteristics of patients with severe covid-19, 3.2. central nervous system manifestations, 3.3. factors associated with cns involvement in patients with severe covid-19, 3.4. cns involvement as an independent risk factor for in-hospital mortality in severe covid-19 patients, 4. discussion, author contributions, institutional review board statement, informed consent statement, data availability statement, acknowledgments, conflicts of interest.

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Hanganu, A.R.; Dulămea, A.O.; Niculae, C.-M.; Moisă, E.; Hristea, A. Independent Risk Factors and Mortality Implications of De Novo Central Nervous System Involvement in Patients Hospitalized with Severe COVID-19: A Retrospective Cohort Study. J. Clin. Med. 2024 , 13 , 3948. https://doi.org/10.3390/jcm13133948

Hanganu AR, Dulămea AO, Niculae C-M, Moisă E, Hristea A. Independent Risk Factors and Mortality Implications of De Novo Central Nervous System Involvement in Patients Hospitalized with Severe COVID-19: A Retrospective Cohort Study. Journal of Clinical Medicine . 2024; 13(13):3948. https://doi.org/10.3390/jcm13133948

Hanganu, Andreea Raluca, Adriana Octaviana Dulămea, Cristian-Mihail Niculae, Emanuel Moisă, and Adriana Hristea. 2024. "Independent Risk Factors and Mortality Implications of De Novo Central Nervous System Involvement in Patients Hospitalized with Severe COVID-19: A Retrospective Cohort Study" Journal of Clinical Medicine 13, no. 13: 3948. https://doi.org/10.3390/jcm13133948

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Lilly’s obesity drug looks more potent than Novo’s in observational study

By Elaine Chen July 9, 2024

Want to stay on top of the science and politics driving biotech today?  Sign up  to get our biotech newsletter in your inbox.

Good morning. STAT published an investigation this morning on the untold story of the Human Genome Project and ethics concerns surrounding the ambitious project. Check it out  here .

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The need-to-know this morning

• Pfizer  Chief Scientific Officer and R&D Chief Mikael Dolsten is  leaving the company  after 15 years. A search for his successor is expected to last until early next year, the pharma giant said.
• UniQure  said its experimental gene therapy called AMT-130  slowed the progression of Huntington’s disease  by 80% compared to an external control group in a mid-stage clinical trial.

Interius to start first ever in-vivo CAR-T clinical trial

Interius Biotherapeutics will soon begin the world’s first clinical trial of in-vivo CAR-T therapy. Patients will receive an IV infusion designed to transform their immune cells into cancer killers inside their bodies.

Traditional CAR-T therapy is “ex-vivo,” meaning the immune cells are manipulated outside the body. It’s a complicated and expensive process that involves extracting the cells from the patient, shipping them to a specialized facility to be worked on, and then reinfusing the cells back into the patient.

Traditional CAR-T is out of reach for many patients, and researchers hope that the new in-vivo approach could provide a cheaper and more scalable option.

Read more  from STAT’s Jason Mast.

Pharma is losing friends on Capitol Hill

Pharma’s position in Washington has already been weakening over the past few years, as underscored by the passage of the Inflation Reduction Act, which allowed Medicare to negotiate drug prices. The industry’s influence is set to wane even more as some of its closest allies leave Capitol Hill.

My colleague Rachel Cohrs Zhang reports that at least six key pharma-friendly lawmakers are expected to have left their seats by the beginning of next year, and their likely replacements are less friendly to the industry and less interested in health care.

One example is Sen. Bob Menendez of New Jersey, home of pharma giants Johnson & Johnson, Merck, and Bristol Myers Squibb. Menendez is currently on trial for bribery charges, and the Democrat vying to replace him, Rep. Andy Kim, supports the party’s more ambitious drug pricing plans.

Read more  on the other allies pharma will be losing.

Lilly’s obesity drug looks more potent than Novo’s in observational study

In its pivotal Phase 3 trial, Eli Lilly’s tirzepatide (sold as Mounjaro/Zepbound) led to more weight loss than what was seen in the trial of Novo Nordisk’s semaglutide (sold as Ozempic/Wegovy). It’s been hard to directly compare the two drugs, though, since there haven’t yet been results from any head-to-head trials, but a new observational study suggests Lilly’s drug may indeed lead to greater weight loss.

