Monitoring Clinical Studies using Generalized Bayesian Predictive Probability

Music City Center 

For clinical studies with multiple clinical endpoints that could contribute to the risk-benefit profile of the product evaluation, it would be desirable to monitor those primary endpoints simultaneously at interim analysis. Dmitrienko and Wang (2006) introduced Bayesian predictive probability which is used for interim decisions including efficacy and futility stopping rules. In this paper, we will expand the application of Bayesian predictive probability to the cases with multiple primary endpoints.

The Bayesian predictive probability is defined as the probability of successful outcomes at the planned completion of the study conditional on the observed data up to the time at interim analysis and predicted data. We consider the case with multiple primary endpoints which are assumed to have a multivariate normal distribution with different mean vectors in each treatment group, and the mean vector has a multivariate normal prior. In this case, the generalized predictive probability (GPP) can be calculated using multivariate normal function in SAS or R program. Some examples will be presented to show how to calculate GPP and make interim decisions.

Predictive Probability

Predictive distribution

Co-primary endpoints

Interim Analysis

Stopping rules 

Biopharmaceutical Section