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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…

The intra-cluster correlation coefficient (ICC) plays an important role while designing the cluster randomized trials (CRTs). Often optimal CRTs are designed assuming that the magnitude of the ICC is constant across the clusters. However,…

Computation · Statistics 2019-02-21 Satya Prakash Singh , Pradeep Yadav

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

Longitudinal cluster randomized trials (L-CRTs) are increasingly used to evaluate the cost-effectiveness of healthcare interventions across multiple assessment periods, yet design methods for powering these trials remain underdeveloped.…

Methodology · Statistics 2026-03-20 Hao Wang , Jingxia Liu , Drew B. Cameron , Jiaqi Tong , Donna Spiegelman , Daniella Meeker , 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 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

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…

Econometrics · Economics 2025-12-17 Yue Fang , Geert Ridder

To minimize the mean squared error (MSE) in global average treatment effect (GATE) estimation under network interference, a popular approach is to use a cluster-randomized design. However, in the presence of homophily, which is common in…

Heterogeneous treatment effects (HTEs) are commonly identified during randomized controlled trials (RCTs). Identifying subgroups of patients with similar treatment effects is of high interest in clinical research to advance precision…

Machine Learning · Computer Science 2022-12-06 Peniel N. Argaw , Elizabeth Healey , Isaac S. Kohane

Cluster or group randomized trials (CRTs) are increasingly used for both behavioral and system-level interventions, where entire clusters are randomly assigned to a study condition or intervention. Apart from the assigned cluster-level…

Methodology · Statistics 2024-11-19 Shubhadeep Chakraborty , Bo Wang , Ram Tiwari , Samiran Ghosh

Cluster randomized trials (CRTs) randomly assign an intervention to groups of individuals (e.g., clinics or communities) and measure outcomes on individuals in those groups. While offering many advantages, this experimental design…

Hierarchical random effect models are used for different purposes in clinical research and other areas. In general, the main focus is on population parameters related to the expected treatment effects or group differences among all units of…

Applications · Statistics 2021-04-07 Maryna Prus , Norbert Benda , Rainer Schwabe

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…

Methodology · Statistics 2026-01-22 Xinyuan Chen , Fan Li

There are multiple cluster randomised trial designs that vary in when the clusters cross between control and intervention states, when observations are made within clusters, and how many observations are made at that time point. Identifying…

Methodology · Statistics 2023-07-20 Samuel I. Watson , Alan Girling , Karla Hemming

In this study, we focus on estimating the heterogeneous treatment effect (HTE) for survival outcome. The outcome is subject to censoring and the number of covariates is high-dimensional. We utilize data from both the randomized controlled…

Methodology · Statistics 2025-03-21 Xin Ye , Shu Yang , Xiaofei Wang , Yanyan Liu

Cluster-level dynamic treatment regimens can be used to guide sequential, intervention or treatment decision-making at the cluster level in order to improve outcomes at the individual or patient-level. In a cluster-level DTR, the…

Methodology · Statistics 2016-07-15 Timothy NeCamp , Amy Kilbourne , Daniel Almirall

Understanding whether and how treatment effects vary across subgroups is crucial to inform clinical practice and recommendations. Accordingly, the assessment of heterogeneous treatment effects (HTE) based on pre-specified potential effect…

Methodology · Statistics 2023-12-04 Bryan S. Blette , Scott D. Halpern , Fan Li , Michael O. Harhay

Clustering and dependence are common in trials. For example, in some cluster randomized trials (CRTs), pre-existing clusters are enrolled, randomized, and serve as the basis of intervention delivery. Such CRTs are "fully clustered":…

Randomized clinical trials (RCTs) are widely considered the gold standard for evaluating the effectiveness of new treatments or interventions in drug development. Still, they may not be feasible in certain cases, such as with rare diseases…

Methodology · Statistics 2025-08-05 Di Ran , Fanni Zhang , Sima Shahsavari , Kristine Broglio , Alasdair Henderson , Binbing Yu

Identifying heterogeneous treatment effects (HTEs) in randomized controlled trials is an important step toward understanding and acting on trial results. However, HTEs are often small and difficult to identify, and HTE modeling methods…

Methodology · Statistics 2020-11-24 Erin Craig , Donald A Redelmeier , Robert J Tibshirani
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