Leveraging historical data to compute predictive probability of success using Dirichlet Process prio

Oregon Convention Center 

Predictive probability of success, or PPoS, is a crucial decision-making tool that predicts trial success and is computed at various phases of the drug development process. We propose a Dirichlet Process meta-analytic prior (DP-MAP), a non-parametric approach to account for the statistical heterogeneity among the treatment effects across all the historical studies considered for constructing an informative prior, for calculating PPoS. It allows for a more robust inference in the case of prior-data conflict. As the basic premise is to borrow only if the historical information is relevant, some prior trials may concur with or disagree with the current data. DP provides a flexible solution; that is, DP offers the chance to borrow from earlier trials based on their similarity with the current trial and resolves the prior data conflict.
In this paper, we assess the model fit of DP-MAP prior and compare it with the model fit for both the standard meta-analytic predictive prior (MAP) and robust-meta-analytic prior (rMAP) approaches. We utilize a real data example from historical RRMM trials and demonstrate PPoS calculations at the design stage and interim analysis of the ongoing trial.

Dirichlet process prior

Predictive probability of success

interim analysis

clinical trials

Bayesian statistics

go-no go decision 

Biopharmaceutical Section