Source link <\/a><\/p>\n","protected":false},"excerpt":{"rendered":"November 13, 2026 5 min read Add topic to email alerts Receive an email when new articles are posted on Please provide your email address to receive an email when new articles are posted on . ” data-action=subscribe> Subscribe We were unable to process your request. Please try again later. If you continue to have this issue please contact customerservice@slackinc.com. Back to Healio Key takeaways: An observed association reported in a study may not reflect true causal mechanisms. Researchers must ask the right causal question when designing a study. Observational studies remain a valuable way to assess real-world relationships and long-term outcomes, but confounding variables, selection bias and poor design can make for spurious associations that perpetuate misinformation. The most difficult step for researchers designing an observational study is not choosing a statistical method, Nandita Mitra, PhD, professor of biostatistics and codirector of the Center for Causal Inference at the Hospital of the University of Pennsylvania, said during a keynote presentation at the Dermatology in Pharma Educational Collaborative (DPEC). Before analyzing any study, Mitra said, the hardest questions come first: \u201cWhat are we trying to learn, and can the data we have answer that question?\u201d Causal inference, Mitra said, changes how people think about evidence by moving beyond observing patterns and toward understanding cause-and-effect relationships. \u201cIf I have a headache, I take aspirin and my headache goes away an hour later, that is not a causal claim,\u201d Mitra told Healio. \u201cWhat I would have to then think about is what would have happened to me had I not taken aspirin? Would my headache still have gone away? Inference relies on these hypothetical, counterfactual situations.\u201d Healio spoke with Mitra about the questions researchers should ask when designing an observational study, which variables to adjust for and why, and the importance of including a credible comparison group. DPEC, hosted by Penn\u2019s Center for Clinical Sciences in Dermatology and led by Healio Dermatology Chief Medical Editor Joel M. Gelfand, MD, MSCE, FAAD, is an academic-industry collaborative program that promotes continuing education for professionals working on clinical research. Healio: What is a causal question? Mitra: The causal question compares two potential outcomes for the same patient. For example, an outcome with treatment vs. an outcome without treatment. The difference between these two outcomes is the causal effect for that patient; the unobserved alternative is the counterfactual. Most clinical questions \u2014 Does this treatment work? Does it cause harm? Would another treatment have been better? \u2014 are causal questions. The problem is we can never observe both outcomes for one person at a particular point in time. People sometimes confuse causation with association. Often, you find there is an association or correlation between a medication and a particular outcome, but that does not necessarily mean that the medication caused that outcome. Because we cannot observe the counterfactual, we must estimate it. We look for patients who can tell us what might have happened otherwise. The comparison group must be a credible stand-in for the missing counterfactual. Healio: During your talk, you referred to a study that assessed whether GLP-1 receptor agonists cause hair loss , as reports of alopecia have increasingly appeared in real-world cohorts. What were you trying to convey with that example? Mitra: I picked the topic because it is timely and relevant. The data seem to say yes. A systematic review and meta-analysis showed 40% higher odds for nonscarring alopecia with GLP-1 use. Should we believe it? When you are conducting these types of observational studies, you cannot randomize people who receive a GLP-1 vs. those who do not just to assess hair loss. Were the patients in the comparison group comparable to begin with? Before treatment, they may differ in obesity severity, age and sex, health-seeking behaviors, healthcare use, nutritional status and use of other medications. This is confounding. Simply adjusting for all the different factors that you have collected does not necessarily mean you now have a causal effect. Healio: What should researchers be adjusting for? What should they not be adjusting for? Mitra: In my talk, I described what a confounder is. A confounder is a common cause of both the treatment and the outcome. People who start GLP-1s may be different from those who do not. These differences confound the association between GLP-1 use and hair loss. If you don\u2019t adjust for confounders, you could end up with a spurious association between the treatment and the outcome. So, we do want to adjust for confounders. However, you may not have measured all confounders so your study may still be susceptible to unmeasured confounding. In addition, there are other types of variables you must think about very carefully. There are mediators, variables that occur after treatment and are on the causal pathway to an outcome. If certain variables result from GLP-1 use, we often do not want to adjust for that if we want to estimate the total effect of GLP-1s on the outcome. Weight loss, for example, is a mediator. If we adjust for weight loss, we are no longer estimating the total effect of GLP-1 treatment. We have changed the causal question. But we might be interested in the indirect effect of weight loss. In those cases, we might want to then conduct a different type of analysis to assess that mediated effect. Colliders are a common effect of treatment and outcome, and we usually do not adjust for those variables. Selection bias is an example. Healio: How can researchers avoid selection bias? Mitra: Continuing with this example, those taking GLP-1s may be more likely to see a dermatologist. Those who have hair loss may be more likely to see a dermatologist. If you subset to investigating only patients who saw a dermatologist, that is selection bias or conditioning on a collider. By doing this, you might end up with a population of people that are not generalizable. Importantly, a statistician cannot make these decisions in a vacuum. 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