title: Interim decision-making strategies in adaptive designs for population selection using time-to-event endpoints.
abstract: Adaptive designs in oncology clinical trials with interim analyses for population selection could be used in the development of targeted therapies if a predefined biomarker hypothesis exists. In this article, we consider an interim analysis using overall survival (OS), progression-free survival (PFS), and both OS and PFS, to determine whether the whole population or only the biomarker-positive population should continue into the subsequent stage of the trial, whereas the final decision is made based on OS data only. In order to increase the probability of selecting the most appropriate population at the interim analysis, we propose an interim decision-making strategy in adaptive designs with correlated endpoints considering the post-progression survival (PPS) magnitudes. In our approach, the interim decision is made on the basis of predictive power by incorporating information on OS as well as PFS to supplement the incomplete OS data. Simulation studies assuming a targeted therapy demonstrated that our interim decision-making procedure performs well in terms of selecting the proper population, especially under a scenario in which PPS affects the correlation between OS and PFS.
authors.affiliation: a Department of Biomedicai Statistics and Bioinformatics , Kyoto University Graduate School of Medicine , Kyoto , Japan.
authors.forename: Ryuji
authors.initials: R
authors.lastname: Uozumi
authors.affiliation: b Department of Management Science, Graduate School of Engineering , Tokyo University of Science , Tokyo , Japan.
authors.forename: Chikuma
authors.initials: C
authors.lastname: Hamada
mesh_terms: D002986:Clinical Trials as Topic; D003657:Decision Making; D018572:Disease-Free Survival; D006801:Humans; D008495:Medical Oncology; D011336:Probability; D012107:Research Design
keywords: Adaptive design; interim decision-making; population selection; post-progression survival; semi-competing risks; time-to-event endpoints