title: A two-stage Bayesian design for co-development of new drugs and companion diagnostics.
abstract: Most new drug development in oncology is based on targeting specific molecules. Genomic profiles and deregulated drug targets vary from patient to patient making new treatments likely to benefit only a subset of patients traditionally grouped in the same clinical trials. Predictive biomarkers are being developed to identify patients who are most likely to benefit from a particular treatment; however, their biological basis is not always conclusive. The inclusion of marker-negative patients in a trial is therefore sometimes necessary for a more informative evaluation of the therapy. In this paper, we present a two-stage Bayesian design that includes both marker-positive and marker-negative patients in a clinical trial. We formulate a family of prior distributions that represent the degree of a priori confidence in the predictive biomarker. To avoid exposing patients to a treatment to which they may not be expected to benefit, we perform an interim analysis that may stop accrual of marker-negative patients or accrual of all patients. We demonstrate with simulations that the design and priors used control type I errors, give adequate power, and enable the early futility analysis of test-negative patients to be based on prior specification on the strength of evidence in the biomarker.
authors.affiliation: Biometric Research Branch, National Cancer Institute, 9000 Rockville Pike, Bethesda, MD 20892-7434, USA.
authors.forename: Stella Wanjugu
authors.initials: SW
authors.lastname: Karuri
authors.forename: Richard
authors.initials: R
authors.lastname: Simon
mesh_terms: D000970:Antineoplastic Agents / Q000494:pharmacology*; D001499:Bayes Theorem; D014408:Biomarkers, Tumor / Q000032:analysis*; D002986:Clinical Trials as Topic / Q000379:methods*; D003198:Computer Simulation; D055808:Drug Discovery / Q000379:methods*; D023381:Endpoint Determination; D006801:Humans; D009369:Neoplasms / Q000737:chemistry / Q000188:drug therapy; D016016:Proportional Hazards Models; D012107:Research Design; D016896:Treatment Outcome
chemical_list: D000970:Antineoplastic Agents; D014408:Biomarkers, Tumor