Trial reportComputer methods and programs in biomedicine2022
Evaluation of the type I error rate when using parametric bootstrap analysis of a cluster randomized controlled trial with binary outcomes and a small number of clusters.
Trial report in Computer methods and programs in biomedicine, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 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
6 citing papers in PubMed.
- Digital Detection of Dementia in Primary Care: A Randomized Clinical Trial.JAMA network open · 2025Trial
- Ambiguity in Statistical Analysis Methods and Nonconformity With Prespecified Commitment to Data Sharing in a Cluster Randomized Controlled Trial.Journal of medical Internet research · 2024Article
- Statistical methods for comparing two independent exponential-gamma means with application to single cell protein data.PloS one · 2024Article
- Causal models and causal modelling in obesity: foundations, methods and evidence.Philosophical transactions of the Royal Society of London. Series B, Biological sciences · 2023Review
- Re-analysis of data from a cluster RCT entitled "health literacy and exercise-focused interventions on clinical measurements in Chinese diabetes patients".EClinicalMedicine · 2022Article
- A practical decision tree to support editorial adjudication of submitted parallel cluster randomized controlled trials.Obesity (Silver Spring, Md.) · 2022Article
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
9 authors.
Funding
Abstract
backgroundCluster randomized controlled trials (cRCTs) are increasingly used but must be analyzed carefully. We conducted a simulation study to evaluate the validity of a parametric bootstrap (PB) approach with respect to the empirical type I error rate for a cRCT with binary outcomes and a small number of clusters.
methodsWe simulated a case study with a binary (0/1) outcome, four clusters, and 100 subjects per cluster. To compare the validity of the test with respect to error rate, we simulated the same experiment with K=10, 20, and 30 clusters, each with 2,000 simulated datasets. To test the null hypothesis, we used a generalized linear mixed model including a random intercept for clusters and obtained p-values based on likelihood ratio tests (LRTs) using the parametric bootstrap method as implemented in the R package "pbkrtest".
resultsThe PB test produced error rates of 9.1%, 5.5%, 4.9%, and 5.0% on average across all ICC values for K=4, K=10, K=20, and K=30, respectively. The error rates were higher, ranging from 9.1% to 36.5% for K=4, in the models with singular fits (i.e., ignoring clustering) because the ICC was estimated to be zero.
conclusionUsing the parametric bootstrap for cRCTs with a small number of clusters results in inflated error rates and is not valid.
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Registered trials
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