title: Group sequential enrichment design incorporating subgroup selection.
abstract: An important component of clinical trials in drug development is the analysis of treatment efficacy in patient subgroups (subpopulations). Because of concerns of multiplicity and of the small sample sizes often involved, such analyses can present substantial statistical challenges and may lead to misleading conclusions. As a confirmatory seamless phase II/III design, we propose an adaptive enrichment group sequential procedure whereby resources can be concentrated on subgroups most likely to respond to treatment. Stopping boundaries are defined through upper and lower spending functions. The procedure is presented in terms of the efficient score, enabling the analysis of normal, binary, or time-to-event data. It addresses the dilution effect by eliminating populations at the first stage that appear likely to derive no therapeutic benefit. It subsequently proceeds with the definitive assessment of treatment efficacy among the remaining pooled populations using a group sequential design. The procedure provides strong protection of familywise type I error rate, and we employ a bootstrap algorithm to obtain point and interval estimates that are adjusted for the selection bias. We give examples to demonstrate how the design is used. We make comparisons with adaptive two-stage combination test procedures and with a group sequential test that does not account for the presence of subgroups. Numerical results show that the procedure has high power to detect subgroup-specific effects and the use of multiple interim analysis points can lead to substantial sample size savings.
authors: Novartis Pharma AG, Basel, Switzerland. baldur.magnusson@novartis.com
authors: Baldur P
authors: BP
authors: Magnusson
authors: Bruce W
authors: BW
authors: Turnbull
mesh_terms: D000465:Algorithms; D001943:Breast Neoplasms / Q000188:drug therapy; D017321:Clinical Trials, Phase I as Topic / Q000379:methods*; D017326:Clinical Trials, Phase III as Topic / Q000379:methods*; D005260:Female; D006801:Humans; D015233:Models, Statistical; D012107:Research Design; D018401:Sample Size; D016896:Treatment Outcome