Sample Size Requirements for Multivariate Models to Predict Between-Patient Differences in Best Treatments of Major Depressive Disorder

Abstract

Clinical trials have documented numerous clinical features, social characteristics, and biomarkers that are “prescriptive” predictors of depression treatment response, that is, predictors of which types of treatments are best for which patients. On the basis of these results, research is actively under way to develop multivariate prescriptive prediction models to guide precision depression treatment planning. However, the sample size requirements for such models have not been analyzed. We present such an analysis here. Simulations using realistic parameter values and a state-of-the-art cross-validated targeted minimum loss-based prescription treatment response estimator show that at least 300 patients per treatment arm are needed to have adequate statistical power to detect clinically significant underlying marginal improvements in treatment response because of precision treatment selection. This is a considerably larger sample size than in most existing studies. We close with a discussion of practical study design options to address the need for larger sample sizes in future studies.