Evidence map›Paper›PMID 32838555›Full record

ArticleClinical trials (London, England)2020

Intent-to-treat analysis of cluster randomized trials when clusters report unidentifiable outcome proportions.

Stacia M DeSantis, Ruosha Li, Yefei Zhang, Xueying Wang, Sally W Vernon, Barbara C Tilley, Gary Koch

Registry-linked trialAbstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in Clinical trials (London, England), 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. It is linked to trial NCT01911208 (A Randomized Recruitment Intervention Trial), which is not on this map. Cited by 3 papers.

0numbers the graph read from it
0cells of the map it votes in
3citing 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.

NCT01911208 nacompletednot on this map

A Randomized Recruitment Intervention Trial

TypeinterventionalSponsorThe University of Texas Health Science Center, HoustonRan2011 to 2017Enrolled50ConditionsRecruitment of MinoritiesArmsRECRUIT intervention, Control
3 · Its place in the literature

Who cites it

3 citing papers in PubMed.

  1. Trial
  2. Article
  3. 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

7 authors.

Stacia M DeSantisDepartment of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX, USA.ORCID 0000-0002-3400-1031
Ruosha LiDepartment of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Yefei ZhangDepartment of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Xueying WangDepartment of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Sally W VernonDepartment of Health Promotions and Behavioral Science, The University of Texas Health Science Center at Houston, Houston, TX, USA.
Barbara C TilleyDepartment of Biostatistics and Data Science, The University of Texas Health Science Center at Houston, Houston, TX, USA.ORCID 0000-0002-3952-2282
Gary KochDepartment of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC, USA.

Funding

A Randomized Recruitment InterventionU24MD006941 · NIMHD · UNIVERSITY OF TEXAS HLTH SCI CTR HOUSTON · PI TILLEY, BARBARA C. · 2011 to 2016
$5.3M
NIMHD NIH HHS U24 MD006941
6 · The paper itself

Abstract

backgroundCluster randomized trials are designed to evaluate interventions at the cluster or group level. When clusters are randomized but some clusters report no or non-analyzable data, intent-to-treat analysis, the gold standard for the analysis of randomized controlled trials, can be compromised. This article presents a very flexible statistical methodology for cluster randomized trials whose outcome is a cluster-level proportion (e.g. proportion from a cluster reporting an event) in the setting where clusters report non-analyzable data (which in general could be due to nonadherence, dropout, missingness, etc.). The approach is motivated by a previously published stratified randomized controlled trial called, "The Randomized Recruitment Intervention Trial (RECRUIT)," designed to examine the effectiveness of a trust-based continuous quality improvement intervention on increasing minority recruitment into clinical trials (ClinicalTrials.gov Identifier: NCT01911208).

methodsThe novel approach exploits the use of generalized estimating equations for cluster-level reports, such that all clusters randomized at baseline are able to be analyzed, and intervention effects are presented as risk ratios. Simulation studies under different outcome missingness scenarios and a variety of intra-cluster correlations are conducted. A comparative analysis of the method with imputation and per protocol approaches for RECRUIT is presented.

resultsSimulation results show the novel approach produces unbiased and efficient estimates of the intervention effect that maintain the nominal type I error rate. Application to RECRUIT shows similar effect sizes when compared to the imputation and per protocol approach.

conclusionThe article demonstrates that an innovative bivariate generalized estimating equations framework allows one to implement an intent-to-treat analysis to obtain risk ratios or odds ratios, for a variety of cluster randomized designs.

Indexed as

Patient SelectionBiasCluster AnalysisComputer SimulationData Interpretation, StatisticalHumansIntention to Treat AnalysisLinear ModelsMinority GroupsOdds RatioRandomized Controlled Trials as TopicResearch DesignTreatment Outcomeclinical trialsCluster randomized trialsintent to treatmissing datarandomized controlled trials

Identifiers

PMID32838555
PMCPMC9497422

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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.