P08 Bayesian Meta-Analysis of Predictive Biomarker Studies using Aggregate Data and Individual Participant Data

Predictive biomarkers are instrumental in forecasting therapeutic outcomes. For instance, PD-L1 has been recognized as a key predictor of success in immunotherapy, where patients with high PD-L1 expression are more likely to respond to immune checkpoint inhibitors. The predictive ability of biomarkers is often assessed across multiple studies, but meta-analysis is challenging due to the variability in cut-points used to dichotomize biomarkers into categorical groups in these studies. Multivariate meta-analysis can help synthesize evidence from such studies, elucidating the relationship between predictive biomarkers and treatment effects. Typically, this analysis uses aggregate data (AD), which may lack sufficient data points. Accessing individual participant data (IPD) can enhance synthesis across studies, but researchers may face challenges. Our goal is to develop Bayesian modeling strategies that use both IPD and AD for more efficient data synthesis, aimed at estimating predictive effects for time-to-event outcomes within a nonlinear dose-response context, specifically using the four-parameter log-logistic model. We propose a one-stage Bayesian meta-analysis model that integrates IPD and AD within a unified synthesis model, allowing both data types to contribute to the estimation of all key model parameters (i.e., the minimum, maximum, ED50, and slope). Simulations are conducted to assess the performance of the proposed approach, suggesting that it would improve the accuracy of estimates of the predictive effects compared to the conventional AD-only approach.

Meta-Analysis

Aggregate Data

Individual Participant Data

Predictive Biomarker 

Other

ASA Biopharmaceutical Section Regulatory-Industry Statistics Workshop 2024