Improving Interim Analysis Results using Bayesian Methods

Music City Center 

Group sequential design is commonly used in lengthy clinical trials with predefined interim analyses. These analyses assess current trial data for potential early stopping based on efficacy, futility, or study modifications.. However, the reliability of interim analysis (IA) results and the use of partial data to make consistent decisions across subsequent IAs and the final analysis (FA) are important considerations. Instead of making decisions based solely on the snapshot of IA data, we will apply the empirical Bayesian (EB) approach to enhance our belief in the IA results and use other Bayesian approaches to make more robust estimates for the next IA/FA. Bayesian simulations utilize prior information from data collected before the IA to better estimate treatment effect for subsequent IAs and the FA. We will examine the impact of different trajectories of treatment effect changes on the IA and FA results. Furthermore, we will compare results from Bayesian and traditional frequentist approaches under scenarios such as no treatment change over time and various types of treatment changes over time.

Interim

Bayesian 

Biopharmaceutical Section