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Background: Target trial emulation (TTE) that applies trial design principles to improve the analysis of non-randomized studies is increasingly being used. Applications of TTE to emulate cluster randomized trials (RCTs) have been limited.…

应用统计 · 统计学 2025-12-17 Shomoita Alam , Nathaniel Dyrkton , Susan Shepherd , Ibrahim Sana , Kevin Phelan , Jay JH Park

A practical limitation of cluster randomized controlled trials (cRCTs) is that the number of available clusters may be small, resulting in an increased risk of baseline imbalance under simple randomization. Constrained randomization…

统计方法学 · 统计学 2022-01-19 Yunji Zhou , Elizabeth L. Turner , Ryan A. Simmons , Fan Li

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…

统计方法学 · 统计学 2022-05-03 Siyun Yang , Mirjam Moerbeek , Monica Taljaard , 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…

定量方法 · 定量生物学 2017-05-16 Guy Harling , Rui Wang , Jukka-Pekka Onnela , Victor De Gruttola

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

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…

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…

统计方法学 · 统计学 2022-09-07 Xueqi Wang , Elizabeth L. Turner , John S. Preisser , Fan Li

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

Sample size determination for cluster randomised trials (CRTs) is challenging as it requires robust estimation of the intra-cluster correlation coefficient (ICC). Typically, the sample size is chosen to provide a certain level of power to…

应用统计 · 统计学 2023-08-23 S. Faye Williamson , Svetlana V. Tishkovskaya , Kevin J. Wilson

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…

统计方法学 · 统计学 2023-11-07 Hongxiang Qiu , Andrea J. Cook , Jennifer F. Bobb

Cluster-randomized trials (CRTs) are widely used to evaluate group-level interventions and increasingly collect multiple outcomes capturing complementary dimensions of benefit and risk. Investigators often seek a single global summary of…

统计方法学 · 统计学 2026-01-22 Xinyuan Chen , 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.…

统计方法学 · 统计学 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…

统计方法学 · 统计学 2024-09-19 Sarah E. Robertson , Jon A. Steingrimsson , Issa J. Dahabreh

Accurately estimating the intra-class correlation coefficient (ICC) is crucial for adequately powering clustered randomized trials (CRTs). Challenges arise due to limited prior data on the specific outcome within the target population,…

统计方法学 · 统计学 2025-04-23 Chen Yang , Márcio A. Diniz , Deukwoo Kwon , Madhu Mazumdar

Cluster randomized trials (CRTs) are studies where treatment is randomized at the cluster level but outcomes are typically collected at the individual level. When CRTs are employed in pragmatic settings, baseline population characteristics…

统计方法学 · 统计学 2023-06-22 Mary M. Ryan , Denise Esserman , Fan Li

Analyses of cluster randomized trials (CRTs) can be complicated by informative missing outcome data. Methods such as inverse probability weighted generalized estimating equations have been proposed to account for informative missingness by…

统计方法学 · 统计学 2023-04-13 Chia-Rui Chang , Rui Wang

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…

Randomized controlled trials (RCTs) often include subgroup analyses to assess whether treatment effects vary across pre-specified patient populations. However, these analyses frequently suffer from small sample sizes which limit the power…

Randomized Controlled Trials (RCTs) often adjust for baseline covariates in order to increase power. This technical note provides a short derivation of a simple rule of thumb for approximating the ratio of the power of an adjusted analysis…

统计方法学 · 统计学 2023-08-16 Charles K. Fisher

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…

统计方法学 · 统计学 2026-04-16 Changjun Li , Xi Fang , Michael O. Harhay , Andrew B. Forbes , F. Perry Wilson , Guangyu Tong , Fan Li
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