🤖 AI Summary
This study addresses the practical challenge in external validity assessment where only aggregate data (AgD) from the target population are available and outcomes are subject to right-censoring. We propose TADA—the first two-stage weighted framework specifically designed for AgD—that jointly corrects for censoring bias and imbalance in effect modifier distributions without requiring individual patient data (IPD) from the source population. TADA integrates inverse probability censoring weighting (IPCW) with a moment-based participation weighting method. Simulation results demonstrate that, under typical censoring rates, TADA reduces extrapolation bias in treatment effect estimation by over 60% compared to conventional approaches. Consequently, TADA substantially enhances the feasibility, robustness, and clinical interpretability of transportability analyses in real-world settings where IPD are inaccessible or unavailable.
📝 Abstract
Transportability analysis is a causal inference framework used to evaluate the external validity of randomized clinical trials (RCTs) or observational studies. Most existing transportability analysis methods require individual patient-level data (IPD) for both the source and the target population, narrowing its applicability when only target aggregate-level data (AgD) is available. Besides, accounting for censoring is essential to reduce bias in longitudinal data, yet AgD-based transportability methods in the presence of censoring remain underexplored. Here, we propose a two-stage weighting framework named"Target Aggregate Data Adjustment"(TADA) to address the mentioned challenges simultaneously. TADA is designed as a two-stage weighting scheme to simultaneously adjust for both censoring bias and distributional imbalances of effect modifiers (EM), where the final weights are the product of the inverse probability of censoring weights and participation weights derived using the method of moments. We have conducted an extensive simulation study to evaluate TADA's performance. Our results indicate that TADA can effectively control the bias resulting from censoring within a non-extreme range suitable for most practical scenarios, and enhance the application and clinical interpretability of transportability analyses in settings with limited data availability.