Managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: a survey to establish current practice
Abstract 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...
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doaj-d23a09064673439192a3ec177fa1c5df2020-11-25T03:46:13ZengBMCTrials1745-62152020-05-0121111310.1186/s13063-020-04318-xManaging clustering effects and learning effects in the design and analysis of multicentre randomised trials: a survey to establish current practiceElizabeth J. Conroy0Jane M. Blazeby1Girvan Burnside2Jonathan A. Cook3Carrol Gamble4Department of Health Data Science, University of Liverpool, a member of Liverpool Health PartnersCentre for Surgical Research, Bristol Biomedical Research Centre, Population Health Sciences, University of BristolDepartment of Health Data Science, University of Liverpool, a member of Liverpool Health PartnersCentre for Statistics in Medicine, University of OxfordDepartment of Health Data Science, University of Liverpool, a member of Liverpool Health PartnersAbstract 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.http://link.springer.com/article/10.1186/s13063-020-04318-xTrials, Clinical Trials Unit, Clinical trial, Randomised controlled trial, Complex interventionSurgical interventionTrial designTrial analysisSurveyClustering |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Elizabeth J. Conroy Jane M. Blazeby Girvan Burnside Jonathan A. Cook Carrol Gamble |
spellingShingle |
Elizabeth J. Conroy Jane M. Blazeby Girvan Burnside Jonathan A. Cook Carrol Gamble Managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: a survey to establish current practice Trials Trials, Clinical Trials Unit, Clinical trial, Randomised controlled trial, Complex intervention Surgical intervention Trial design Trial analysis Survey Clustering |
author_facet |
Elizabeth J. Conroy Jane M. Blazeby Girvan Burnside Jonathan A. Cook Carrol Gamble |
author_sort |
Elizabeth J. Conroy |
title |
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_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_sort |
managing clustering effects and learning effects in the design and analysis of multicentre randomised trials: a survey to establish current practice |
publisher |
BMC |
series |
Trials |
issn |
1745-6215 |
publishDate |
2020-05-01 |
description |
Abstract 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. |
topic |
Trials, Clinical Trials Unit, Clinical trial, Randomised controlled trial, Complex intervention Surgical intervention Trial design Trial analysis Survey Clustering |
url |
http://link.springer.com/article/10.1186/s13063-020-04318-x |
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