The study,  published yesterday in JAMA Internal Medicine , analyzed the health records of over 18,000 people and found that those on tirzepatide had about 15% weight loss at one year, while those on semaglutide had about 8%. Additionally, 42% of patients on tirzepatide achieved more than 15% weight loss, compared with 18% of patients on semaglutide.

Since it’s a retrospective observational study, there are many limitations. For example, patients may have encountered shortages and may not have been able to consistently take their medications. Patients may have also been on different diets and exercise regimens.

We’ll be watching for results of  a randomized head-to-head trial  that Lilly is running that’s expected to complete in November this year.

A private equity approach to investing in neuro drugs

Investors have long been reluctant to invest in drug programs for neurological disorders. There’s been a history of failed studies and many trial endpoints are subjective and difficult to measure.

Bruce Leuchter, CEO of Neurvati Neurosciences, a Blackstone Life Sciences portfolio company, argues in a new opinion piece that investors should adopt a a “private equity model” to invest in neurology drug candidates.

This means looking at molecules later in the development lifecycle rather than at early-stage drugs that the classic venture model focuses on. While the costs to acquire a later-stage drug will be higher, investors would be able to better vet the program and confirm the rationale behind the drug’s mechanism, Leuchter writes.

Read more .

• When can pharma companies correct online misinformation? FDA explains,  Endpoints
• AI leads the way as health tech funding inches toward a post-pandemic stability,  STAT
• Trump’s 2024 platform abandons calls to cut Medicare, broadly restrict abortion,  STAT
• New study sparks debate about whether H5N1 virus in cows is adapted to better infect humans,  STAT

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Elaine chen.

National Biotech Reporter

Elaine Chen covers biotech and co-writes the The Readout newsletter.

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Observational Research Opportunities and Limitations

Edward j. boyko.

Epidemiologic Research and Information Center, VA Puget Sound Health Care System, Seattle, WA USA. University of Washington School of Medicine, Seattle, WA

Medical research continues to progress in its ability to identify treatments and characteristics associated with benefits and adverse outcomes. The principle engine for the evaluation of treatment efficacy is the randomized controlled trial (RCT). Due to the cost and other considerations, RCTs cannot address all clinically important decisions. Observational research often is used to address issues not addressed or not addressable by RCTs. This article provides an overview of the benefits and limitations of observational research to serve as a guide to the interpretation of this category of research designs in diabetes investigations. The potential for bias is higher in observational research but there are design and analysis features that can address these concerns although not completely eliminate them. Pharmacoepidemiologic research may provide important information regarding relative safety and effectiveness of diabetes pharmaceuticals. Such research must effectively address the important issue of confounding by indication in order to produce clinically meaningful results. Other methods such as instrumental variable analysis are being employed to enable stronger causal inference but these methods also require fulfillment of several key assumptions that may or may not be realistic. Nearly all clinical decisions involve probabilistic reasoning and confronting uncertainly, so a realistic goal for observational research may not be the high standard set by RCTs but instead the level of certainty needed to influence a diagnostic or treatment decision.

A major focus of medical research is the identification of causes of health outcomes, good and bad. The current gold standard method to accomplish this aim is the randomized controlled trial (RCT) ( Meldrum, 2000 ). The performance of a RCT requires strict specification of study conditions related to all aspects of its conduct, such as participant selection, treatment and control assignment arms, inclusion/exclusion criteria, randomization method, outcome measurement, and many other considerations. Such trials are difficult to mount due to the expense in terms of both time and money, and often lead to results that may be difficult to apply to a real-world setting due to either the rigor or complexity of the intervention or the selection process for participants that yields a population dissimilar from that seen in general clinical practice. A randomized controlled trial focuses on an assessment of the validity of its results at the expense of generalizability. For example, the Diabetes Prevention Program screened 158,177 subjects to yield 3,819 subjects who were eventually randomized to one of the four original arms ( Rubin et al., 2002 ). Other limitations of RCTs include a focus on treatment effects and not the ability to detect rarer adverse reactions; restrictions on diabetes duration at the time of trial entry, thereby yielding results that may not apply to persons with a different diabetes duration at the initiation of treatment; and high costs that limits the number of therapeutic comparisons. Regarding this last point, assessment of a new treatment for hyperglycemia requires comparison to existing accepted treatments, but the control population usually is restricted to fewer treatments than in current use, thereby limiting the ability to compare the new treatment to all existing treatments.

