P07 Bayesian Dynamic Borrowing for Nonparametric Survival Analysis

In certain circumstances (pediatrics, rare disease, early phase) it is becoming increasingly common to use Bayesian Dynamic Borrowing (BDB) to incorporate prior information from one or more data sources in the design of clinical trials of analysis of clinical trial data. Covariate adjusted BDB methods have been proposed and accepted for this analysis which will be based on an ORR endpoint. However, BDB for survival analysis is less well-studied when progression free survival (PFS) is the key endpoint. Existing work on BDB for survival endpoints has typically focused on the use of parametric survival models, such as Weibull proportional hazard model. When the proportional hazard assumption is violated, the results from such models are most likely biased.

We propose a formal non-parametric Bayesian survival analysis, which utilizes a Dirichlet Process Mixture Model (DPMM). Such an approach extends the idea of using a prior distribution on a parameter in a parametric model to using a prior distribution on the CDF that governs the time-to-event distribution. DPMMs are easily implemented using stan through their finite mixture approximation. We observed reduction in bias while providing comparable rMSE as with other methods. In addition, use of DPMMs offers a flexible alternative, allowing nonparametric survival analysis with or without dynamic borrowing from historical data.​

Clinical Trial Conduct and Analysis Tools (e.g., Monitoring, Operations, Visualization)

ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop 2024