Think of a time you looked at a study and wondered if the thing being studied—a treatment, program or other intervention—was more effective for some people than others. Subgroup analysis is one way of finding out. It’s a type of analysis done by breaking down study samples into subsets of participants based on a shared characteristic. The goal is to explore differences in how people respond to an intervention.
For example, let’s say you want to study the effectiveness of a new drug for pain relief. You might set up a randomized controlled trial where one group gets the drug (the intervention group) and the other gets a placebo (the control group). Your goal is to find out whether those who receive the new drug report less pain compared to the control group.
However, you might also want to know if the new drug works better for certain groups of people than others. So you divide the study participants into subgroups according to factors that may be important: the type of condition causing the pain, how long the condition has been present, gender, age, etc. You may learn that the treatment works better for certain conditions and for women below a certain age—all potentially crucial information.
This might sound easy enough. But the research world struggles with subgroup analysis. That’s because, when done improperly, it can lead to exaggerated or wrong findings.
How subgroup analysis can go wrong
There are two main reasons subgroups can lead to error. The sample size can be too small, and there can be too many comparisons done. When you break down your study sample into many subgroups, you may end up with too few participants in each to detect differences, or to ensure differences aren’t just a matter of chance.
Take our pain relief study. Let’s say there’s a small but important difference in how people with neck pain respond to the treatment versus those with back pain. With enough people in the subgroups, you could find that difference, even if it’s small. But if your subgroups have too few people in them, you won’t have the “statistical power,” as it’s called, to detect the difference. As a result, you miss a difference that exists. Scientists call this a false-negative error.
Subgroup analysis can also lead you to make a false-positive error—when you see differences that aren’t really there. If you slice and dice your study sample enough times, you’ll eventually end up with a subgroup that responds to the pain treatment differently than the rest—such as redheads or people born in January. That would be what scientists call a spurious finding—one that doesn’t make sense biologically or isn’t based on sound theory.
There’s also the kind of error that happens when you inappropriately define your subgroups. Take a factor such as age, for example. In your study, you might look at how the drug affects people of different ages—say, people in their 20s, 30s and 40s. But really, what’s your rationale for subgroups of 10 years and not five years or 20? What if, by pure chance, the 37- and 38-year-olds respond really well to the treatment? Would you be able to resist the temptation to divvy up your sample into two-year subgroups and report on those findings? What if that meant the difference between getting your research published and not?
When subgroup analysis goes right
Despite these problems, there are certain things you can look for to tell whether a subgroup analysis has been done right:
- the subgroup analysis is a stated study objective from the start—not an afterthought;
- the researcher can explain the reason for doing the subgroup analysis (based on previous research or a sound hypothesis, for example);
- ideally, the researcher defines the subgroups upfront and states how many subgroup analyses will be done. As well, the researcher reports on all of them, not just the ones that give rise to interesting findings; and
- the study is designed so that the subgroups have large enough sample size.
Subgroup analysis is important for investigating differences in how people respond to a treatment or intervention. But when misused, it can result in misleading findings. That’s why it’s important to understand the risks associated with this kind of analysis and to know what to look for when you come across it.
Source: At Work, Issue 75, Winter 2014: Institute for Work & Health, Toronto