Stepped-wedge cluster randomized trials (SW-CRTs) evaluate interventions rolled out across clusters over time. Standard analyses typically use immediate-treatment (IT) models, which assume effects begin at crossover and remain constant thereafter. When effects vary with exposure duration, IT models may misrepresent target effects. Exposure-time indicator (ETI) models address this by allowing treatment effects to differ by time since exposure and by targeting the time-averaged treatment effect (TATE) and long-term effect (LTE). Like IT models, ETI models require specification of a random-effects structure, which is often misspecified, and the performance of robust variance estimators (RVEs) in this setting is not well understood. We review RVEs for ETI models and evaluate them in simulation studies with continuous and binary outcomes under correctly specified (binary only) and misspecified random-effects structures. We compare the classic sandwich, Kauermann-Carroll (KC), Mancl-DeRouen (MD), and Morel-Bokossa-Neerchal (MBN) estimators for inference on the TATE and LTE. Our simulations show that under misspecified random-effects structures, model-based standard errors (SE) produced undercoverage, whereas RVEs improved performance. For continuous outcomes, MD with a t-distribution and degrees of freedom equal to the number of clusters minus two gave the most consistent coverage probabilities. For binary outcomes, MBN was the only consistently reliable option. MD, however, could be unstable in one-cluster-per-sequence designs because of data sparsity. Across scenarios, both model-based SE and RVE for LTE were unstable, indicating that greater caution is needed when targeting LTE under ETI models.

翻译：阶梯楔形整群随机试验（SW-CRTs）评估随时间在整群中逐步实施的干预措施。标准分析通常采用即时处理（IT）模型，该模型假设效应始于交叉点并此后保持不变。当效应随暴露时间变化时，IT模型可能扭曲目标效应。暴露时间指标（ETI）模型通过允许处理效应随暴露后时间不同，并针对时间平均处理效应（TATE）和长期效应（LTE）来解决此问题。与IT模型类似，ETI模型需要指定随机效应结构，而该结构常被误设，且在此情形下稳健方差估计量（RVEs）的性能尚不明确。我们综述了ETI模型的RVEs，并在连续型和二分类结局（仅针对后者）下，通过正确设定和误设随机效应结构的模拟研究评估其表现。比较了经典三明治、Kauermann-Carroll（KC）、Mancl-DeRouen（MD）和Morel-Bokossa-Neerchal（MBN）估计量对TATE与LTE的推断效果。模拟表明：在随机效应结构误设下，基于模型的标准误（SE）会产生覆盖不足，而RVEs改进了性能。对于连续型结局，采用t分布且自由度等于整群数减2的MD估计量给出了最一致的覆盖率；对于二分类结局，MBN是唯一始终可靠的选择。然而，MD在每序列仅一个整群的设计中可能因数据稀疏而不稳定。在所有场景中，针对LTE的基于模型SE和RVE均不稳定，表明在使用ETI模型估计LTE时需要更加谨慎。