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

Thomas M. Menino Convention & Exhibition Center 

The Antibody Mediated Prevention (AMP) trials opened a new scientific frontier by demonstrating that passively administered monoclonal broadly neutralizing antibodies (bnAbs) could prevent HIV-1 acquisition. Conducted across multiple geographic regions, including the United States, Brazil, Peru, Switzerland, and sub-Saharan Africa, the AMP trials revealed substantial regional heterogeneity in treatment efficacy. These differences, together with privacy and regulatory limits on central data pooling, call for methods that appropriately 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 reweights site-specific estimators via an L1-regularized loss that penalizes data sources not aligned with the target. We showcase our method for a general class of causal contrasts, including the risk difference (RD), survival ratio (SR), and restricted mean survival time (RMST) difference. We conduct extensive simulations and analyze the AMP trials for different target populations, demonstrating that our approach yields region-adaptive and privacy-preserving inference with improved precision.

AMP HIV-1 prevention trials

Time-to-event outcome

Distribution shift

Semiparametric efficiency theory

Federated learning