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.

翻译：中介分析已在独立数据中得到全面研究，但对于相关数据，尤其是日益广泛采用的阶梯式楔形群组随机试验（SW-CRTs），相关研究相对较少。受实效性与实施科学临床试验中效应机制解析挑战的启发，我们开发了适用于SW-CRTs的中介分析新方法。具体而言，基于线性与广义线性混合模型，我们阐明了如何在具有四种数据类型的典型SW-CRTs中估计自然间接效应与中介比例，涵盖连续型和二分类的中介变量与结局变量。此外，为应对暴露时间处理效应异质性这一新兴挑战，我们推导了当总效应随暴露时间函数变化时SW-CRTs中的中介表达式。针对所有数据类型与处理效应结构，我们采用群组刀切法进行统计推断。通过大量模拟研究评估了所提中介估计量在有限样本下的性能，并在实际数据案例中验证了所提方法的有效性。为便于估计量的实际应用，我们开发了用户友好的R软件包mediateSWCRT。