Evidence map›Paper›PMID 28464567›Full record

ArticleStatistics in medicine2017

The use of permutation tests for the analysis of parallel and stepped-wedge cluster-randomized trials.

Rui Wang, Victor De Gruttola

Abstract read
In one paragraph

Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 27 papers.

0numbers the graph read from it
0cells of the map it votes in
27citing 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

27 citing papers in PubMed.

  1. Opportunistic Treatment of Hepatitis C Infection Among Hospitalized People Who Inject Drugs (OPPORTUNI-C): A Stepped Wedge Cluster Randomized Trial.Clinical infectious diseases : an official publication of the Infectious Diseases Society of America · 2024
    Trial
  2. Trial
  3. Trial
  4. Article
  5. Review
  6. Article
  7. Article
  8. Article
  9. Article
  10. Article
  11. Article
  12. Article
  13. Review
  14. Article
  15. Article
  16. Article
  17. Article
  18. Review
  19. Article
  20. Article
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

2 authors.

Rui WangDepartment of Population Medicine, Harvard Medical School and Harvard Pilgrim Health Care Institute, 401 Park Drive, Suite 401 East, Boston, 02215, MA, U.S.A.ORCID http://orcid.org/0000-0001-5007-193X
Victor De GruttolaDepartment of Biostatistics, Harvard T.H. Chan School of Public Health, 655 Huntington Avenue, Boston, 02115, MA, U.S.A.

Funding

Methods to Advance the HIV Prevention Research AgendaR37AI051164 · NIAID · HARVARD SCHOOL OF PUBLIC HEALTH · PI DEGRUTTOLA, VICTOR GERARD · 2012 to 2019
$3.3M
STATISTICAL METHODS IN AIDS RESEARCHR01AI024643 · NIAID · HARVARD UNIVERSITY (MEDICAL SCHOOL) · PI HUGHES, MICHAEL DAVID · 1987 to 2014
$1.7M
NIAID NIH HHS R01 AI024643NIAID NIH HHS R37 AI051164
6 · The paper itself

Abstract

We investigate the use of permutation tests for the analysis of parallel and stepped-wedge cluster-randomized trials. Permutation tests for parallel designs with exponential family endpoints have been extensively studied. The optimal permutation tests developed for exponential family alternatives require information on intraclass correlation, a quantity not yet defined for time-to-event endpoints. Therefore, it is unclear how efficient permutation tests can be constructed for cluster-randomized trials with such endpoints. We consider a class of test statistics formed by a weighted average of pair-specific treatment effect estimates and offer practical guidance on the choice of weights to improve efficiency. We apply the permutation tests to a cluster-randomized trial evaluating the effect of an intervention to reduce the incidence of hospital-acquired infection. In some settings, outcomes from different clusters may be correlated, and we evaluate the validity and efficiency of permutation test in such settings. Lastly, we propose a permutation test for stepped-wedge designs and compare its performance with mixed-effect modeling and illustrate its superiority when sample sizes are small, the underlying distribution is skewed, or there is correlation across clusters. Copyright © 2017 John Wiley & Sons, Ltd.

Indexed as

BiostatisticsCluster AnalysisCross InfectionData Interpretation, StatisticalFatty AcidsHumansIndolesModels, StatisticalOutcome Assessment, Health CareRandomized Controlled Trials as TopicSample SizeCU 23Fatty AcidsIndolescluster-randomized trialspair-matched designpermutation teststepped-wedge designtime-to-event endpoints

Identifiers

PMID28464567
PMCPMC5507602

What OpenQuestion holds

Textmetadata
LicenceTDM
Read underepoch 390

Registered trials

None linked

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.