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Managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: a survey to establish current practice
BACKGROUND: Patient outcomes can depend on the treating centre, or health professional, delivering the intervention. A health professional’s skill in delivery improves with experience, meaning that outcomes may be associated with learning. Considering differences in intervention delivery at trial de...
Autores principales: | , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BioMed Central
2020
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251810/ https://www.ncbi.nlm.nih.gov/pubmed/32460815 http://dx.doi.org/10.1186/s13063-020-04318-x |
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author | Conroy, Elizabeth J. Blazeby, Jane M. Burnside, Girvan Cook, Jonathan A. Gamble, Carrol |
author_facet | Conroy, Elizabeth J. Blazeby, Jane M. Burnside, Girvan Cook, Jonathan A. Gamble, Carrol |
author_sort | Conroy, Elizabeth J. |
collection | PubMed |
description | BACKGROUND: Patient outcomes can depend on the treating centre, or health professional, delivering the intervention. A health professional’s skill in delivery improves with experience, meaning that outcomes may be associated with learning. Considering differences in intervention delivery at trial design will ensure that any appropriate adjustments can be made during analysis. This work aimed to establish practice for the allowance of clustering and learning effects in the design and analysis of randomised multicentre trials. METHODS: A survey that drew upon quotes from existing guidelines, references to relevant publications and example trial scenarios was delivered. Registered UK Clinical Research Collaboration Registered Clinical Trials Units were invited to participate. RESULTS: Forty-four Units participated (N = 50). Clustering was managed through design by stratification, more commonly by centre than by treatment provider. Managing learning by design through defining a minimum expertise level for treatment provider was common (89%). One-third reported experience in expertise-based designs. The majority of Units had adjusted for clustering during analysis, although approaches varied. Analysis of learning was rarely performed for the main analysis (n = 1), although it was explored by other means. The insight behind the approaches used within and reasons for, or against, alternative approaches were provided. CONCLUSIONS: Widespread awareness of challenges in designing and analysing multicentre trials is identified. Approaches used, and opinions on these, vary both across and within Units, indicating that approaches are dependent on the type of trial. Agreeing principles to guide trial design and analysis across a range of realistic clinical scenarios should be considered. |
format | Online Article Text |
id | pubmed-7251810 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2020 |
publisher | BioMed Central |
record_format | MEDLINE/PubMed |
spelling | pubmed-72518102020-06-07 Managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: a survey to establish current practice Conroy, Elizabeth J. Blazeby, Jane M. Burnside, Girvan Cook, Jonathan A. Gamble, Carrol Trials Methodology BACKGROUND: Patient outcomes can depend on the treating centre, or health professional, delivering the intervention. A health professional’s skill in delivery improves with experience, meaning that outcomes may be associated with learning. Considering differences in intervention delivery at trial design will ensure that any appropriate adjustments can be made during analysis. This work aimed to establish practice for the allowance of clustering and learning effects in the design and analysis of randomised multicentre trials. METHODS: A survey that drew upon quotes from existing guidelines, references to relevant publications and example trial scenarios was delivered. Registered UK Clinical Research Collaboration Registered Clinical Trials Units were invited to participate. RESULTS: Forty-four Units participated (N = 50). Clustering was managed through design by stratification, more commonly by centre than by treatment provider. Managing learning by design through defining a minimum expertise level for treatment provider was common (89%). One-third reported experience in expertise-based designs. The majority of Units had adjusted for clustering during analysis, although approaches varied. Analysis of learning was rarely performed for the main analysis (n = 1), although it was explored by other means. The insight behind the approaches used within and reasons for, or against, alternative approaches were provided. CONCLUSIONS: Widespread awareness of challenges in designing and analysing multicentre trials is identified. Approaches used, and opinions on these, vary both across and within Units, indicating that approaches are dependent on the type of trial. Agreeing principles to guide trial design and analysis across a range of realistic clinical scenarios should be considered. BioMed Central 2020-05-27 /pmc/articles/PMC7251810/ /pubmed/32460815 http://dx.doi.org/10.1186/s13063-020-04318-x Text en © The Author(s) 2020 Open AccessThis 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/. The Creative Commons Public Domain Dedication waiver (http://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 | Methodology Conroy, Elizabeth J. Blazeby, Jane M. Burnside, Girvan Cook, Jonathan A. Gamble, Carrol Managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: a survey to establish current practice |
title | Managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: a survey to establish current practice |
title_full | Managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: a survey to establish current practice |
title_fullStr | Managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: a survey to establish current practice |
title_full_unstemmed | Managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: a survey to establish current practice |
title_short | Managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: a survey to establish current practice |
title_sort | managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: a survey to establish current practice |
topic | Methodology |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7251810/ https://www.ncbi.nlm.nih.gov/pubmed/32460815 http://dx.doi.org/10.1186/s13063-020-04318-x |
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