Bayesian survival analysis for early detection of treatment effects in phase 3 clinical trials.

ABSTRACT: Despite appealing characteristics for the clinical trials setting, Bayesian inference methods remain scarcely used, especially in randomized controlled clinical trials (RCT). This is particularly true when dealing with a survival endpoint, likely due to the additional complexities to model specifications. We propose to use Bayesian inference to estimate the treatment effect in this setting, using a proportional hazards (PH) model for right-censored data. Implementation of such an estimation process is illustrated on two working examples from cancer RCTs, the ALLOZITHRO and the CLL7-SA trials, both originally analyzed using a frequentist approach. In these two different settings, we show that Bayesian sequential analyses can provide early insight on treatment effect in RCTs. Relying on post

SUBMITTER: Biard L 

PROVIDER: S-EPMC7817368 | biostudies-literature | 2021 Mar

REPOSITORIES: biostudies-literature