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Predicting attrition of men with a history of violence from randomised clinical trials
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-...
Autores principales: | , , , , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2023
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10657031/ https://www.ncbi.nlm.nih.gov/pubmed/37978559 http://dx.doi.org/10.1186/s13063-023-07774-3 |
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author | Doring, Natalie Hwang, Ye In (Jane) Akpanekpo, Emaediong Gullotta, Mathew Ton, Bianca Knight, Lee Knight, Crosbi Schofield, Peter Butler, Tony Gerard |
author_facet | Doring, Natalie Hwang, Ye In (Jane) Akpanekpo, Emaediong Gullotta, Mathew Ton, Bianca Knight, Lee Knight, Crosbi Schofield, Peter Butler, Tony Gerard |
author_sort | Doring, Natalie |
collection | PubMed |
description | 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13063-023-07774-3. |
format | Online Article Text |
id | pubmed-10657031 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2023 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-106570312023-11-17 Predicting attrition of men with a history of violence from randomised clinical trials Doring, Natalie Hwang, Ye In (Jane) Akpanekpo, Emaediong Gullotta, Mathew Ton, Bianca Knight, Lee Knight, Crosbi Schofield, Peter Butler, Tony Gerard Trials Research 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. SUPPLEMENTARY INFORMATION: The online version contains supplementary material available at 10.1186/s13063-023-07774-3. BioMed Central 2023-11-17 /pmc/articles/PMC10657031/ /pubmed/37978559 http://dx.doi.org/10.1186/s13063-023-07774-3 Text en © The Author(s) 2023 https://creativecommons.org/licenses/by/4.0/Open Access This article is licensed under a Creative Commons Attribution 4.0 International License, which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons licence, and indicate if changes were made. The images or other third party material in this article are included in the article's Creative Commons licence, unless indicated otherwise in a credit line to the material. If material is not included in the article's Creative Commons licence and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder. To view a copy of this licence, visit http://creativecommons.org/licenses/by/4.0/ (https://creativecommons.org/licenses/by/4.0/) . The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/ (https://creativecommons.org/publicdomain/zero/1.0/) ) applies to the data made available in this article, unless otherwise stated in a credit line to the data. |
spellingShingle | Research Doring, Natalie Hwang, Ye In (Jane) Akpanekpo, Emaediong Gullotta, Mathew Ton, Bianca Knight, Lee Knight, Crosbi Schofield, Peter Butler, Tony Gerard Predicting attrition of men with a history of violence from randomised clinical trials |
title | Predicting attrition of men with a history of violence from randomised clinical trials |
title_full | Predicting attrition of men with a history of violence from randomised clinical trials |
title_fullStr | Predicting attrition of men with a history of violence from randomised clinical trials |
title_full_unstemmed | Predicting attrition of men with a history of violence from randomised clinical trials |
title_short | Predicting attrition of men with a history of violence from randomised clinical trials |
title_sort | predicting attrition of men with a history of violence from randomised clinical trials |
topic | Research |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10657031/ https://www.ncbi.nlm.nih.gov/pubmed/37978559 http://dx.doi.org/10.1186/s13063-023-07774-3 |
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