Assessing mediation in cross-sectional stepped wedge cluster randomized trials

TL;DR

This paper develops new statistical methods for mediation analysis in stepped wedge cluster randomized trials, addressing correlated data and treatment effect heterogeneity, with practical implementation tools.

Contribution

It introduces novel mediation analysis techniques for SW-CRTs using mixed models, including handling heterogeneity and providing an R package for implementation.

Findings

Effective estimation of natural indirect effects demonstrated through simulations.

Method successfully applied to real SW-CRT data example.

Proposed estimators show good finite-sample performance.

Abstract

Mediation analysis has been comprehensively studied for independent data but relatively little work has been done for correlated data, especially for the increasingly adopted stepped wedge cluster randomized trials (SW-CRTs). Motivated by challenges in underlying the effect mechanisms in pragmatic and implementation science clinical trials, we develop new methods for mediation analysis in SW-CRTs. Specifically, based on a linear and generalized linear mixed models, we demonstrate how to estimate the natural indirect effect and mediation proportion in typical SW-CRTs with four data types, including both continuous and binary mediators and outcomes. Furthermore, to address the emerging challenges in exposure-time treatment effect heterogeneity, we derive the mediation expressions in SW-CRTs when the total effect varies as a function of the exposure time. The cluster jackknife approach is considered for inference across all data types and treatment effect structures. We conduct extensive simulations to evaluate the finite-sample performances of proposed mediation estimators and demonstrate the proposed approach in a real data example. A user-friendly R package mediateSWCRT has been developed to facilitate the practical implementation of the estimators.