Center-specific causal inference with multicenter trials—Interpreting trial evidence in the context of each participating center

In multicenter randomized trials, when effect modifiers have a different distribution across centers, comparisons between treatment groups that average (standardize) effects over centers may not apply to any of the populations underlying the individual centers. In the presence of such heterogeneity, interpreting the evidence produced by a multicenter trial in the context of the local population underlying each center may be necessary. Here, we identify center-specific effects under conditions that are largely supported by the study design and are weaker than those underlying popular methods for the analysis of multicenter studies, in the presence of associations between center membership and the outcome ("center-outcome associations" conditional on baseline covariates and treatment). We then consider an additional testable condition of "no center-outcome associations," given baseline covariates and treatment. We propose methods for estimating center-specific average treatment effects, when center-outcome associations are present and when they are absent. When center-outcome associations are absent, we discuss how the proposed methods are often more efficient and make weaker conditions than related transportability methods applied to multicenter trials. We evaluate the performance of the methods in a simulation study and illustrate their implementation using data from the Hepatitis C Antiviral Long-Term Treatment Against Cirrhosis trial.