ArticleComputational statistics & data analysis2009
Effect of Imbalance and Intracluster Correlation Coefficient in Cluster Randomized Trials with Binary Outcomes.
Article in Computational statistics & data analysis, 2009. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 2 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.
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Who cites it
2 citing papers in PubMed.
- Methods for dealing with unequal cluster sizes in cluster randomized trials: A scoping review.PloS one · 2021Article
- Sample Size Calculation for Clustered Binary Data with Sign Tests Using Different Weighting Schemes.Statistics in biopharmaceutical research · 2011Article
Corrections and comments
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Authors and funding
3 authors.
Funding
Abstract
Cluster randomization trials are increasingly popular among healthcare researchers. Intact groups (called 'clusters') of subjects are randomized to receive different interventions and all subjects within a cluster receive the same intervention. In cluster randomized trials, a cluster is the unit of randomization and a subject is the unit of analysis. Variation in cluster sizes can affect the sample size estimate or the power of the study. Guittet et al. (2006) investigated the impact of an imbalance in cluster size on the power of trials with continuous outcomes through simulations. In this paper, we examine the impact of cluster size variation and intracluster correlation on the power of the study for binary outcomes through simulations. Because the sample size formula for cluster randomization trials is based on a large sample approximation, we evaluate the performance of the sample size formula with small sample sizes through simulation. Simulation study findings show that the sample size formula (m(p)) accounting for unequal cluster sizes yields empirical powers closer to the nominal power than the sample size formula (m(a)) for the average cluster size method. The differences in sample size estimates and empirical powers between m(a) and m(p) get smaller as the imbalance in cluster sizes gets smaller.
Identifiers
What OpenQuestion holds
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.