In stepped wedge cluster randomized trials (SW-CRTs), observations collected under the control condition are, on average, from an earlier time than observations collected under the intervention condition. In a cohort design, participants are followed up throughout the study, so correlations between measurements within a participant are dependent of the timing in which the observations are made. Therefore, changes in participants' characteristics over time must be taken into account when estimating intervention effects. For example, participants' age progresses, which may impact the outcome over the study period. Motivated by an SW-CRT of a geriatric care intervention to improve quality of life, we conducted a simulation study to compare model formulations analysing data from an SW-CRT under different scenarios in which time was related to the covariates and the outcome. The aim was to find a model specification that produces reliable estimates of the intervention effect. Six linear mixed effects (LME) models with different specification of fixed effects were fitted. Across 1000 simulations per parameter combination, we computed mean and standard error of the estimated intervention effects. We found that LME models with fixed categorical time effects additional to the fixed intervention effect and two random effects used to account for clustering (within-cluster correlation) and multiple measurements on participants (within-individual correlation) seem to produce unbiased estimates of the intervention effect even if time-varying confounders or their functional influence on outcome were unknown or unmeasured and if secular time trends occurred. Therefore, including (time-varying) covariates describing the study cohort seems to be avoidable.

翻译：在阶梯楔形整群随机试验（SW-CRTs）中，在对照条件下收集的观测值平均而言早于干预条件下的观测值。在队列设计中，参与者在整个研究期间被随访，因此参与者内部测量值之间的相关性取决于观测的时间点。因此，在估计干预效果时必须考虑参与者特征随时间的变化。例如，参与者的年龄增长可能在整个研究期间影响结局。受一项旨在改善生活质量的老年护理干预SW-CRT的启发，我们开展了一项模拟研究，以比较在不同时间与协变量和结局相关的情景下分析SW-CRT数据的模型公式。目的是找到一种能够产生可靠干预效果估计的模型设定。我们拟合了六种具有不同固定效应设定的线性混合效应（LME）模型。针对每个参数组合进行1000次模拟，我们计算了估计干预效果的均值和标准误。我们发现，在固定干预效果之外加上固定分类时间效应，并采用两种随机效应分别解释整群内相关性和参与者内多次测量相关性的LME模型，即使存在未知或未测量的时变混杂变量及其对结局的功能性影响，以及存在时间趋势时，也能产生无偏的干预效果估计。因此，似乎可以避免纳入描述研究队列的（时变）协变量。