Evidence map›Paper›PMID 35393864›Full record

ArticleClinical trials (London, England)2022

Practical issues in operationalizing the design and outcome evaluation of cluster randomized trials.

Deborah Donnell

Abstract read
In one paragraph

Article in Clinical trials (London, England), 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 1 paper.

0numbers the graph read from it
0cells of the map it votes in
1citing 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

1 citing paper in PubMed.

  1. Observational
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

1 author.

Deborah DonnellFred Hutchinson Cancer Research Center, Seattle, WA, USA.ORCID 0000-0002-0587-7480

Funding

SDMC: HPTN 084 Pregnancy SupplementUM1AI068617 · NIAID · FRED HUTCHINSON CANCER RESEARCH CENTER · PI Elizabeth Renata Brown, Deborah J Donnell · 2011 to 2026
$204.9M
NIAID NIH HHS UM1 AI068617
6 · The paper itself

Abstract

Trial designs using cluster-level randomization are necessary when interventions have intended effects that cannot be measured with individual randomization. When an intervention is intrinsically only able to be delivered to a cluster or when implementation of an individual level intervention is only feasibly implemented at a cluster level, cluster-level randomization is required. In designing the strategy for evaluation of the primary outcome of a cluster randomized trial, there are a multitude of important decisions to consider. While these decisions are guided primarily by the intervention-who benefits, what is the intended effect and when will it be achieved-there are important detailed choices that affect potential bias and statistical power, and implementation considerations that require compromise for considerations of feasibility and practicality. Through the lens of three large completed cluster randomized trials in HIV prevention, we present specific choices made for the overall evaluation plan, together with some of the detailed considerations, compromises and modifications that occurred during trial implementation.

Indexed as

Research DesignBiasCluster AnalysisHumansRandomized Controlled Trials as TopicCluster randomized trialsHawthorneHIV preventionoutcome evaluation

Identifiers

PMID35393864
PMCPMC9378425

What OpenQuestion holds

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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.