Identifying treatment moderators may help mental health practitioners arrive at more precise treatment selection for individual patients and can focus clinical research on subpopulations that differ in treatment response.
To demonstrate a novel exploratory approach to moderation analysis in randomized clinical trials.
Design, Setting, and Participants
A total of 291 adults from a randomized clinical trial that compared an empirically supported psychotherapy with selective serotonin reuptake inhibitor (SSRI) pharmacotherapy as treatments for depression.
Main Outcomes and Measures
We selected 8 relatively independent individual moderators out of 32 possible variables. A combined moderator, M*, was developed as a weighted combination of the 8 selected individual moderators. M* was then used to identify individuals for whom psychotherapy may be preferred to SSRI pharmacotherapy or vice versa.
Among individual moderators, psychomotor activation had the largest moderator effect size (0.12; 95% CI, <.01 to 0.24). The combined moderator, M*, had a larger moderator effect size than any individual moderator (0.31; 95% CI, 0.15 to 0.46). Although the original analyses demonstrated no overall difference in treatment response, M* divided the study population into 2 subpopulations, with each showing a clinically significant difference in response to psychotherapy vs SSRI pharmacotherapy.
Conclusions and Relevance
Our results suggest that the strongest determinations for personalized treatment selection will likely require simultaneous consideration of multiple moderators, emphasizing the value of the methods presented here. After validation in a randomized clinical trial, a mental health practitioner could input a patient’s relevant baseline values into a handheld computer programmed with the weights needed to calculate M*. The device could then output the patient’s M* value and suggested treatment, thereby allowing the mental health practitioner to select the treatment that would offer the greatest likelihood of success for each patient.