Optimized multiple testing procedures for nested sub-populations based on a continuous biomarker. (October 2020)

• Record Type: Journal Article
• Authors: Graf, Alexandra Christine; Magirr, Dominic; Dmitrienko, Alex; Posch, Martin
• Is Part Of: Statistical methods in medical research. Volume 29:Number 10(2020)
• Page Start: 2945
• Page End: 2957
• Publication Date: 2020-10
• Subjects: Nested subgroups -- subgroup analysis -- group sequential design -- multiple testing -- biomarker; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280220913071
• Languages: English
• ISSNs: 0962-2802

Abstract

An important step in the development of targeted therapies is the identification and confirmation of sub-populations where the treatment has a positive treatment effect compared to a control. These sub-populations are often based on continuous biomarkers, measured at baseline. For example, patients can be classified into biomarker low and biomarker high subgroups, which are defined via a threshold on the continuous biomarker. However, if insufficient information on the biomarker is available, the a priori choice of the threshold can be challenging and it has been proposed to consider several thresholds and to apply appropriate multiple testing procedures to test for a treatment effect in the corresponding subgroups controlling the family-wise type 1 error rate. In this manuscript we propose a framework to select optimal thresholds and corresponding optimized multiple testing procedures that maximize the expected power to identify at least one subgroup with a positive treatment effect. Optimization is performed over a prior on a family of models, modelling the relation of the biomarker with the expected outcome under treatment and under control. We find that for the considered scenarios 3 to 4 thresholds give the optimal power. If there is a prior belief on a small subgroup where the treatment has a positive effect, additional optimization of the spacing of thresholds may result in a large benefit. The procedure is illustrated with a clinical trial example in depression.