A novel approach for developing and interpreting treatment moderator profiles in randomized clinical trials.

Journal Title: 
Publication Date: 
Nov 07, 2013

IMPORTANCE: 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.

OBJECTIVE: 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.

RESULTS: 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.