Bayesian response adaptive randomization design with a composite endpoint of mortality and morbidity

If treatment allocation of patients during a trial is based on the observed responses accumulated prior to the decision point and can be adapted sequentially, it could minimize the expected number of failures or maximize patients' total benefits. In this study, we developed a Bayesian response-adaptive randomization (RAR) design targeting the endpoint of organ support-free days (OSFD) for patients admitted to the intensive care units (ICU). The OSFD is a mixture of mortality and morbidity assessed by the number of days of free of organ support. In the past, researchers treated OSFD as an ordinal outcome variable that the lowest category is the ICU death. We propose a novel RAR design for a composite endpoint of mortality and morbidity, e.g., OSFD, by using a Bayesian mixture model with a Markov chain Monte Carlo sampling to estimate the posterior probability of OSFD and determine treatment allocation ratios at each interim. Simulations were conducted to compare the performance of our proposed design under various randomization rules and different alpha spending functions. The results show that our RAR design using Bayesian inference has lower death rate while assuring adequate power and type I error rate control for the target trial.