Collaborative Indirect Treatment Comparisons with Multiple Distributed Single-arm Trials

2025-09-28

AI Summary

In settings where randomized controlled trials (RCTs) are infeasible—such as rare disease research and early-phase drug development—this paper proposes Distributed Augmented Calibration weighting (DAC), a federated method enabling fair indirect treatment comparisons across heterogeneous populations using only aggregated summary statistics from single-arm trials across multiple centers. DAC integrates calibration weighting to balance covariate distributions, flexibly models confounding effects, and synchronously estimates multiple average treatment effects via iterative inter-center communication, ensuring double robustness and asymptotic unbiasedness—its theoretical properties are equivalent to those of centralized analysis. Extensive simulations and real-world case studies demonstrate that DAC maintains high accuracy and stability under challenging conditions—including small sample sizes, high inter-site heterogeneity, and non-overlapping covariates—thereby substantially improving the reliability and generalizability of multi-treatment efficacy evaluation. DAC establishes a trustworthy, privacy-preserving paradigm for distributed evidence synthesis to support regulatory decision-making and clinical translation.

Technology Category

• Machine Learning: Distributed Machine Learning & Federated Learning
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization
• Game Theory and Economic Paradigms: Fair Division

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Economics, Online Markets and Human Computation: Fairness, privacy, and diversity in economic environments
• Systems and Infrastructure for Web, Mobile and WoT: Federated Web and WoT systems, including distributed, federated and edge-based data processing

Abstract

When randomized controlled trials are impractical or unethical to simultaneously compare multiple treatments, indirect treatment comparisons using single-arm trials offer valuable evidence for health technology assessments, especially for rare diseases and early-phase drug development. In practice, each sponsor conducts a single-arm trial on its own drug with restricted data-sharing and targets effects in its trial population, which can lead to unfair comparisons. This motivates methods for fair treatment comparisons across a range of target populations in distributed networks of single-arm trials sharing only aggregated data. Existing federated methods, which assume at least one site contains all treatments and allow pooling of treatment groups within the same site, cannot address this problem. We propose a novel distributed augmented calibration weighting (DAC) method to simultaneously estimate the pairwise average treatment effects (ATEs) across all trial population combinations in a distributed network of multiple single-arm trials. Using two communication rounds, DAC estimators balance covariates via calibration weighting, incorporate flexible nuisance parameter estimation, achieve doubly robust consistency, and yield results identical to pooled-data analysis. When nuisance parameters are estimated parametrically, DAC estimators are enhanced to achieve doubly robust inference with minimal squared first-order asymptotic bias. Simulations and a real-data application show good performance.

Problem

• Enabling fair treatment comparisons across distributed single-arm trials
• Addressing bias in indirect comparisons with restricted data sharing
• Estimating pairwise treatment effects without pooling individual patient data

Innovation

• Distributed augmented calibration weighting method
• Balances covariates via calibration weighting
• Achieves doubly robust consistency and inference