Given these considerations, observational research is often used to address important clinical questions in the absence of randomized clinical trial data, but may also make important potential contributions even when RCTs have been conducted. Examples include monitoring for long-term adverse events that did not appear during the time interval over which the RCT was conducted, or to assess whether the trial findings apply to a different population excluded from the trial due to younger or older age, gender, presence of comorbid conditions, or other factors. Observational research often also addresses other questions not suitable for randomized clinical trials, such as an exposure known to be harmful or in other ways unacceptable to participants or whose administration is inconsistent with ethical principles. Also, observational research can address other exposures that are not potentially under the control of the investigator, such as, for example, eye color, blood type, presence of a specific genetic marker, or elevations of blood pressure or plasma glucose concentration. Observational research may also provide preliminary data to justify the performance of a clinical trial, which might not have received sufficient funding support without the existence of such results.

This paper will review observational research methods applied to addressing questions of causation in diabetes research, with a particular focus on pharmacoepidemiology as an area of research where many important questions may be addressed regarding the relative merits of multiple pharmaceuticals for a given condition. There have been an increasing number of observational studies of the association between diabetes treatments and hard outcomes, such as death or CVD events. The increase in such studies likely has been facilitated by the availability of big data in general and specifically large pharmaceutical databases created by national health plans, large health care systems, or mail-order pharmacy providers ( Sobek et al., 2011 ). In addition, the ongoing development of diabetes pharmacotherapies approved based on ability to achieve an improvement in glycemic control but without data on hard outcomes may also provide the impetus to use such large databases for research on comparative safety and efficacy.

Observational Research Study Designs

Cohort and case-control studies.

The two most popular designs for investigating causal hypotheses are the cohort and case-control studies. Features are shown in Table 1 . The major difference between the two is that the cohort study begins with identification of the exposure status, whereas the case-control study begins with the identification of the outcome. A cohort study can be prospective, where exposed and non-exposed subjects are followed for the development of the outcome, or retrospective, where collected data can be used to identify both the exposure status at some past time point and the subsequent development of the outcome. A case-control study, on the other hand, can only look back in time for occurrence of the exposure. There are of course exceptions to these general statements. It is possible in some case-control studies to measure the exposure after the outcome in time if the exposure is invariant and if it is not related to a greater loss to follow-up among persons with the outcome due to mortality or other reasons. Examples of such exposures include genetic markers or an unchanging characteristic of adults such as femur length, eye color, or red blood cell type. Variations in these study designs include the case-cohort and case-only studies, which are described in detail elsewhere, and which a description of which will not be provided here ( DiPietro, 2010 ). Also, the relative merits of these study designs will not be discussed here but are covered in standard epidemiology texts.

Observational Study Designs for Assessment of Causal Relationships

Weaker Observational Research Designs

Other research designs are often used in studies reported in the medical literature. These include cross-sectional, case-series, and case-reports. The cross-sectional study has limited value in assessing a potential causal relationship since it may not be possible to determine whether the potential exposure preceded the outcome, except when the exposure does not vary over one’s life history, such as in the case of a genotype, ABO blood group, or eye color. Case-series and case reports are even more limited since it is not possible to assess if the outcome occurred more frequently among the persons included compared to a control population. Case reports do though have potential value in pharmaceutical safety research by generating potential signals that signify unexpected adverse events. Such monitoring is employed in the Food and Drug Administration’s Adverse Event Reporting System, and has led to changes in product labeling as well as restriction or outright removal of pharmaceuticals from the market due to safety concerns ( Wysowski & Swartz, 2005 ). Over 2 million case reports of adverse reactions were submitted between 1969–2002, resulting in only about 1% of marketed drugs being withdrawn or restricted. Therefore the noise-to-signal ratio for this method of surveillance is exceedingly high and presents an opportunity for other observational methods to better address this issue.

Observation Research for Causal Inference

Causal associations will always involve correlation, but the presence of a correlation does not imply causation. The challenge of observational research is to assess whether a correlation is present and then determine whether it may be due to a causal association. A list of criteria was developed by Dr. Austin Bradford Hill decades ago that is still referred to frequently today ( Hill, 1965 ), although reexamination of these criteria more recently has led to the conclusion that only one of the nine original features is really necessary for a causal relationship in a observational study ( Phillips & Goodman, 2004 ; Rothman & Greenland, 2005 ). The magnitude of the observed association, another Hill criterion, often figures into determinations about the presence of bias, with those of greater magnitude considered less likely to be due to bias and more likely due to a causal process ( Grimes & Schulz, 2002 ).

