ArticleContemporary clinical trials2022
Two weights make a wrong: Cluster randomized trials with variable cluster sizes and heterogeneous treatment effects.
Article in Contemporary clinical trials, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 21 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
21 citing papers in PubMed, 25 citations in OpenAlex.
- Informative cluster size in cluster-randomised trials: A case study from the TRIGGER trial.Clinical trials (London, England) · 2023Trial
- Estimating Marginal Odds Ratios for Cluster Randomized Trials: The Cluster-Level GLM.Statistics in medicine · 2026Article
- What is estimated in cluster randomized crossover trials with informative sizes? A survey of estimands and common estimators.Statistical methods in medical research · 2026Article
- CRT-Estimands Framework: consensus based extension of the ICH E9(R1) addendum for cluster randomised trials.BMJ (Clinical research ed.) · 2026Article
- Model-robust standardization in stepped wedge cluster randomized trials.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2026Article
- Use of estimands in cluster randomised trials: A review.Clinical trials (London, England) · 2026Review
- Estimands and Doubly Robust Estimation for Cluster-Randomized Trials With Survival Outcomes.Statistics in medicine · 2026Article
- Handling incomplete outcomes and covariates in cluster-randomized trials: doubly robust estimation, efficiency considerations, and sensitivity analysis.Biometrics · 2026Article
- Article
- Analysis of Stepped-Wedge Cluster Randomized Trials When Treatment Effects Vary by Exposure Time or Calendar Time.Statistics in medicine · 2025Article
- Development of a consensus extension of the estimands framework for cluster randomised trials (CRT-estimands): results from an international Delphi study.medRxiv : the preprint server for health sciences · 2025Article
- How Should Parallel Cluster Randomized Trials With a Baseline Period be Analyzed?-A Survey of Estimands and Common Estimators.Biometrical journal. Biometrische Zeitschrift · 2025Article
- Weighted Repeated Measures Correlation Coefficient: A New Correlation Coefficient for Handling Missing Data With Repeated Measures.Statistics in medicine · 2025Article
- Article
- Robust analysis of stepped wedge trials using composite likelihood models.Statistics in medicine · 2024Article
- Demystifying estimands in cluster-randomised trials.Statistical methods in medical research · 2024Article
- Designing individually randomized group treatment trials with repeated outcome measurements using generalized estimating equations.Statistics in medicine · 2024Article
- Leveraging baseline covariates to analyze small cluster-randomized trials with a rare binary outcome.Biometrical journal. Biometrische Zeitschrift · 2024Article
- Model-robust and efficient covariate adjustment for cluster-randomized experiments.Journal of the American Statistical Association · 2024Article
- Defining and estimating effects in cluster randomized trials: A methods comparison.Statistics in medicine · 2023Review
Corrections and comments
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Authors and funding
8 authors at 6 institutions in 1 country.
Funding
Abstract
In cluster randomized trials (CRTs), the hierarchical nesting of participants (level 1) within clusters (level 2) leads to two conceptual populations: clusters and participants. When cluster sizes vary and the goal is to generalize to a hypothetical population of clusters, the unit average treatment effect (UATE), which averages equally at the cluster level rather than equally at the participant level, is a common estimand of interest. From an analytic perspective, when a generalized estimating equations (GEE) framework is used to obtain averaged treatment effect estimates for CRTs with variable cluster sizes, it is natural to specify an inverse cluster size weighted analysis so that each cluster contributes equally and to adopt an exchangeable working correlation matrix to account for within-cluster correlation. However, such an approach essentially uses two distinct weights in the analysis (i.e. both cluster size weights and covariance weights) and, in this article, we caution that it will lead to biased and/or inefficient treatment effect estimates for the UATE estimand. That is, two weights "make a wrong" or lead to poor estimation characteristics. These findings are based on theoretical derivations, corroborated via a simulation study, and illustrated using data from a CRT of a colorectal cancer screening program. We show that, an analysis with both an independence working correlation matrix and weighting by inverse cluster size is the only approach that always provides valid results for estimation of the UATE in CRTs with variable cluster sizes.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.