BayesCT: a Tool for Simulation and Analysis of Adaptive Bayesian Clinical Trials

• Activity Number: 470 - Biomarker Evaluation and Winning Student Papers on Medical Devices and Diagnostics
• Type: Contributed
• Date/Time: Wednesday, July 31, 2019 : 8:30 AM to 10:20 AM
• Sponsor: Section on Medical Devices and Diagnostics
• Author(s): Thevaa Chandereng* and Donald Musgrave and Tarek Haddad and Graeme Hickey and Tim Hanson and Theodore Lystig and Rick Chappell
• Companies: University of Wisconsin-Madison and Medtronic and Medtronic and Medtronic and Medtronic and Medtronic and University of Wisconsin-Madison
• Keywords: early stopping; adaptive design; historical data

Abstract

Adaptive Bayesian clinical trials have increased in popularity in recent years due to the significant flexibility they offer over conventional clinical trials. We present the R package bayesCT for the design and analysis of adaptive Bayesian trials for binomial, gaussian, and time-to-event data types. The package enables early stopping for futility or success via interim analyses, allowing trials to stop or continue enrollment based on the posterior distribution of the difference between treatment and control, thus reducing patient cost and exposure to potentially inferior treatments. We use novel and efficient Monte Carlo methods for estimating Bayesian posterior probabilities, evaluation of loss to follow up, and imputation of incomplete data. The package has the functionality for dynamically incorporating historical data into the analysis via the power prior or non-informative priors. Trial simulation can be carried out using parallel computing to reduce processing time. The bayesCT R package is available at https://thevaachandereng.github.io/bayesCT/.