Which Small-Sample Correction Should Be Used When Analyzing Stepped-Wedge Designs with Time-Varying Treatment Effects?
时变处理效应阶梯楔形设计应采用哪种小样本修正？AI 解读 ↓
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时变处理效应阶梯楔形设计应采用哪种小样本修正？
- 研究问题
- 阶梯楔形集群随机试验中，处理效应随暴露时长变化且随机效应结构可能误设时，哪类方差修正能支持可靠的小样本推断？
- 方法与思路
- 作者在暴露时间指标模型中比较经典夹心及 KC、MD、MBN 方差估计，模拟连续和二元结局的时间平均与长期处理效应。
- 材料陈述的贡献
- 摘要称误设时稳健方差改善覆盖；连续结局中 MD 配合指定 t 推断较稳定，二元结局中 MBN 较可靠，但长期效应推断仍不稳定。
- 阅读关注点
- 应核对集群数、每序列集群数与暴露时长分布，方差修正及自由度选择，并比较平均效应和长期效应各自的信息支持。
- 阅读边界
- 仅据 arXiv 预印本摘要，不能确认同行评审或期刊接收；模拟设定和完整数值未说明，方法建议仅获所考察情景的支持。
原文摘要
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.