Methods for identification and confirmation of targeted subgroups in clinical trials: A systematic review

Abstract

Important objectives in the development of stratified medicines include the identification and confirmation of subgroups of patients with a beneficial treatment effect and a positive benefit-risk balance. We report the results of a literature review on methodological approaches to the design and analysis of clinical trials investigating a potential heterogeneity of treatment effects across subgroups. The identified approaches are classified based on certain characteristics of the proposed trial designs and analysis methods. We distinguish between exploratory and confirmatory subgroup analysis, frequentist, Bayesian and decision-theoretic approaches and, last, fixed-sample, group-sequential, and adaptive designs and illustrate the available trial designs and analysis strategies with published case studies.

Group Sequential and Confirmatory Adaptive Designs in Clinical Trials

“…As an essential feature, this restriction is not required for the procedure described here (see also, Rosenblum 2015). A systematic review of (also exploratory) procedures for enrichment designs is provided in Ondra et al (2016).…”

Design and estimation in clinical trials with subpopulation selection

“…√ K∕k and C l k is a certain function of k. In addition, the calculations in (5) assumes that the futility bounds are binding. For nonbinding bounds, one can simply set the lower bounds to −∞.…”

“…An overview of different multiple testing approaches for this purpose is given in the work of Alosh et al and the references therein. A single‐stage design with one biomarker tests, for example, the null hypotheses, ie, the treatment effect of the full population is zero, ie, H 0 F and the treatment effect in the subgroup of interest is zero, ie, H 0 S . These designs are usually employed for exploratory subgroup analysis in phase II (ie, to identify an interesting subgroup) or for confirmatory subgroup analysis in phase III, examining the treatment benefit of prespecified subgroups.…”

“…A single-stage design with one biomarker tests, for example, the null hypotheses, ie, the treatment effect of the full population is zero, ie, H 0F and the treatment effect in the subgroup of interest is zero, ie, H 0S . 5,[8][9][10][11][12] These designs are usually employed for exploratory subgroup analysis in phase II (ie, to identify an interesting subgroup) or for confirmatory subgroup analysis in phase III, examining the treatment benefit of prespecified subgroups. Corresponding multistage designs are constructed either as extensions of group sequential approaches 13 or using combination tests.…”

Confidence interval estimation for the changepoint of treatment stratification in the presence of a qualitative covariate‐treatment interaction

“…Selection or identification of optimal treatment regimes for individual patients in an evidence‐based manner is currently discussed. The literature provides proposals for study designs to investigate differential treatment effects and statistical methods for identification of relevant predictive markers or subgroups benefiting from different treatments …”