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...
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doaj-650714c609594d768add72926a27b3ca2021-07-02T13:01:12ZengBMJ Publishing GroupBMJ Open2044-60552021-03-0111310.1136/bmjopen-2020-045803Discrete choice experiment (DCE) to quantify the influence of trial features on the decision to participate in cystic fibrosis (CF) clinical trialsRebecca Anne Dobra0Marco Boeri1Stuart Elborn2Susan Madge3National Heart Lung institute, Imperial College London, London, UKHealth Preference Assessments, RTI Health Solutions, Belfast, UKSchool of Medicine, Dentistry and Biomedical Sciences, Queens University of Belfast, Belfast, UKDepartment of Adult Cystic Fibrosis, Royal Brompton Hospital, London, UKIntroduction 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.https://bmjopen.bmj.com/content/11/3/e045803.full |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Rebecca Anne Dobra Marco Boeri Stuart Elborn Susan Madge |
spellingShingle |
Rebecca Anne Dobra Marco Boeri Stuart Elborn Susan Madge Discrete choice experiment (DCE) to quantify the influence of trial features on the decision to participate in cystic fibrosis (CF) clinical trials BMJ Open |
author_facet |
Rebecca Anne Dobra Marco Boeri Stuart Elborn Susan Madge |
author_sort |
Rebecca Anne Dobra |
title |
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_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_sort |
discrete choice experiment (dce) to quantify the influence of trial features on the decision to participate in cystic fibrosis (cf) clinical trials |
publisher |
BMJ Publishing Group |
series |
BMJ Open |
issn |
2044-6055 |
publishDate |
2021-03-01 |
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. |
url |
https://bmjopen.bmj.com/content/11/3/e045803.full |
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