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Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention

BACKGROUND: Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately pow...

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Autores principales: Claire, Ravinder, Gluud, Christian, Berlin, Ivan, Coleman, Tim, Leonardi-Bee, Jo
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BioMed Central 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7702700/
https://www.ncbi.nlm.nih.gov/pubmed/33256626
http://dx.doi.org/10.1186/s12874-020-01169-7
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author Claire, Ravinder
Gluud, Christian
Berlin, Ivan
Coleman, Tim
Leonardi-Bee, Jo
author_facet Claire, Ravinder
Gluud, Christian
Berlin, Ivan
Coleman, Tim
Leonardi-Bee, Jo
author_sort Claire, Ravinder
collection PubMed
description BACKGROUND: Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms. METHODS: We describe using feasibility and pilot trial data in the Trial Sequential Analysis software to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using trial sequential analysis methods to assess the intervention’s effects. RESULTS: We provide a worked example to illustrate how we successfully used the Trial Sequential Analysis software to arrive at a sensible sample size for a new randomised clinical trial and use it in the argumentation for research funds for the trial. CONCLUSIONS: Trial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more, similarly designed, future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods.
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spelling pubmed-77027002020-12-01 Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention Claire, Ravinder Gluud, Christian Berlin, Ivan Coleman, Tim Leonardi-Bee, Jo BMC Med Res Methodol Research Article BACKGROUND: Assessing benefits and harms of health interventions is resource-intensive and often requires feasibility and pilot trials followed by adequately powered randomised clinical trials. Data from feasibility and pilot trials are used to inform the design and sample size of the adequately powered randomised clinical trials. When a randomised clinical trial is conducted, results from feasibility and pilot trials may be disregarded in terms of benefits and harms. METHODS: We describe using feasibility and pilot trial data in the Trial Sequential Analysis software to estimate the required sample size for one or more trials investigating a behavioural smoking cessation intervention. We show how data from a new, planned trial can be combined with data from the earlier trials using trial sequential analysis methods to assess the intervention’s effects. RESULTS: We provide a worked example to illustrate how we successfully used the Trial Sequential Analysis software to arrive at a sensible sample size for a new randomised clinical trial and use it in the argumentation for research funds for the trial. CONCLUSIONS: Trial Sequential Analysis can utilise data from feasibility and pilot trials as well as other trials, to estimate a sample size for one or more, similarly designed, future randomised clinical trials. As this method uses available data, estimated sample sizes may be smaller than they would have been using conventional sample size estimation methods. BioMed Central 2020-11-30 /pmc/articles/PMC7702700/ /pubmed/33256626 http://dx.doi.org/10.1186/s12874-020-01169-7 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 Research Article
Claire, Ravinder
Gluud, Christian
Berlin, Ivan
Coleman, Tim
Leonardi-Bee, Jo
Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention
title Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention
title_full Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention
title_fullStr Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention
title_full_unstemmed Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention
title_short Using Trial Sequential Analysis for estimating the sample sizes of further trials: example using smoking cessation intervention
title_sort using trial sequential analysis for estimating the sample sizes of further trials: example using smoking cessation intervention
topic Research Article
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7702700/
https://www.ncbi.nlm.nih.gov/pubmed/33256626
http://dx.doi.org/10.1186/s12874-020-01169-7
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