Performance of Biomarker-Based Subgroup Selection Rules in Adaptive Enrichment Designs

Abstract

In the planning stage of a clinical trial investigating a potentially targeted therapy, there is commonly a high degree of uncertainty whether the treatment is more efficient (or efficient only) in a subgroup compared to the whole population. Recently developed adaptive designs enable to allow for an efficacy assessment both for the whole population and a subgroup and to select the target population mid-course based on interim results (see, e.g., Wang et al., Pharm Stat 6:227–244, 2007, Brannath et al., Stat Med 28:1445–1463, 2009, Wang et al., Biom J 51:358–374, 2009, Jenkins et al., Pharm Stat 10:347–356, 2011, Friede et al., Stat Med 31:4309–4120, 2012). Frequently, predictive biomarkers are used in these trials for identifying patients more likely to benefit from a drug. We consider the situation that the selection of the patient population is based on a biomarker and where the diagnostics that evaluates the biomarker may be perfect, i.e., with 100 % sensitivity and specificity, or not. The performance of the applied subset selection rule is crucial for the overall characteristics of the design. In the setting of an adaptive enrichment design, we evaluate the properties of subgroup selection rules in terms of type I error rate and power by taking into account decision rules with a fixed ad hoc threshold and optimal decision rules developed for the situation of uncertain assumptions. In a simulation study, we demonstrate that designs with optimal decision rules are under certain assumptions more powerful as compared to those with ad hoc decision rules. Throughout the results, a strong impact of sensitivity and specificity of the biomarker on both type I error rate and power is observed.