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In cluster randomized trials, the average treatment effect among individuals (i-ATE) can be different from the cluster average treatment effect (c-ATE) when informative cluster size is present, i.e., when treatment effects or participant…

统计方法学 · 统计学 2025-10-02 Bryan S. Blette , Zhe Chen , Brennan C. Kahan , Andrew Forbes , Michael O. Harhay , Fan Li

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…

统计方法学 · 统计学 2025-05-01 Kenneth Menglin Lee , Fan Li

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

Estimands can help clarify the interpretation of treatment effects and ensure that estimators are aligned to the study's objectives. Cluster randomised trials require additional attributes to be defined within the estimand compared to…

统计方法学 · 统计学 2024-02-23 Brennan C Kahan , Bryan Blette , Michael Harhay , Scott Halpern , Vipul Jairath , Andrew Copas , Fan Li

In cluster-randomized trials (CRTs), entire clusters of individuals are randomized to treatment, and outcomes within a cluster are typically correlated. While frequentist approaches are standard practice for CRT analysis, Bayesian methods…

统计方法学 · 统计学 2025-11-27 Ruyi Liu , Joshua L. Warren , Yuki Ohnishi , Donna Spiegelman , Liangyuan Hu , Fan Li

Across research disciplines, cluster randomized trials (CRTs) are commonly implemented to evaluate interventions delivered to groups of participants, such as communities and clinics. Despite advances in the design and analysis of CRTs,…

Cluster-randomized trials (CRTs) are experimental designs where groups or clusters of participants, rather than the individual participants themselves, are randomized to intervention groups. Analyzing CRT requires distinguishing between…

统计方法学 · 统计学 2025-10-10 Xi Fang , Bingkai Wang , Liangyuan Hu , 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

Treatment effect estimands based on win statistics, including the win ratio, win odds, and win difference are increasingly popular targets for summarizing endpoints in clinical trials. Such win estimands offer an intuitive approach for…

统计方法学 · 统计学 2026-02-13 Kenneth M. Lee , Xi Fang , Fan Li , Michael O. Harhay

In cluster randomized controlled trials (CRCT) with a finite populations, the exact design-based variance of the Horvitz-Thompson (HT) estimator for the average treatment effect (ATE) depends on the joint distribution of unobserved…

计量经济学 · 经济学 2025-12-17 Yue Fang , Geert Ridder

In cluster-randomized trials, generalized linear mixed models and generalized estimating equations have conventionally been the default analytic methods for estimating the average treatment effect as routine practice. However, recent…

统计方法学 · 统计学 2025-09-19 Fan Li , Jiaqi Tong , Xi Fang , Chao Cheng , Brennan C. Kahan , Bingkai Wang

In cluster-randomized crossover (CRXO) trials, groups of individuals are randomly assigned to two or more sequences of alternating treatments. Since clusters serve as their own control, the CRXO design is typically more statistically…

统计方法学 · 统计学 2026-01-30 Dane Isenberg , Michael O. Harhay , Andrew B. Forbes , Paul J. Young , Fan Li , Nandita Mitra

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

While average treatment effects (ATE) and conditional average treatment effects (CATE) provide valuable population- and subgroup-level summaries, they fail to capture uncertainty at the individual level. For high-stakes decision-making,…

统计方法学 · 统计学 2026-03-31 Juraj Bodik , Yaxuan Huang , Bin Yu

In cluster randomized trials, patients are typically recruited after clusters are randomized, and the recruiters and patients may not be blinded to the assignment. This often leads to differential recruitment and consequently systematic…

统计方法学 · 统计学 2021-10-05 Fan Li , Zizhong Tian , Jennifer Bobb , Georgia Papadogeorgou , 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

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: 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

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

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…

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