Examination of the features of an RCT provide some insight into the limitations of observational research in assessing causal associations. The randomization process provides the opportunity for equal distribution of risk factors for the outcome among persons assigned to the treatment and control. Thus any difference in the outcome between these two groups will not likely be due to unequal distribution of risk factors by treatment assignment. The use of randomization provides a way to approach the problem of not having complete knowledge about predictors of all clinically important outcomes. If we did have such knowledge then groups with exactly equal risks of the outcome could be assembled by the investigator. As we do not have such knowledge, the process of randomization utilizes chance to distribute both known and more importantly unknown risk factors for the outcome, and is most likely to achieve this aim with larger sample size ( Efird, 2011 ). Randomization, though, does not guarantee that the treatment and control group will have the same risk of the outcome. Accidents of randomization have occurred for known risk factors for outcomes as in the UGDP, where older subjects with a higher prevalence of cardiovascular disease risk factors were disproportionately assigned to the tolbutamide treatment arm ( Leibel, 1971 ). Such accidents also must occur for the unknown risk factors, although these would not be apparent to the investigator.

Bias in Observational Research

Confounding bias.

Observational research does not have the benefit of randomization to allocate by chance risk factors for an outcome of interest. Exposures to risk factors occur due to self-selection, medical provider prescription, in association with occupation, and for other reasons. When an exposure of interest is strongly associated with another exposure that is also related to the outcome, confounding bias is present, but methods exist to obtain an unbiased estimated of the exposure-disease association as long as the confounding factor is identified and measured accurately.

A cross-sectional study of a genetic marker (Gm haplotype Gm 3;5,13,14 ) and diabetes prevalence provides an example of confounding bias. Subjects included members of the Pima and Papago tribes of the Gila River Indian Community in Southern Arizona who underwent a medical history and examination every two years including assessment of diabetes status through oral glucose tolerance testing ( Knowler, Williams, Pettitt & Steinberg, 1988 ). Subjects were further characterized by degree of Indian heritage measured in eighths and referred to as “quantum.” A total of 4,640 subjects of either 0/8, 4/8, and 8/8 quantum were included in this analysis. There were 1,336 persons with and 3,304 persons without diabetes available for analysis, yielding a crude (unadjusted) overall odds ratio of 0.24 for the association between haplotype Gm 3;5,13,14 and diabetes prevalence ( Figure 1 , Panel A). This result supports a lower prevalence of diabetes in association with haplotype Gm 3;5,13,14 , but the unadjusted result represents a substantial overestimate due to confounding by Quantum. In Figure 1 panel B, subjects were divided by the three Quantum categories found in the sample, and within each of these the odds ratio is closer to 1.0 and therefore of smaller magnitude than the crude result. Note that collapsing the three tables in Panel B by summing the cells yields the single overall table shown in Panel A. Adjustment for these Quantum categories yields an odds ratio of 0.59, which is of smaller magnitude than the result seen in the unadjusted analysis ( Figure 1 , Panel B). Although the odds ratios vary across Quantum categories, a test for heterogeneity across these strata was non-significant (p=0.295). Therefore the null hypothesis that the odds ratios differed across Quantum strata could not be rejected.

Cross-sectional study of Native Americans of the Pima and Papago Indian tribes in Southern Arizona on the associations between the GM haplotype Gm 3;5,13,14 , native quantum, and diabetes mellitus prevalence. Panel A displays all participants combined with Native quantum of either 0/8, 4/8 or 8/8 by presence of diabetes mellitus in relation to Gm 3;5,13,14 presence or absence. The overall (crude) odds ratio for the association is shown. Panel B displays all participants from Panel A stratified by Native quantum, demonstrating confounding by Native quantum as judged by the discordance between the crude and stratified or Quantum-adjusted results. Panel C demonstrates that Quantum meets the criterion as a confounding variable due to its negative association with Gm 3;5,13,14 and positive association with diabetes prevalence.

