Evidence map›Paper›PMID 30298551›Full record

ArticleStatistics in medicine2019

Power and sample size requirements for GEE analyses of cluster randomized crossover trials.

Fan Li, Andrew B Forbes, Elizabeth L Turner, John S Preisser

Abstract read
In one paragraph

Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 40 papers.

0numbers the graph read from it
0cells of the map it votes in
40citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

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.

2 · The registry

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.

3 · Its place in the literature

Who cites it

40 citing papers in PubMed.

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  12. Family Interviews Improve Health Service Recommendations in Mortality Review Process: A Mixed-Methods Assessment.Health expectations : an international journal of public participation in health care and health policy · 2025
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  20. Generalizing the information content for stepped wedge designs: A marginal modeling approach.Scandinavian journal of statistics, theory and applications · 2023
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4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

4 authors.

Fan LiDepartment of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina.ORCID 0000-0001-6183-1893
Andrew B ForbesSchool of Public Health and Preventive Medicine, Monash University, Melbourne, Victoria, Australia.
Elizabeth L TurnerDepartment of Biostatistics and Bioinformatics, Duke University, Durham, North Carolina.
John S PreisserDepartment of Biostatistics, University of North Carolina at Chapel Hill, Chapel Hill, North Carolina.

Funding

Re-Entry Supplement: Investigation of Oral Microbial Enzymes for the Detection and Treatment of Periodontal DiseaseUL1TR002489 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BUSE, JOHN BERNARD, SHAHEEN, NICHOLAS J · 2018 to 2022
$48.6M
North Carolina Translational & Clinical Sciences Institute (NC TraCS)UL1TR001111 · NCATS · UNIV OF NORTH CAROLINA CHAPEL HILL · PI BUSE, JOHN BERNARD, CAREY, TIMOTHY S · 2013 to 2017
$43.9M
NCATS NIH HHS UL1 TR001111NCATS NIH HHS UL1 TR002489
6 · The paper itself

Abstract

The cluster randomized crossover design has been proposed to improve efficiency over the traditional parallel cluster randomized design, which often involves a limited number of clusters. In recent years, the cluster randomized crossover design has been increasingly used to evaluate the effectiveness of health care policy or programs, and the interest often lies in quantifying the population-averaged intervention effect. In this paper, we consider the two-treatment two-period crossover design, and develop sample size procedures for continuous and binary outcomes corresponding to a population-averaged model estimated by generalized estimating equations, accounting for both within-period and interperiod correlations. In particular, we show that the required sample size depends on the correlation parameters through an eigenvalue of the within-cluster correlation matrix for continuous outcomes and through two distinct eigenvalues of the correlation matrix for binary outcomes. We demonstrate that the empirical power corresponds well with the predicted power by the proposed formulae for as few as eight clusters, when outcomes are analyzed using the matrix-adjusted estimating equations for the correlation parameters concurrently with a suitable bias-corrected sandwich variance estimator.

Indexed as

Cross-Over StudiesData Interpretation, StatisticalSample SizeHumansModels, StatisticalRandomized Controlled Trials as TopicTreatment Outcomecluster randomized trialscrossoverfinite-sample correctiongeneralized estimating equations (GEE)matrix-adjusted estimating equations (MAEEs)sandwich variance estimator

Identifiers

PMID30298551
PMCPMC6461037

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Registered trials

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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.