Effect of Predictive Performance of a Biomarker for the Sample Size of Targeted Designs for Randomized Clinical Trials

Abstract

Targeted designs based on the use of predictive biomarkers for patient enrollment have been used to increase the study efficiency. We consider the problem of efficiency of targeted design when the targeted subgroup is defined by a predictive biomarker or classifier. Here we incorporate the predictive performance of a biomarker or classifier to predict a responsive subgroup in the sample size evaluation for targeted design. Predictive performance metrics such as PPV and NPV of a predictive biomarker can usually be best estimated from preclinical studies, such as cell line panel screening. In this article, we focus on sample size calculations for clinical trial studies with time-to-event endpoint. Different assumptions on the treatment and control effects for the subgroups of patients are assumed. Patients’ accrual rate and losses to follow-up are incorporated in the sample size calculation. Simulations are used to check the performance of the sample size formulas. Also, the efficiency of a targeted versus untargeted design is evaluated using cohort size ratio and screened patient ratio. In these studies, the efficiency depends primarily on the prevalence of the subgroup targeted for trial enrollment, the treatment and control effects in subgroups defined by the biomarkers, PPV and NPV.