Examination of the frequency of haplotype Gm 3;5,13,14 and diabetes prevalence across Indian heritage Quantum reveals the reason for the overestimation of the association in the unadjusted analysis. Diabetes occurred more frequently while the haplotype Gm 3;5,13,14 occurred less frequently among subjects with greater Indian heritage ( Figure 1 , Panel C). Adjustment for the imbalance in Quantum by haplotype Gm 3;5,13,14 in this specific example and in general any accurately measured confounding factor yields a less biased odds ratio that is closer to the true magnitude of the association between this haplotype and diabetes prevalence.

Another more recent example of confounding can be seen in a case-cohort European study of the association between artificially sweetened soft drinks and the risk of developing type 2 diabetes ( 2013 ). The unadjusted hazard ratio for the daily consumption of ≥ 250 g of this beverage type was 1.84 (95% CI 1.52 to 2.23) representing a statistically significant elevation in risk. After adjustment for daily energy intake and BMI, the hazard ratio diminished to 1.13 (95% CI 0.85 to 1.52) and was no longer statistically significant (p=0.24). The investigators concluded that consumption of artificially sweetened soft drinks was not associated with type 2 diabetes risk in their population.

Multiple methods exist to remove the bias from recognized, accurately measured confounding factors, but unfortunately there is no widely accepted option for handling unmeasured confounding factors and adjusting for this bias. In this regard observational research is unable to match the ability of a RCT to account for this potential bias. Methods have been developed to better assess whether associations represent causal pathways that will be described later in this paper.

Information Bias

Observational research can be susceptible to other types of bias. Information bias refers to inaccurate assessment of the outcome, the exposure, or potential confounding variables. An example includes measurement of nutritional intake, which is often assessed by research subjects completing a food frequency survey or 24-hour dietary recall. Even if subjects report these intakes correctly, the likelihood is low that this will reflect long-term dietary intake exactly. Attempts have been made to reduce the error of these measurements through biomarker calibration that in one study was based on a urinary nitrogen protocol to estimate daily protein consumption over a 24-hour period ( Tinker et al., 2011 ). This analysis revealed a slight increase in risk of incident diabetes in association with a 20% higher protein intake in grams (Hazard Ratio 1.05, 95% CI 1.03–1.07). Recalibrated results based on the results of the urinary nitrogen protocol yielded a substantially higher diabetes hazard ratio of 1.82 (95% CI 1.56–2.12) that after adjustment for BMI was reduced to 1.16 (95% CI 1.05–2.28). In this example, reduction of measurement error yielded a difference of greater magnitude than see in the analysis based on dietary self-reports only without objective validation, although theoretically more accurate measurements may yield smaller differences, depending on the type and magnitude of measurement error.

Selection Bias

Selection bias may produce factitious exposure-disease associations if the study population fails to mirror the target population of interest. For example, selection of control subjects from among hospitalized patients as might be the case in a study based on administrative data may not accurately depict smoking prevalence among controls, given that smoking is related to multiple diseases that would increase the risk for hospitalization. Effective observational research must recognize the potential for bias and attempt to minimize it both in the design and analysis, as well as accurately describing limitations of these data and the implications for study validity in reports of results.

Agreement and Discrepancies between Observational and Clinical Trial Research

One way to assess whether the potential biases of observational studies result in failure to detect true associations is by comparison of observational versus RCT results on the same questions. Since observational studies of treatments often precede definitive clinical trials, several authors have assessed agreement between similar hypotheses tested using the gold standard compared to observational designs, concluding that agreement between the two is high. A comparison of 136 reports published between 1985 to 1998 on 19 different treatments found excellent agreement, with the combined magnitude of the effect in observational studies lying within the 95% confidence intervals of the combined magnitude of the effect in RCTs for 17 of the 19 hypotheses tested ( Benson & Hartz, 2000 ). Another comparison focused on comparing the results of meta-analyses of observation and clinical trial research on five clinical questions that were identified through a search of five major medical journals from 1991 to 1995 ( Concato, Shah & Horwitz, 2000 ). These investigators concluded that average results of these studies were “remarkably similar.”

