What is a Matched Pairs Design?
A matched pairs design is an experimental design where researchers match pairs of participants by relevant characteristics. Then the researchers randomly assign one person from each pair to the treatment group and the other to the control group. This type of experiment is also known as a matching pairs design.
Statisticians recommend using this design to control for potential confounders that would otherwise bias the study’s results. The matched pairs experimental design is particularly advantageous for studies with limited sample sizes. When sample sizes are small, it can be challenging to achieve well-balanced groups through random assignment alone.
To conduct this type of experiment, researchers must identify the characteristics they’ll use to match the participants. Typically, these attributes include the potential confounders along with other relevant qualities such as age, gender, and race. Matching factors can incorporate medical history, lifestyle habits, and baseline measurements of the outcome of interest.
After identifying the criteria for the matched pairs design, researchers select pairs of participants with similar characteristics. Then they split each pair between the two experimental groups. For example, if a pair of participants match on the relevant variables, the researchers randomly assign one to the treatment group and the other to the control group.
This process creates two similar experimental groups. The goal is to reduce variability between groups relative to a typical between-subjects study.
Learn more about Experimental Designs, Random Assignment, and Control Groups.
Suppose a study evaluates the effectiveness of a new drug for treating hypertension. The researchers match participants on their age, gender, BMI, and baseline blood pressure and then randomly assign the members of each pair to receive the drug or a placebo.
This matched pairs design ensures that the treatment and control groups have similar characteristics at the beginning of the study. Notably, the process explicitly equalizes the factors the researchers know to affect hypertension. Consequently, if the mean blood pressures of the treatment and control groups differ at the end of the study, the researchers can confidently state that the drug caused the difference.
Advantages of a Matched Pairs Design
Helps Researchers Draw Causal Conclusions
A matched pairs design helps researchers draw causal inferences by controlling for confounding variables. It helps ensure that the experimental groups are equivalent before the experiment. Hence, the experimental treatment likely caused the differences the researchers observed afterward.
Learn more about Confounding Variables and how they can bias the results.
Increases Statistical Power and Precision
Another advantage of this experimental design is that it helps increase the precision and statistical power of the study. By matching participants, the experimental design reduces the variability between groups, making it easier to detect a significant difference between them. This condition increases a hypothesis test’s ability to find an effect when it exists and produces a more precise estimate of the effect.
Learn more about Statistical Power and How Confidence Intervals Assess Precision.
Disadvantages of a Matched Pairs Design
With a matched pairs design, if one subject drops out of the study, the study must drop the other member of the pair. In other words, one dropout causes the study to lose two participants!
Matching Can Be Difficult
Researchers might find it challenging and time-consuming to find participants who match on all the characteristics. As the number of variables increases, the challenge of matching subjects for all of them also increases. This difficulty can increase the cost and logistical challenges of the study and limit the sample size.
Might Not Control All Confounders
This disadvantage is an extension of the previous one. If the outcome of interest is complex and involves many factors, a matched pairs design might not be able to match participants on all of them. When a design does not control a confounder, it can bias the results, making them untrustworthy.
In this case, researchers can use random assignment with a sufficiently large sample size. This approach requires larger samples, but it tends to produce equivalent experimental groups without requiring researchers to match subjects.
Despite these limitations, a matched pairs design is a valuable tool for conducting experiments. By carefully selecting and matching participants, researchers can use smaller sample sizes while increasing the statistical power of their study and obtain more precise estimates of the treatment effect.
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