Constructing external comparator groups via transportability in mean or in effect measure

2026-04-21

AI Summary

This study addresses the challenge of constructing high-quality external control arms for target populations using multi-source data to reliably estimate causal effects of alternative treatments. To this end, the authors propose two identification strategies—one based on the transportability of potential outcome means and the other on the transportability of effect measures—and develop a semiparametric, doubly robust augmented weighting estimator. This estimator integrates models for trial participation probability, treatment assignment probability, and conditional outcome mean, maintaining robustness under partial model misspecification. Theoretical analysis establishes its asymptotic efficiency, while simulation studies demonstrate superior finite-sample performance compared to methods relying solely on modeling or weighting. The approach is successfully applied to the ACCEPT and PHOENIX 1 trials, yielding reliable assessments of efficacy differences among biologic therapies for psoriasis.

Technology Category

• Machine Learning: Causal Learning
• Intelligent Robots: State Estimation
• Search and Optimization: Sampling/Simulation-based Search

Application Category

• User Modeling, Personalization and Recommendation: Attacks and countermeasures in recommendation systems
• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

Abstract

Learning about causal effects in target populations and their subsets may be facilitated by combining information from multiple sources. One major class of study designs that combine information involves appending an index study with data from an external comparator, which may facilitate head-to-head comparisons of treatments initially studied in different populations. We delineate external comparator analyses under two distinct, but related, identification strategies. The first strategy relies on exchangeability (transportability) of potential outcome means, which uses information only on the treatments that are to be compared. The second strategy relies on transportability in effect measure, requiring additional use of information on a third treatment common to the populations that have been combined. In a time-fixed setting with a point treatment and non-failure time outcome, we examine identification and estimation under a basic setup where information from an index trial is combined with a second, and external to the index trial, data source. We propose estimators for identifying observed data functionals, with a particular focus on semiparametric efficient augmented weighting estimators that incorporate models for the probability of trial participation, the probability of treatment, and conditional outcome means. We derive the asymptotic properties of these augmented weighting estimators -- including robustness to model misspecification and slower rates of convergence for some nuisance function models -- and use simulation to compare their finite sample performance to estimators based only on outcome modeling or weighting. Last, we provide a practical demonstration of the proposed methods by combining the ACCEPT and PHOENIX 1 randomized trials to evaluate the effect of various biologic agents on plaque psoriasis, a chronic inflammatory disorder.

Problem

• external comparator
• transportability
• causal effect
• target population
• effect measure

Innovation

• transportability
• external comparator
• augmented weighting estimator
• causal inference
• semiparametric efficiency