Targeted Data Fusion for Region-Specific Survival Effects in the AMP HIV Prevention Trials

The Antibody Mediated Prevention (AMP) trials opened a new scientific frontier by showing that passively administered monoclonal broadly neutralizing antibodies (bnAbs) could prevent HIV-1 acquisition. The trials enrolled 4,611 participants across four epidemiologically distinct regions, with 174 HIV-1 diagnoses observed during follow-up. Regional heterogeneity in treatment efficacy, together with privacy and regulatory limits on central data pooling, calls for methods that borrow strength across regions without sharing individual-level data. To estimate region- and treatment-specific survival curves under distributional heterogeneity, we develop a federated learning approach that combines site-specific estimators via an L1-regularized criterion that downweights data sources not aligned with the target. We further extend the framework to a general class of causal contrasts, including the survival difference, survival ratio, and restricted mean survival time difference. In the AMP analysis, for example, the standard error of the day-148 survival difference decreased from 0.007 using the target-only estimator to 0.004 using the federated estimator. For women in South Africa, the region-specific analyses provided no strong evidence of an overall VRC01 benefit, consistent with the primary AMP findings. Overall, the proposed approach provides region-adaptive inference with improved precision without sharing individual-level data, while preserving estimation of region-specific treatment effects under cross-region heterogeneity.