In contrast, other research has demonstrated discrepancies between RCT and observational designs. The Women’s Health Initiative (WHI) was a RCT of dietary and menopausal hormone interventions to assess these effects on mortality, cardiovascular disease, and cancer risk ( Prentice et al., 2005 ). Perhaps unique to this study was the establishment of a concurrent observational study accompanying the randomized clinical trial, thereby permitting direct comparison of reported associations by type of research design within the same study framework. In the trial/observational study of estrogen plus progestin for menopausal hormone replacement, marked differences were seen between the treatment and control groups by participation in the RCT or observational study ( Table 2 ). In the RCT, no important differences were seen by treatment assignment for race, educational level, BMI, or current smoking status. This was not true by estrogen-progestin exposure in the observational study, where exposed women were more likely to be White, having completed a college degree or higher, and less likely to be current smokers or obese. Outcomes occurred more frequently in the estrogen-progestin arm of the RCT, but less frequently in the corresponding arm of the observational study, except for venous thromboembolism ( Table 2 ). Hazard ratios for these comparisons adjusted for imbalances in baseline potential confounding factors show a harmful effect of estrogen-progestin use that is statistically significantly elevated in 2 of 3 outcomes and a discordance with the observational results due to null, somewhat protective hazard ratios or in the case of venous thromboembolism, an elevated hazard ratio of considerably smaller magnitude than in the clinical trial. Although good agreement between clinical trials and observational research occurs often, the example of the WHI prevents having complete confidence in the results of observational studies.

Comparison of baseline characteristics and outcomes in the randomized controlled trial and observational study of estrogen-progestin treatment in the Women’s Health Initiative (1994–2002).

Achievements of Observational Research

Despite the limitations of observational research design, many well-accepted causal associations in medicine are supported entirely or in part due to this type of investigation. Several examples include the association between hyperglycemia and diabetes complications including retinopathy, nephropathy, peripheral neuropathy, and ischemic heart disease ( 2013 ). Other well known examples include hypertension and stroke, smoking and lung cancer, asbestosis and mesothelioma, and LDL and HDL cholesterol concentrations and risk of ischemic heart disease ( Churg, 1988 ; Gordon, Kannel, Castelli & Dawber, 1981 ; Kannel, Wolf, Verter & McNamara, 1970 ; Pirie, Peto, Reeves, Green & Beral, 2013 ). In the case of complications due to hyperglycemia, high LDL-cholesterol concentration, and hypertension, clinical trials to reduce these levels have resulted in reductions in the rate of these outcomes, further supporting a causal association ( 1991 ; 1994 ; 1998 ; 1998 ). For many associations that involve an exposure that cannot be controlled by the investigator or should not be modified for ethical reasons, observational research may be the only avenue for direct testing of these associations in humans.

Causal Inference from Observational Research

The results of an observational research study are never interpreted in an information vacuum. Given the potential for bias with this study design, a number of other factors should be considered when weighing the strength of this evidence. First and foremost would be the replication of the finding in other observational research studies. Additional evidence to bolster the potential causal association would be support from the biological understanding of underlying mechanisms, animal experiments confirming that the exposure results in a similar outcome, and trend data in disease incidence following changes in exposure prevalence. For example, in the UK Million Women Study where median age was reported at 55 years, women who quit smoking completely at ages 25–34 or 35–44 years had only 3% and 10% of the excess mortality, respectively, seen among women who were continuing smokers ( Pirie, Peto, Reeves, Green & Beral, 2013 ). Coronary heart disease deaths in the U.S. declined by approximately 50% between 1980 to 2000. One analysis that addressed the reasons for this decline concluded that change in risk factors (reductions in total cholesterol concentration, systolic blood pressure, smoking, and physical inactivity) accounted for approximately 47% of this decrease ( Ford et al., 2007 ). These trends provide support for a causal association between smoking and lung cancer, and multiple cardiovascular disease risk factors and coronary death risk.

Pharmacoepidemiology

Many questions regarding the use of pharmaceuticals may never be answered through use of RCTs, thereby creating a need to address knowledge gaps using observational research. The specialized field of pharmacoepidemiology directly addresses these needs. The earliest appearance of the term “pharmacoepidemiology” on PubMed.com is in an article written in 1984 ( Lawson, 1984 ). The field of pharmacoepidemiology encompasses the use of observational research to assess pharmaceutical safety and effectiveness. For example, diabetes pharmaceuticals have received FDA approval based on efficacy at lowering glucose and safety, without the need to prove efficacy at preventing long-term complications. The sulfonylurea hypoglycemic agents glyburide and glipizide are in widespread use to manage the hyperglycemia of diabetes, but it is not clear whether one is associated with a greater reduction in hard outcomes such as mortality or diabetes complications, as this has not been tested in a clinical trial. Use of such surrogate endpoints as opposed to the hard outcomes one wishes to prevent has been criticized as an ineffective and potentially harmful approach to medication approval ( Fleming & DeMets, 1996 ; Psaty et al., 1999 ). Design of clinical trials to address hard as opposed to surrogate endpoints typically requires larger sample size, longer follow-up, and greater costs.

