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

TL;DR

This paper develops methods to interpret and estimate treatment effects specific to each center in multicenter trials, accounting for differences in effect modifiers and center-outcome associations, enhancing personalized understanding of trial evidence.

Contribution

It introduces novel statistical methods for center-specific causal inference in multicenter trials, addressing heterogeneity and center-outcome associations, with practical implementation guidance.

Findings

Methods accurately estimate center-specific effects under various conditions.

Simulation shows methods perform well in diverse scenarios.

Application to Hepatitis C trial demonstrates practical utility.

Abstract

In multicenter randomized trials, when effect modifiers have a different distribution across centers, comparisons between treatment groups that average over centers may not apply to any of the populations underlying the individual centers. Here, we describe methods for reinterpreting the evidence produced by a multicenter trial in the context of the population underlying each center. We describe how to identify center-specific effects under identifiability conditions that are largely supported by the study design and when associations between center membership and the outcome may be present, given baseline covariates and treatment ("center-outcome associations"). We then consider an additional condition of no center-outcome associations given baseline covariates and treatment. We show that this condition can be assessed using the trial data; when it holds, center-specific treatment effects can be estimated using analyses that completely pool information across centers. We propose methods for estimating center-specific average treatment effects, when center-outcome associations may be present and when they are absent, and describe approaches for assessing whether center-specific treatment effects are homogeneous. 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.