title: Biomarker driven population enrichment for adaptive oncology trials with time to event endpoints.
abstract: The development of molecularly targeted therapies for certain types of cancers has led to the consideration of population enrichment designs that explicitly factor in the possibility that the experimental compound might differentially benefit different biomarker subgroups. In such designs, enrollment would initially be open to a broad patient population with the option to restrict future enrollment, following an interim analysis, to only those biomarker subgroups that appeared to be benefiting from the experimental therapy. While this strategy could greatly improve the chances of success for the trial, it poses several statistical and logistical design challenges. Because late-stage oncology trials are typically event driven, one faces a complex trade-off between power, sample size, number of events, and study duration. This trade-off is further compounded by the importance of maintaining statistical independence of the data before and after the interim analysis and of optimizing the timing of the interim analysis. This paper presents statistical methodology that ensures strong control of type 1 error for such population enrichment designs, based on generalizations of the conditional error rate approach. The special difficulties encountered with time-to-event endpoints are addressed by our methods. The crucial role of simulation for guiding the choice of design parameters is emphasized. Although motivated by oncology, the methods are applicable as well to population enrichment designs in other therapeutic areas.
authors.affiliation: Cytel Corporation, Cambridge MA, U.S.A.
authors.forename: Cyrus
authors.initials: C
authors.lastname: Mehta
authors.forename: Helmut
authors.initials: H
authors.lastname: Schäfer
authors.forename: Hanna
authors.initials: H
authors.lastname: Daniel
authors.forename: Sebastian
authors.initials: S
authors.lastname: Irle
mesh_terms: D015415:Biomarkers / Q000032:analysis*; D002289:Carcinoma, Non-Small-Cell Lung / Q000188:drug therapy; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D023381:Endpoint Determination; D066246:ErbB Receptors / Q000097:blood / Q000235:genetics; D006801:Humans; D008175:Lung Neoplasms / Q000188:drug therapy; D018579:Patient Selection; D012107:Research Design; D016019:Survival Analysis
chemical_list: D015415:Biomarkers; D066246:ErbB Receptors
keywords: adaptive design; clinical trial; conditional error function; multiple comparisons; precision medicine; predictive biomarker; subgroup selection; survival endpoints; targeted therapies