Adjustment for exploratory cut-off selection in randomized clinical trials with survival endpoint

• Verfasst von: Götte, Heiko [VerfasserIn]
• Verfasst von: Kirchner, Marietta [VerfasserIn]
• Verfasst von: Kieser, Meinhard [VerfasserIn]
• Titel: Adjustment for exploratory cut-off selection in randomized clinical trials with survival endpoint
• Verf.angabe: Heiko Götte, Marietta Kirchner, Meinhard Kieser
• Jahr: 2020
• Jahr des Originals: 2019
• Umfang: 16 S.
• Fussnoten: Gesehen am 17.07.2020 ; First published: 07 October 2019
• Titel Quelle: Enthalten in: Biometrical journal
• Ort Quelle: Berlin : Wiley-VCH, 1977
• Jahr Quelle: 2020
• Band/Heft Quelle: 62(2020), 3, Seite 627-642
• ISSN Quelle: 1521-4036
• DOI: doi:10.1002/bimj.201800302
• Datenträger: Online-Ressource
• Sprache: eng
• Sach-SW: approximate Bayesian computation
• Sach-SW: cut-off selection
• Sach-SW: mixed exponential distribution
• Sach-SW: probability of success
• Sach-SW: time to event
• K10plus-PPN: 1725080966
• K10plus-PPN:
  Universitätsbibliothek
• Lokale URL UB: Zum Volltext

Abstract

Defining the target population based on predictive biomarkers plays an important role during clinical development. After establishing a relationship between a biomarker candidate and response to treatment in exploratory phases, a subsequent confirmatory trial ideally involves only subjects with high potential of benefiting from the new compound. In order to identify those subjects in case of a continuous biomarker, a cut-off is needed. Usually, a cut-off is chosen that resulted in a subgroup with a large observed treatment effect in an exploratory trial. However, such a data-driven selection may lead to overoptimistic expectations for the subsequent confirmatory trial. Treatment effect estimates, probability of success, and posterior probabilities are useful measures for deciding whether or not to conduct a confirmatory trial enrolling the biomarker-defined population. These measures need to be adjusted for selection bias. We extend previously introduced Approximate Bayesian Computation techniques for adjustment of subgroup selection bias to a time-to-event setting with cut-off selection. Challenges in this setting are that treatment effects become time-dependent and that subsets are defined by the biomarker distribution. Simulation studies show that the proposed method provides adjusted statistical measures which are superior to naïve Maximum Likelihood estimators as well as simple shrinkage estimators.