Background: Stepped wedge cluster randomized trials (SW-CRTs) involve sequential measurements within clusters over time. Initially, all clusters start in the control condition before crossing over to the intervention on a staggered schedule. In cohort designs, secular trends, cluster-level changes, and individual-level changes (e.g., aging) must be considered. Methods: We performed a Monte Carlo simulation to analyze the influence of different time effects on the estimation of the intervention effect in cohort SW-CRTs. We compared four linear mixed models with different adjustment strategies, all including random intercepts for clustering and repeated measurements. We recorded the estimated fixed intervention effects and their corresponding model-based standard errors, derived from models both without and with cluster-robust variance estimators (CRVEs). Results: Models incorporating fixed categorical time effects, a fixed intervention effect, and two random intercepts provided unbiased estimates of the intervention effect in both closed and open cohort SW-CRTs. Fixed categorical time effects captured temporal cohort changes, while random individual effects accounted for baseline differences. However, these differences can cause large, non-normally distributed random individual effects. CRVEs provide reliable standard errors for the intervention effect, controlling the Type I error rate. Conclusions: Our simulation study is the first to assess individual-level changes over time in cohort SW-CRTs. Linear mixed models incorporating fixed categorical time effects and random cluster and individual effects yield unbiased intervention effect estimates. However, cluster-robust variance estimation is necessary when time-varying independent variables exhibit nonlinear effects. We recommend always using CRVEs.

翻译：背景：阶梯式楔形整群随机试验（SW-CRTs）需要在时间维度上对整群进行序贯测量。所有整群最初均处于对照状态，随后按交错时间表逐步转入干预阶段。在队列设计中，必须考虑长期趋势、整群水平变化以及个体水平变化（如年龄增长）的影响。方法：我们采用蒙特卡洛模拟方法，分析了队列SW-CRTs中不同时间效应对干预效应估计的影响。比较了四种具有不同调整策略的线性混合模型，所有模型均包含针对整群效应和重复测量的随机截距。记录了基于无集群稳健方差估计量（CRVEs）和含CRVEs的模型所得到的固定干预效应估计值及其相应的模型标准误。结果：在封闭队列和开放队列SW-CRTs中，包含固定分类时间效应、固定干预效应及两个随机截距的模型能够提供无偏的干预效应估计。固定分类时间效应捕捉了队列的时序变化，而随机个体效应则解释了基线差异。然而，这些差异可能导致随机个体效应呈现非正态的大幅度分布。CRVEs能为干预效应提供可靠的标准误，有效控制第一类错误率。结论：本模拟研究首次评估了队列SW-CRTs中个体水平随时间变化的影响。包含固定分类时间效应及随机整群与个体效应的线性混合模型可获得无偏的干预效应估计。但当时间依赖性自变量呈现非线性效应时，必须采用集群稳健方差估计。我们建议始终使用CRVEs。