Subgroup analysis: What it is + How to avoid mistakes  

The first step in conducting a subgroup analysis is to define the groups you want to include in your study. Your goal is to determine whether any of these groups have a higher risk of developing a particular disease than other groups. For example, if you’re studying breast cancer, you may want to know whether women who have had previous surgeries are at higher risk than women who have not had surgery.

Once you’ve decided what your subgroups will be, it’s time to collect data from each group. You’ll want to collect information from your target population. This can be done through polls, surveys, or by collecting medical records for those diagnosed with the condition during your project.

Once you’ve collected data from both healthy people and those with the disease or condition under study, it’s time for statistical analysis! The purpose of statistical analysis is twofold: firstly, we need to make sure there aren’t any errors in our sample size; secondly, we need to see whether there are any differences between our samples (that is, whether there are differences between populations with different characteristics).

What is subgroup analysis?

Subgroup analysis is a process that allows you to drill down to see how specific variables affect the outcome of secondary data analysis. Respondents are grouped according to demographic characteristics like race, ethnicity, age, education, or gender. Other variables can be party identification, health status, or attitudes toward certain situations.

A researcher might analyze differences in variable means or distributions across subgroups to identify disparities or other differences.

For example, let’s say that you have a survey about people’s attitudes toward the use of animals for scientific research, and you’re interested in whether there are any differences between men and women in their opinions on this topic.

You could perform a subgroup analysis by dividing your sample into male and female respondents and examining their answers to see if there is any difference between them.

In subgroup analyses (for instance, an intervention or a treatment), we seek to determine the outcome of a factor in specific segments of the population or on specific parameters. 

Subgroup analysis can be classified into two types: 

  1. Prespecified 
  2. Post-hoc.

How to avoid mistakes  

Performing multiple tests on the same data can result in false positives in large-scale projects. It is possible for some researchers to ignore a large number of tedious or repetitive results in favor of subset results that they tend to be biased towards.

This is especially true when working with machine learning algorithms, which are often used to generate a lot of repetitive results that may not be useful to the user. The time it takes for these algorithms to run can be very long and should be factored into the cost of running an experiment.

This is an issue because it can lead researchers down a path without considering other possibilities that may exist in their data set or alternative approaches that would produce better results.

When you analyze your data using subgroups, you’re breaking it down into smaller groups to see if there are any differences between them.

If you want to look at how gender affects a certain outcome, you might break up your study sample into men and women and then compare their responses. But how many people should be in each group? And how many comparisons do you need to make?

There are two main reasons subgroups can lead to error. The sample size can be too small, and too many comparisons can be made. When you break down your study sample into many subgroups, you may end up with too few participants to detect differences or ensure differences aren’t just a matter of chance.

Subgroup Analysis Advantages

The main advantage of subgroup analysis is that it allows researchers to test their hypotheses in more detail. They may find out that certain subgroups respond better than others or that there are differences between men and women, for example.

Subgroup analysis is a common technique used in medical research. It can be thought of as an extension of the approach used in a standard study, where different groups are examined to see if they respond differently to a treatment. However, this technique can be problematic for several reasons:

  • Some studies don’t define their subgroups upfront or state how many subgroups will be examined. If a researcher doesn’t do this, it’s difficult for others to understand why they chose certain groups and what they were trying to show with each analysis. A good researcher should also report on all of the subgroups he or she analyzed, not just the ones that gave rise to interesting findings.
  • It’s possible that when analyzing subgroups, researchers might find something statistically significant but clinically insignificant (that is, something that doesn’t really matter). For example, let’s say we’re studying whether aspirin works better than acetaminophen for treating headaches; we find that 80 percent of people who took aspirin had no relief whatsoever.

How to do a subgroup analysis

The important role of subgroup analysis in significant research cannot be overstated. Because of this, it is essential that the following elements are included in any report:

  1. A clear indication that the analysis results are subgroup results.

  2. The appropriate significance levels are calculated and reported.

  3. If the research was pre-specified or post-hoc, this should be stated in the write-up.

Subgroup analysis is an important component of a research project. You will find many different products on the market. They have all been designed to benefit your research endeavors, but you have to know how to take advantage of them effectively.

QuestionPro for analysis

At QuestionPro, we have a quota control logic that you can use for subgroup analysis. We can provide and distribute survey URLs with custom variables to differentiate subgroups. You can also create subgroup-specific questions in the same survey by creating logic based on the subgroup.

For example, let’s say you want to analyze 50 male and 50 female respondents. You can add gender as a select one question and then add quota control logic for males and females. Based on gender question responses, we can create logic for male or female-specific questions.

This way, in response, you can easily subgroup male and female respondents with their responses and based on quota control limits, ensure you get an exact number of respondents.

Learn how to use QuestionPro’s best features to support your market research needs 

Authors: Danielle Figueroa, Virat Harsoda