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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…

统计方法学 · 统计学 2026-04-21 Yongdong Ouyang , Monica Taljaard , James P. Hughes , Fan Li

Stepped wedge cluster randomized controlled trials are typically analyzed using models that assume the full effect of the treatment is achieved instantaneously. We provide an analytical framework for scenarios in which the treatment effect…

统计方法学 · 统计学 2025-09-25 Avi Kenny , Emily Voldal , Fan Xia , Patrick J. Heagerty , James P. Hughes

Stepped-wedge cluster randomized trials (SW-CRTs) are traditionally analyzed with models that assume an immediate and sustained treatment effect. Previous work has shown that making such an assumption in the analysis of SW-CRTs when the…

统计方法学 · 统计学 2025-04-08 Kenneth M. Lee , Elizabeth L. Turner , Avi Kenny

In stepped wedge cluster randomized trials (SW-CRTs), the intervention is rolled out to clusters over multiple periods. A standard approach for analyzing SW-CRTs utilizes the linear mixed model, where the treatment effect is only present…

The traditional model specification of stepped-wedge cluster-randomized trials assumes a homogeneous treatment effect across time while adjusting for fixed-time effects. However, when treatment effects vary over time, the constant effect…

统计方法学 · 统计学 2025-04-22 Zhe Chen , Wei Wang , Yingying Lu , Scott D. Halpern , Katherine R. Courtright , Fan Li , Michael O. Harhay

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…

统计方法学 · 统计学 2023-02-23 Jale Basten , Katja Ickstadt , Nina Timmesfeld

Stepped wedge cluster randomized trials (SW-CRTs) have become increasingly popular and are used for a variety of interventions and outcomes, often chosen for their feasibility advantages. SW-CRTs must account for time trends in the outcome…

统计方法学 · 统计学 2024-07-16 Lee Kennedy-Shaffer , Victor De Gruttola , Marc Lipsitch

Multivariate outcomes are common in pragmatic cluster randomized trials. While sample size calculation procedures for multivariate outcomes exist under parallel assignment, none have been developed for a stepped wedge design. In this…

统计方法学 · 统计学 2022-12-05 Kendra Davis-Plourde , Monica Taljaard , Fan Li

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…

统计方法学 · 统计学 2026-01-21 Jale Basten , Katja Ickstadt , Nina Timmesfeld

Stepped wedge cluster randomized trials (SW-CRTs) are a form of randomized trial whereby clusters are progressively transitioned from control to intervention, with the timing of transition randomized for each cluster. An important task at…

统计方法学 · 统计学 2025-06-27 Mary Ryan Baumann , Denise Esserman , Monica Taljaard , Fan Li

Cluster-randomized trials (CRTs) are a well-established class of designs for evaluating community-based interventions. An essential task in planning these trials is determining the number of clusters and cluster sizes needed to achieve…

Mediation analysis has been comprehensively studied for independent data but relatively little work has been done for correlated data, especially for the increasingly adopted stepped wedge cluster randomized trials (SW-CRTs). Motivated by…

统计方法学 · 统计学 2025-04-03 Zhiqiang Cao , Fan Li

Stepped-wedge cluster randomised trials (SW-CRTs) increasingly evaluate complex interventions, yet methodological guidance for analysing composite endpoints using generalized pairwise comparisons (GPC)remains limited. This work investigates…

Stepped-wedge cluster-randomized trials (SW-CRTs) are widely used in healthcare and implementation science, providing an ethical advantage by ensuring all clusters eventually receive the intervention. The staggered rollout of treatment…

统计方法学 · 统计学 2026-04-03 Xi Fang , Xueqi Wang , Patrick J. Heagerty , Bingkai Wang , Fan Li

Stepped wedge designs (SWDs) are designs for cluster randomized trials that feature staggered, unidirectional cross-over, typically from a control to a treatment condition. Existing literature on statistical power for SWDs primarily focuses…

统计方法学 · 统计学 2020-10-07 Phillip T. Sundin , Catherine M. Crespi

In stepped wedge cluster randomized trials (SW-CRTs), interventions are sequentially rolled out to clusters over multiple periods. It is common practice to analyze SW-CRTs using discrete-time linear mixed models, in which measurements are…

统计方法学 · 统计学 2025-11-25 Hao Wang , Guangyu Tong , Heather Allore , Monica Taljaard , Fan Li

This article develops new closed-form variance expressions for power analyses for commonly used difference-in-differences (DID) and comparative interrupted time series (CITS) panel data estimators. The main contribution is to incorporate…

统计方法学 · 统计学 2021-10-18 Peter Z. Schochet

Multi-period cluster randomized trials (CRTs) are increasingly used for the evaluation of interventions delivered at the group level. While generalized estimating equations (GEE) are commonly used to provide population-averaged inference in…

统计计算 · 统计学 2022-05-31 Ying Zhang , John S. Preisser , Fan Li , Elizabeth L. Turner , Paul J. Rathouz

Linear mixed models are commonly used in analyzing stepped-wedge cluster randomized trials (SW-CRTs). A key consideration for analyzing a SW-CRT is accounting for the potentially complex correlation structure, which can be achieved by…

统计方法学 · 统计学 2024-08-21 Yongdong Ouyang , Monica Taljaard , Andrew B Forbes , Fan Li

Causal inference in the presence of intermediate variables is a challenging problem in many applications. Principal stratification (PS) provides a framework to estimate principal causal effects (PCE) in such settings. However, existing PS…

统计方法学 · 统计学 2026-02-20 Lei Yang , Michael J. Daniels , Fan Li
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