Bayesian Phase II optimization for time-to-event data based on historical information. (April 2019)

• Record Type: Journal Article
• Authors: Bertsche, Anja; Fleischer, Frank; Beyersmann, Jan; Nehmiz, Gerhard
• Is Part Of: Statistical methods in medical research. Volume 28:Number 4(2019)
• Page Start: 1272
• Page End: 1289
• Publication Date: 2019-04
• Subjects: Proof-of-concept -- Go–NoGo decision -- Bayes -- time-to-event -- operating characteristics -- meta-analytic-predictive prior distribution; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280217747310
• Languages: English
• ISSNs: 0962-2802

Abstract

After exploratory drug development, companies face the decision whether to initiate confirmatory trials based on limited efficacy information. This proof-of-concept decision is typically performed after a Phase II trial studying a novel treatment versus either placebo or an active comparator. The article aims to optimize the design of such a proof-of-concept trial with respect to decision making. We incorporate historical information and develop pre-specified decision criteria accounting for the uncertainty of the observed treatment effect. We optimize these criteria based on sensitivity and specificity, given the historical information. Specifically, time-to-event data are considered in a randomized 2-arm trial with additional prior information on the control treatment. The proof-of-concept criterion uses treatment effect size, rather than significance. Criteria are defined on the posterior distribution of the hazard ratio given the Phase II data and the historical control information. Event times are exponentially modeled within groups, allowing for group-specific conjugate prior-to-posterior calculation. While a non-informative prior is placed on the investigational treatment, the control prior is constructed via the meta-analytic-predictive approach. The design parameters including sample size and allocation ratio are then optimized, maximizing the probability of taking the right decision. The approach is illustrated with an example in lung cancer.