"Most antidepressants today are tested in large, mixed populations. The results reflect the "average" effect across that group. But depression isn't one condition; it's likely many conditions that look similar on the outside but differ biologically. That means a treatment that helps one group might not help another. And when we average the effects together, we may miss the fact that it worked really well for a specific group."
"This is where subgroup analysis comes in. It's a way for researchers to look at how different types of people respond to a treatment based on biology, genetics, or other meaningful traits. Done right, this approach doesn't weaken the science; it strengthens it. It allows us to stop thinking in averages and start thinking in individuals. Take a recent example from a study I was involved in."
"Researchers tested an experimental treatment for major depression, and while the overall results were positive, the most compelling finding was that a particular subgroup, defined by a genetic marker linked to the body's stress system, had a faster and stronger response than everyone else. The difference was seen within the first week and continued to grow over the treatment period."
Antidepressant trials commonly report average effects that mask strong responses in biologically distinct subgroups. Depression likely comprises multiple biologically different conditions that appear similar clinically. Subgroup analysis using genetics, biomarkers, or other meaningful traits can reveal who benefits and who does not. Pre-specified biomarker-guided analyses can identify patients who respond rapidly and robustly, sometimes within the first week. Identifying responsive subgroups can shorten time-to-remission, reduce trial-and-error prescribing, and strengthen rather than weaken trial science. Asking who benefits facilitates targeted treatment, faster clinical improvement, and more efficient therapeutic development.
Read at Psychology Today
Unable to calculate read time
Collection
[
|
...
]