Observational research may also identify adverse effects associated with the use of pharmaceuticals that were not anticipated based on research conducted in support of the drug approval process. The withdrawal of the thiazolidinedione agent troglitazone from the U.S. market in 2000 followed reports on cases of severe liver toxicity during post-marketing surveillance. Similar data on a high number of reported cases of severe myopathies in cerivastatin users led to its withdrawal from the worldwide market in 2001 ( Furberg & Pitt, 2001 ). An observational study using administrative claims databases to assess the relative safety of lipid lowering medications in the U.S. between 2000–2004 reported a much higher risk for hospitalization for treatment of myopathy among cerivastatin users compared to users of other statin and non-stain lipid lowering agents ( Cziraky et al., 2006 ).

Confounding by Indication

As with other observational research designs, there are limitations to pharmacoepidemiology due to biases previous described, but in addition to these is the vexing phenomenon of confounding by indication, also referred to as channeling bias ( McMahon & MacDonald, 2000 ; Petri & Urquhart, 1991 ). This refers to an observed benefit (or harm) associated with a pharmaceutical due to the indications for treatment with it and not a medication effect. A hypothetical example of how confounding by indication results in outcome differences not due to medication effect is shown in Figure 2 , which provides an example of how the choice of a diabetes pharmaceutical may depend on the existence of a condition (higher serum creatinine reflecting lower GFR) associated with higher mortality risk ( Fox et al., 2012 ).

A hypothetical population of 2000 identical persons with type 2 diabetes differing only by renal function as measured by serum creatinine and assigned to either metformin or glipizide based on the serum creatinine level. The active treatment, though, is never dispensed, and instead substituted with a identical placebo. An expected difference in mortality is seen between the two groups given the association between poorer renal function and mortality in the glipizide group. This difference cannot be explained by the effect of the active pharmaceutical (since there was none) and therefore represents an example of confounding by indication.

Several approaches exist to the problem of confounding by indication. If there is no association between the indication for the pharmaceutical and the outcome of interest, then no bias will occur, since an association must also be present between both the indication and the outcome to yield a biased result. This same principle applies to all confounding factors ( van Stralen, Dekker, Zoccali & Jager, 2010 ). If the conditions for confounding are fulfilled, then statistical adjustment techniques are available to produce unbiased estimates of effect. Commonly used methods in biomedical research include linear regression analysis for continuous outcomes, logistic regression for categorical outcomes, and the Cox proportional hazards model for time-to-event outcomes. In addition, propensity scores have risen in popularity over the past decade. An “all fields” search of Pubmed conducted January 15, 2012 using the search term “propensity score” yielded 2,895 hits for the immediate past 5 years, and only 715 hits for the previous 5 years. The propensity score method models the probability of exposure in relation to predictor variables, and therefore estimates the likelihood, in the case of a pharmacoepidemiology study, of a subject receiving a particular pharmaceutical based on his or her characteristics ( Rubin, 2010 ). An additional step is required which uses standard previously mentioned adjustment methods to remove the bias associated with varying likelihood of receiving the pharmaceutical. Despite the rising popularity of this method, it has been demonstrated to be merely equivalent and sometimes inferior to standard multivariate adjustment methods ( Shah, Laupacis, Hux & Austin, 2005 ; Sturmer et al., 2006 ). Furthermore, propensity scores cannot address the issue of unmeasured confounding ( Cummings, 2008 ). So if the indications for the pharmaceutical cannot be determined from the other measured factors, neither multivariate adjustment or propensity scores will allow for adjustment and removal of bias.

