Evidence map›Paper›PMID 37978559›Full record

Trial reportTrials2023

Predicting attrition of men with a history of violence from randomised clinical trials.

Natalie Doring, Ye In Jane Hwang, Emaediong Akpanekpo, Mathew Gullotta, Bianca Ton, Lee Knight, Crosbi Knight, Peter Schofield, Tony Gerard Butler

Open access · goldAbstract readRandomized Controlled Trial
In one paragraph

Trial report in Trials, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
0.6field-weighted citation impact, top 28% of its field
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

0 citing papers in PubMed, 2 citations in OpenAlex.

No citing paper in PubMed yet.

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

9 authors at 3 institutions in 1 country.

Natalie DoringSchool of Population Health, University of New South Wales, Sydney, NSW, Australia. n.doring@unsw.edu.au.ORCID http://orcid.org/0000-0001-5648-0160
Ye In Jane HwangSchool of Population Health, University of New South Wales, Sydney, NSW, Australia.ORCID http://orcid.org/0000-0003-0142-3251
Emaediong AkpanekpoSchool of Population Health, University of New South Wales, Sydney, NSW, Australia.ORCID http://orcid.org/0000-0002-0864-7488
Mathew GullottaSchool of Population Health, University of New South Wales, Sydney, NSW, Australia.
Bianca TonSchool of Population Health, University of New South Wales, Sydney, NSW, Australia.ORCID http://orcid.org/0000-0003-0285-0554
Lee KnightSchool of Population Health, University of New South Wales, Sydney, NSW, Australia.ORCID http://orcid.org/0000-0002-1832-8544
Crosbi KnightSchool of Population Health, University of New South Wales, Sydney, NSW, Australia.
Peter SchofieldThe University of New Castle, Callaghan, NSW, Australia.ORCID http://orcid.org/0000-0001-8298-8590
Tony Gerard ButlerSchool of Population Health, University of New South Wales, Sydney, NSW, Australia.ORCID http://orcid.org/0000-0002-2679-2769
UNSW Sydney · AUHunter New England Local Health District · AUThe University of Sydney · AU

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Preventing dropout (attrition) from clinical trials is vital for improving study validity. Dropout is particularly important in justice-involved populations as they can be very challenging to engage and recruit in the first instance. This study identifies factors associated with dropout in a double-blind, placebo-controlled randomised control trial of a selective serotonin reuptake inhibitor (SSRI) aimed at reducing reoffending in highly impulsive men with histories of violent offending. Age, education, social support, psychiatric history, and length of previous incarceration were identified as factors that predict attrition. These findings are consistent with previous research examining variables associated with attrition in clinical trials for community and offender populations. We also explored referral source and treatment allocation as attrition predictors. Although neither significantly predicted attrition, we identified that there are discernible differences in the median time to attrition among the referral source subgroups. Understanding factors that predict treatment completion and attrition will allow researchers to identify participants for whom additional provisions may optimise retention and inform development of targeted interventions.

Indexed as

Selective Serotonin Reuptake InhibitorsSocial SupportHumansMaleViolenceSelective Serotonin Reuptake InhibitorsAttritionDropoutOffender interventionRandomised clinical trialsRetentionTreatment attrition

Identifiers

PMID37978559
PMCPMC10657031
OpenAlexW4388749596

What OpenQuestion holds

Textmetadata
LicenceCC BY
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.