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Discrete choice experiment (DCE) to quantify the influence of trial features on the decision to participate in cystic fibrosis (CF) clinical trials
INTRODUCTION: Engaging people with cystic fibrosis (CF) in clinical trials is critical to improving outcomes for this fatal disease. Following extensive exploration of engagement in CF trials we believe six key concepts require a quantitative understanding of their influence in the current CF trials...
Autores principales: | , , , , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
BMJ Publishing Group
2021
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7929793/ https://www.ncbi.nlm.nih.gov/pubmed/33653764 http://dx.doi.org/10.1136/bmjopen-2020-045803 |
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author | Dobra, Rebecca Anne Boeri, Marco Elborn, Stuart Kee, Frank Madge, Susan Davies, Jane C |
author_facet | Dobra, Rebecca Anne Boeri, Marco Elborn, Stuart Kee, Frank Madge, Susan Davies, Jane C |
author_sort | Dobra, Rebecca Anne |
collection | PubMed |
description | INTRODUCTION: Engaging people with cystic fibrosis (CF) in clinical trials is critical to improving outcomes for this fatal disease. Following extensive exploration of engagement in CF trials we believe six key concepts require a quantitative understanding of their influence in the current CF trials landscape including how controversial issues like placebos, washouts, stipend provision and location of trial visits are viewed by the CF community and how these might be modified depending on the type of medicine being investigated and the mechanism of access to the drug on trial completion. METHODS AND ANALYSIS: We have designed and will administer an online discrete choice experiment to elicit and quantify preferences of people with CF for these trials’ attributes and estimate the relative importance of an attribute when choosing to participate in a trial. The cross-sectional data generated will be explored using conditional multinomial logit model. Mixed logit models such as the random-parameters logit and a latent class models will be used to explore preference heterogeneity. To determine the relative importance of an attribute, the difference between the attribute level with the highest preference weight and the level with the lowest preference weight will be calculated. ETHICS AND DISSEMINATION: Imperial College London Joint Research Compliance Office has granted ethical approval for this study. Patient consent will be sought following full explanation. No identifying information will be collected. Dissemination will be via international conferences, peer-review publication and patient accessible forums. Major CF trials networks have agreed to incorporate our findings into their review process, meaning our results can realistically influence and optimise CF trial delivery. PROSPERO REGISTRATION NUMBER: CRD42020184886. |
format | Online Article Text |
id | pubmed-7929793 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2021 |
publisher | BMJ Publishing Group |
record_format | MEDLINE/PubMed |
spelling | pubmed-79297932021-03-19 Discrete choice experiment (DCE) to quantify the influence of trial features on the decision to participate in cystic fibrosis (CF) clinical trials Dobra, Rebecca Anne Boeri, Marco Elborn, Stuart Kee, Frank Madge, Susan Davies, Jane C BMJ Open Research Methods INTRODUCTION: Engaging people with cystic fibrosis (CF) in clinical trials is critical to improving outcomes for this fatal disease. Following extensive exploration of engagement in CF trials we believe six key concepts require a quantitative understanding of their influence in the current CF trials landscape including how controversial issues like placebos, washouts, stipend provision and location of trial visits are viewed by the CF community and how these might be modified depending on the type of medicine being investigated and the mechanism of access to the drug on trial completion. METHODS AND ANALYSIS: We have designed and will administer an online discrete choice experiment to elicit and quantify preferences of people with CF for these trials’ attributes and estimate the relative importance of an attribute when choosing to participate in a trial. The cross-sectional data generated will be explored using conditional multinomial logit model. Mixed logit models such as the random-parameters logit and a latent class models will be used to explore preference heterogeneity. To determine the relative importance of an attribute, the difference between the attribute level with the highest preference weight and the level with the lowest preference weight will be calculated. ETHICS AND DISSEMINATION: Imperial College London Joint Research Compliance Office has granted ethical approval for this study. Patient consent will be sought following full explanation. No identifying information will be collected. Dissemination will be via international conferences, peer-review publication and patient accessible forums. Major CF trials networks have agreed to incorporate our findings into their review process, meaning our results can realistically influence and optimise CF trial delivery. PROSPERO REGISTRATION NUMBER: CRD42020184886. BMJ Publishing Group 2021-03-02 /pmc/articles/PMC7929793/ /pubmed/33653764 http://dx.doi.org/10.1136/bmjopen-2020-045803 Text en © Author(s) (or their employer(s)) 2021. Re-use permitted under CC BY-NC. No commercial re-use. See rights and permissions. Published by BMJ. http://creativecommons.org/licenses/by-nc/4.0/ http://creativecommons.org/licenses/by-nc/4.0/This is an open access article distributed in accordance with the Creative Commons Attribution Non Commercial (CC BY-NC 4.0) license, which permits others to distribute, remix, adapt, build upon this work non-commercially, and license their derivative works on different terms, provided the original work is properly cited, appropriate credit is given, any changes made indicated, and the use is non-commercial. See: http://creativecommons.org/licenses/by-nc/4.0/. |
spellingShingle | Research Methods Dobra, Rebecca Anne Boeri, Marco Elborn, Stuart Kee, Frank Madge, Susan Davies, Jane C Discrete choice experiment (DCE) to quantify the influence of trial features on the decision to participate in cystic fibrosis (CF) clinical trials |
title | Discrete choice experiment (DCE) to quantify the influence of trial features on the decision to participate in cystic fibrosis (CF) clinical trials |
title_full | Discrete choice experiment (DCE) to quantify the influence of trial features on the decision to participate in cystic fibrosis (CF) clinical trials |
title_fullStr | Discrete choice experiment (DCE) to quantify the influence of trial features on the decision to participate in cystic fibrosis (CF) clinical trials |
title_full_unstemmed | Discrete choice experiment (DCE) to quantify the influence of trial features on the decision to participate in cystic fibrosis (CF) clinical trials |
title_short | Discrete choice experiment (DCE) to quantify the influence of trial features on the decision to participate in cystic fibrosis (CF) clinical trials |
title_sort | discrete choice experiment (dce) to quantify the influence of trial features on the decision to participate in cystic fibrosis (cf) clinical trials |
topic | Research Methods |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7929793/ https://www.ncbi.nlm.nih.gov/pubmed/33653764 http://dx.doi.org/10.1136/bmjopen-2020-045803 |
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