Statistical Analysis Plan for Stage 1 EMBARC (Establishing Moderators and Biosignatures of Antidepressant Response for Clinical Care) Study

Eva Petkova, R Todd Ogden, Thaddeus Tarpey, Adam Ciarleglio, Bei Jiang, Zhe Su, Thomas Carmody, Philip Adams, Helena C Kraemer, Bruce D Grannemann, Maria A Oquendo, Ramin Parsey, Myrna Weissman, Patrick J McGrath, Maurizio Fava, Madhukar H Trivedi, Eva Petkova, R Todd Ogden, Thaddeus Tarpey, Adam Ciarleglio, Bei Jiang, Zhe Su, Thomas Carmody, Philip Adams, Helena C Kraemer, Bruce D Grannemann, Maria A Oquendo, Ramin Parsey, Myrna Weissman, Patrick J McGrath, Maurizio Fava, Madhukar H Trivedi

Abstract

Antidepressant medications are commonly used to treat depression, but only about 30% of patients reach remission with any single first-step antidepressant. If the first-step treatment fails, response and remission rates at subsequent steps are even more limited. The literature on biomarkers for treatment response is largely based on secondary analyses of studies designed to answer primary questions of efficacy, rather than on a planned systematic evaluation of biomarkers for treatment decision. The lack of evidence-based knowledge to guide treatment decisions for patients with depression has lead to the recognition that specially designed studies with the primary objective being to discover biosignatures for optimizing treatment decisions are necessary. Establishing Moderators and Biosignatures of Antidepressant Response in Clinical Care (EMBARC) is one such discovery study. Stage 1 of EMBARC is a randomized placebo controlled clinical trial of 8 week duration. A wide array of patient characteristics is collected at baseline, including assessments of brain structure, function and connectivity along with electrophysiological, biological, behavioral and clinical features. This paper reports on the data analytic strategy for discovering biosignatures for treatment response based on Stage 1 of EMBARC.

Keywords: combining biomarkers; differential treatment response index; moderator; optimizing treatment decisions; precision medicine.

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