In stepped wedge cluster randomized trials (SW-CRTs), interventions are sequentially rolled out to clusters over multiple periods. It is common practice to analyze data from SW-CRTs using linear mixed models that treat time as discrete. However, a recent systematic review found that 95.1% of cross-sectional SW-CRTs recruit individuals continuously over time. Despite the high prevalence of such continuous recruitment designs, there has been limited guidance on how to draw model-robust inference when analyzing such SW-CRTs. In this article, we investigate through simulations the implications of using such discrete-time linear mixed models in the case of continuous recruitment designs with a continuous outcome. Specifically, in the data-generating process, we characterize continuous recruitment using a continuous-time exponential decay correlation structure in the presence or absence of a fixed continuous period effect, addressing scenarios both with and without a random or exposure-time-dependent intervention effect. We then analyze the simulated data under three popular discrete-time working correlation structures: simple exchangeable, nested exchangeable, and discrete-time exponential decay, with a robust sandwich variance estimator. Our results demonstrate that discrete-time analysis often yields negligible bias and that the robust variance estimator with the Mancl and DeRouen correction consistently achieves nominal coverage and type I error rate. One important exception occurs when recruitment patterns vary systematically between control and intervention periods, where discrete-time analysis leads to slightly biased estimates. Finally, we illustrate these findings by reanalyzing a completed SW-CRT.

翻译：在阶梯楔形整群随机试验（SW-CRTs）中，干预措施在不同时期逐步推广至各集群。目前普遍采用将时间视为离散变量的线性混合模型分析SW-CRTs数据。然而，近期一项系统综述发现，95.1%的横断面SW-CRTs会随时间持续招募受试者。尽管持续招募设计如此普遍，但关于如何对此类SW-CRTs进行模型稳健推断的指导仍十分有限。本文通过模拟研究，探讨在连续结果变量的持续招募设计中采用离散时间线性混合模型的影响。具体而言，在数据生成过程中，我们采用连续时间指数衰减相关结构刻画持续招募特征（考虑或不考虑固定连续时间效应），并处理存在随机或暴露时间依赖性干预效应的情况。随后，我们基于三种流行的离散时间工作相关结构（简单可交换、嵌套可交换和离散时间指数衰减）结合稳健夹心方差估计量分析模拟数据。结果表明：离散时间分析通常产生的偏倚可忽略不计，采用Mancl-DeRouen校正的稳健方差估计量能稳定实现名义覆盖率和第一类错误率。但一个重要的例外是，当对照组与干预组之间的招募模式存在系统性差异时，离散时间分析会导致轻微有偏估计。最后，我们通过重新分析一项已完成的SW-CRT来验证上述发现。