Several design features of observational studies may increase the likelihood of confounding by indication but if recognized may be amenable to correction in the design or analysis phases of a study. Assessing outcomes for pharmaceuticals prescribed for different indications or by a comparison of populations who differ with regard to the presence of medication contraindications may introduce bias into comparisons. An assessment of the mortality risk associated with beta-blocker use compared to other antihypertensive medications should exclude participants in whom beta blockers but not other antihypertensive medications are prescribed for other indications, such as migraine headache or stage fright prophylaxis, as these conditions may be associated with better outcomes and lead to over-optimistic survival benefit. Also, failure to consider medication contraindications may lead to risk of the outcome differing by medication used, as seen in the example in Figure 2 which would lead to a higher frequency of subjects with renal insufficiency in the glipizide treatment group for hyperglycemia. To account for this potential bias, subjects with contraindications for use of any of the pharmaceuticals of interest in the comparison should be eliminated from the study. For example, recent studies of mortality and cardiovascular events among users of sulfonylurea or metformin monotherapy for treatment of diabetes in the Veterans Health Administration system excluded patients with serious medical conditions at baseline that might influence the prescription of diabetes medication ( Roumie et al., 2012 ; Wheeler et al., 2013 ). For example, some items on the list of exclusions were congestive heart failure, serum creatinine concentration of 1.5 mg/dl or greater, HIV, and other conditions described in this publication. Despite these design features and adjustment methods to correct for factors associated with a particular prescription that may also be associated with a different outcome risk, there will always be some uncertainty about the presence of bias due to residual confounding by indication.

Methods to Improve Causal Inference from Observational Research

Instrumental variables analysis has been promoted as a method to overcome the inability to exclude undetected confounding in observational research. This method involves identification of a factor that strongly predicts treatment (or exposure in an epidemiologic study not involving a pharmaceutical). This factor is referred to as an “instrument,” and it is used in a manner analogous to the intention to treat analysis employed in RCTs ( Thomas & Conti, 2004 ). A Mendelian Randomization study is a type of instrumental variable analysis that uses a genetic marker as the instrument ( Thomas & Conti, 2004 ). Although intriguing in concept, the difficulty is in the application, as this relies on finding an “instrument” that is (1) causally related to treatment but not unobserved risk factors for the outcome, and (2) influences the outcome only through its effect on treatment ( Hernan & Robins, 2006 ). This method is being explored in pharmacoepidemiologic investigations, with one example being use of physician prescribing preference for types of NSAIDS in the evaluation of the gastrointestinal toxicity of COX-2 inhibitors versus non-COX-2 inhibitor NSAIDS ( Brookhart, Wang, Solomon & Schneeweiss, 2006 ). This analysis reported a protective association with COX-2 inhibitors only in the instrumental variable analysis, leading the authors to conclude that this analysis resulted in a reduction in unmeasured confounding. Examples can also be found in the diabetes epidemiology literature, such as the lack of association between serum uric acid level and type 2 diabetes risk ( Pfister et al., 2011 ), and higher risk associated with lower sex hormone-binding globulin concentration ( Ding et al., 2009 ).

Conclusions

As it will not be possible to assess efficacy of all possible treatment comparisons in all possible groups of interest, or identify adverse (or unexpected beneficial) outcomes requiring longer follow-up or greater sample size using RCTs, observational research stands prepared to step forward to address these knowledge gaps. Much medical knowledge and practice currently rests on a foundation of observational research. Perhaps this is not noticed due to the gloss and novelty of recently completed RCTs. Little research has been conducted comparing results from observational and clinical trial designs, but that which has been completed finds generally good agreement in these findings. With any observational research finding, though, comes less certainly due to the inability to completely exclude the possibility of residual confounding, or in the case of a pharmaceutical, confounding by indication. However, the expectation of absolute certainty is unrealistic and inconsistent with the current practice of medicine, where decisions are made probabilistically, with the threshold for actions such as further testing or treatment varying widely depending on the comparative costs and benefits of true and false positive and negative decisions ( Boland & Lehmann, 2010 ; Pauker & Kassirer, 1980 ; Plasencia, Alderman, Baron, Rolfs & Boyko, 1992 ). Observational research definitely has had and will continue to have an important role in providing the information needed to improve medical decision-making. There is always room for improvement and the hope that the future will bring better methods to further reduce the uncertainty surrounding the validity of its results.

Acknowledgments

Grant Support: VA Epidemiologic Research and Information Center; the Diabetes Research Center at the University of Washington (DK-017047)

Thanks for James S. Floyd MD for his careful review of this manuscript. The work was supported by the VA Epidemiologic Research and Information Center; the Diabetes Research Center at the University of Washington (DK-017047); and VA Puget Sound Health Care System.

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