A doubly robust estimator for unanchored indirect treatment comparisons: Development and evaluation using simulated and case-study data

Motivated by the growing use of unanchored indirect treatment comparisons (ITCs) in the absence of head-to-head trials, this paper evaluates and compares methods for estimating treatment effects across two data sources for continuous and binary outcomes. Within the Neyman-Rubin causal framework, we target the population average treatment effect for the treated, which in our setting quantifies the counterfactual difference between receiving treatment 0 and treatment 1 among individuals in Study 1, the population for which individual patient data (IPD) may not be accessible. We review six existing methods based on propensity score weighting and outcome regression and introduce a novel doubly robust (DR) estimator tailored to settings in which IPD is unavailable for the target population. Through a simulation study and a clinical case study, we demonstrate that doubly robust estimators consistently perform well across a range of practical settings. In particular, we recommend the proposed DR estimator for unavailable IPD settings.