Target Aggregate Data Adjustment Method for Transportability Analysis Utilizing Summary-Level Data From the Target...

Transportability analysis is a causal inference framework used to evaluate the external validity of studies by transporting treatment effects from a study sample to an external target population by adjusting for differences in the distributions of their effect modifiers. Most existing 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) are available. For survival analysis, accounting for censoring may be needed to reduce bias, yet AgD-based transportability methods in the presence of informative-censoring remain underexplored. Here, we propose a two-stage weighting framework named "Target Aggregate Data Adjustment" (TADA) that can simultaneously adjust for both censoring bias and distributional imbalances of effect modifiers. In our framework, the final weights are the product of the time-varying 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. We have applied our methods to a real case study on the squamous non-small-cell lung cancer trial (NCT00981058). Our results indicate that TADA can effectively control the bias resulting from moderate censoring representative of most practical scenarios, and enhance the application and clinical interpretability of transportability analyses in settings with limited data availability.