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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

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

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

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

A stepped wedge cluster randomized trial is a type of longitudinal cluster design that sequentially switches clusters to intervention over time until all clusters are treated. While the traditional posttest-only parallel design requires…

应用统计 · 统计学 2021-01-05 Fan Li

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

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 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 trial (CRTs) designs randomize clusters of individuals to intervention sequences, ensuring that every cluster eventually transitions from a control period to receive the intervention under study by the end…

统计方法学 · 统计学 2025-02-19 Alessandro Gasparini , Michael J. Crowther , Emiel O. Hoogendijk , Fan Li , Michael O. Harhay

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

Recently, methodology was presented to facilitate the incorporation of interim analyses in stepped-wedge (SW) cluster randomised trials (CRTs). Here, we extend this previous discussion. We detail how the stopping boundaries, allocation…

统计方法学 · 统计学 2018-03-28 Michael Grayling , David Robertson , James Wason , Adrian Mander

Stepped wedge cluster randomized trials (SW-CRTs) with binary outcomes are increasingly used in prevention and implementation studies. Marginal models represent a flexible tool for analyzing SW-CRTs with population-averaged interpretations,…

统计方法学 · 统计学 2021-01-05 Fan Li , Hengshi Yu , Paul J. Rathouz , Elizabeth L. Turner , John S. Preisser

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

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…

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

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

Stepped wedge cluster randomized trials (SW-CRTs) have historically been analyzed using immediate treatment (IT) models, which assume the effect of the treatment is immediate after treatment initiation and subsequently remains constant over…

统计方法学 · 统计学 2025-11-25 Avi Kenny , Emily C. Voldal , Fan Xia , Kwun Chuen Gary Chan , Patrick J. Heagerty , James P. Hughes

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

Staggered rollout cluster randomized experiments (SR-CREs) involve sequential treatment adoption across clusters, requiring analysis methods that address a general class of dynamic causal effects, anticipation, and non-ignorable…

统计方法学 · 统计学 2026-02-02 Xinyuan Chen , Fan Li

Stepped wedge designs (SWDs) are increasingly used to evaluate longitudinal cluster-level interventions but pose substantial challenges for valid inference. Because crossover times are randomized, intervention effects are intrinsically…

统计方法学 · 统计学 2026-05-12 Fan Xia , K. C. Gary Chan , Emily Voldal , Avi Kenny , Patrick J. Heagerty , James P. Hughes
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