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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…

Generalized linear mixed models (GLMM) are commonly used to analyze clustered data, but when the number of clusters is small to moderate, standard statistical tests may produce elevated type I error rates. Small-sample corrections have been…

Methodology · Statistics 2023-11-07 Hongxiang Qiu , Andrea J. Cook , Jennifer F. Bobb

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…

Methodology · Statistics 2025-06-27 Mary Ryan Baumann , Denise Esserman , Monica Taljaard , Fan Li

This paper studies inference in cluster randomized trials where treatment status is determined according to a "matched pairs" design. Here, by a cluster randomized experiment, we mean one in which treatment is assigned at the level of the…

Econometrics · Economics 2025-08-14 Yuehao Bai , Jizhou Liu , Azeem M. Shaikh , Max Tabord-Meehan

The parallel cluster randomized trial with baseline (PB-CRT) is a common variant of the standard parallel cluster randomized trial (P-CRT). We define two natural estimands in the context of PB-CRTs with informative cluster sizes, the…

Methodology · Statistics 2025-05-01 Kenneth Menglin Lee , Fan Li

Background: In settings where proof-of-principle trials have succeeded but the effectiveness of different forms of implementation remains uncertain, trials that not only generate information about intervention effects but also provide…

Quantitative Methods · Quantitative Biology 2017-05-16 Guy Harling , Rui Wang , Jukka-Pekka Onnela , Victor De Gruttola

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…

Applications · Statistics 2021-01-05 Fan Li

Paired cluster-randomized experiments (pCRTs) are common across many disciplines because there is often natural clustering of individuals, and paired randomization can help balance baseline covariates to improve experimental precision.…

Methodology · Statistics 2024-07-03 Charlotte Z. Mann , Adam C. Sales , Johann A. Gagnon-Bartsch

Background: When planning a cluster randomized trial, evaluators often have access to an enumerated cohort representing the target population of clusters. Practicalities of conducting the trial, such as the need to oversample clusters with…

Methodology · Statistics 2024-09-19 Sarah E. Robertson , Jon A. Steingrimsson , Issa J. Dahabreh

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…

Pragmatic trials evaluating health care interventions often adopt cluster randomization due to scientific or logistical considerations. Previous reviews have shown that co-primary endpoints are common in pragmatic trials but infrequently…

Methodology · Statistics 2022-05-03 Siyun Yang , Mirjam Moerbeek , Monica Taljaard , Fan Li

In this article, we develop methods for sample size and power calculations in four-level intervention studies when intervention assignment is carried out at any level, with a particular focus on cluster randomized trials (CRTs). CRTs…

Methodology · Statistics 2022-09-07 Xueqi Wang , Elizabeth L. Turner , John S. Preisser , Fan Li

Cluster-randomized experiments are widely used due to their logistical convenience and policy relevance. To analyze them properly, we must address the fact that the treatment is assigned at the cluster level instead of the individual level.…

Methodology · Statistics 2021-08-06 Fangzhou Su , Peng Ding

Numerous publications have now addressed the principles of designing, analyzing, and reporting the results of, stepped-wedge cluster randomized trials. In contrast, there is little research available pertaining to the design and analysis of…

Methodology · Statistics 2018-06-29 Michael Grayling , Adrian Mander , James Wason

We address estimation of intervention effects in experimental designs in which (a) interventions are assigned at the cluster level; (b) clusters are selected to form pairs, matched on observed characteristics; and (c) intervention is…

Methodology · Statistics 2014-11-24 Zhenke Wu , Constantine E. Frangakis , Thomas A. Louis , Daniel O. Scharfstein

Evaluating heterogeneity of treatment effects (HTE) across subgroups is common in both randomized trials and observational studies. Although several statistical challenges of HTE analyses including low statistical power and multiple…

Methodology · Statistics 2024-07-10 Noorie Hyun , Abisola E. Idu , Andrea J. Cook , Jennifer F. Bobb

Cluster-randomized trials (CRTs) are widely used to evaluate interventions delivered at the clinic, practice, or community level. Although standard analyses typically target average treatment effects, such summaries mask potentially…

Methodology · Statistics 2026-04-16 Changjun Li , Xi Fang , Michael O. Harhay , Andrew B. Forbes , F. Perry Wilson , Guangyu Tong , Fan Li

The Stepped Wedge Design (SWD) is a form of cluster randomized trial, usually comparing two treatments, which is divided into time periods and sequences, with clusters allocated to sequences. Typically all sequences start with the standard…

Methodology · Statistics 2020-11-06 John N. S. Matthews

While palliative care is increasingly commonly delivered to hospitalized patients with serious illnesses, few studies have estimated its causal effects. Courtright et al. (2016) adopted a cluster-randomized stepped-wedge design to assess…

Missing observations are common in cluster randomised trials. Approaches taken to handling such missing data include: complete case analysis, single-level multiple imputation that ignores the clustering, multiple imputation with a fixed…

Methodology · Statistics 2014-07-18 Karla Diaz-Ordaz , Michael G. Kenward , Manuel Gomes , Richard Grieve