Sample Size Re-Estimation in Multi-Center Oncology Trials with Time-to-Event Endpoint Using Bayesian Enrollment Timeline Projection and Clinical Event Prediction

• Activity Number: 595
• Type: Topic Contributed
• Date/Time: Thursday, August 7, 2014 : 8:30 AM to 10:20 AM
• Sponsor: Korean International Statistical Society
• Author(s): Sunhee Ro*+ and Mihaela Obreja and Anthiyur Kannappan
• Companies: Onyx Pharmaceuticals and Onyx Pharmaceuticals and Cytel
• Keywords: Oncology ; time to event endpoint ; Bayesian event timeline projection ; enrollment projection ; survival analysis ; Frequentist event timeline projection

Abstract

In a time to event study, the defining element of accrual goal is to achieve the target number of events, rather than a planned sample size. It is known that sample size re-estimating based on revised estimate of event rate from blinded analysis of aggregate study data enhances efficiency with limited risk of introducing bias/impairing interpretability (FDA Guidance for Industry, 2010). Cook (2003) used Markov Chain method to estimate the probability for a subject to be in event state or treatment state at a given survival time point using either parametric (Weibull) or empirical per-time point hazard rates. These probabilities are combined with actual and future enrollments to generate the number of subjects in event state at a given "study" time point thus guiding the sample size modification decision. We propose to use Bayesian Poisson-Gamma model to predict future enrollments (Nitin et al 2014) in applying above method. An application example using actual data will be presented with the comparison/discussion of using parametric vs. empirical per time point hazard rates in the algorithm. The extension of this method including the lost to follow up state will be also discussed.