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In multicenter randomized trials, when effect modifiers have a differentdistribution across centers, comparisons between treatment groups that averageover centers may not apply to any of the populations underlying the individualcenters. Here, we describe methods for reinterpreting the evidence produced bya multicenter trial in the context of the population underlying each center. Wedescribe how to identify center-specific effects under identifiabilityconditions that are largely supported by the study design and when associationsbetween center membership and the outcome may be present, given baselinecovariates and treatment ("center-outcome associations"). We then consider anadditional condition of no center-outcome associations given baselinecovariates and treatment. We show that this condition can be assessed using thetrial data; when it holds, center-specific treatment effects can be estimatedusing analyses that completely pool information across centers. We proposemethods for estimating center-specific average treatment effects, whencenter-outcome associations may be present and when they are absent, anddescribe approaches for assessing whether center-specific treatment effects arehomogeneous. We evaluate the performance of the methods in a simulation studyand illustrate their implementation using data from the Hepatitis C AntiviralLong-Term Treatment Against Cirrhosis trial.

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