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Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey

OBJECTIVE: To describe individual patient preferences for Personalised Trials and to identify factors and conditions associated with patient preferences. DESIGN: Each participant was presented with 18 conjoint questions via an online survey. Each question provided two choices of Personalised Trials...

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Autores principales: Cheung, Ying Kuen, Wood, Dallas, Zhang, Kangkang, Ridenour, Ty A, Derby, Lilly, St Onge, Tara, Duan, Naihua, Duer-Hefele, Joan, Davidson, Karina W, Kronish, Ian, Moise, Nathalie
Formato: Online Artículo Texto
Lenguaje:English
Publicado: BMJ Publishing Group 2020
Materias:
Acceso en línea:https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7282396/
https://www.ncbi.nlm.nih.gov/pubmed/32513886
http://dx.doi.org/10.1136/bmjopen-2019-036056
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author Cheung, Ying Kuen
Wood, Dallas
Zhang, Kangkang
Ridenour, Ty A
Derby, Lilly
St Onge, Tara
Duan, Naihua
Duer-Hefele, Joan
Davidson, Karina W
Kronish, Ian
Moise, Nathalie
author_facet Cheung, Ying Kuen
Wood, Dallas
Zhang, Kangkang
Ridenour, Ty A
Derby, Lilly
St Onge, Tara
Duan, Naihua
Duer-Hefele, Joan
Davidson, Karina W
Kronish, Ian
Moise, Nathalie
author_sort Cheung, Ying Kuen
collection PubMed
description OBJECTIVE: To describe individual patient preferences for Personalised Trials and to identify factors and conditions associated with patient preferences. DESIGN: Each participant was presented with 18 conjoint questions via an online survey. Each question provided two choices of Personalised Trials that were defined by up to eight attributes, including treatment types, clinician involvement, study logistics and trial burden on a patient. SETTING: Online survey of adults with at least two common chronic conditions in the USA. PARTICIPANTS: A nationally representative sample of 501 individuals were recruited from the Chronic Illness Panel by Harris Poll Online. Participants were recruited from several sources, including emails, social media and telephone recruitment of the target population. MAIN OUTCOME MEASURES: The choice of Personalised Trial design that the participant preferred with each conjoint question. RESULTS: There was large variability in participants’ preferences for the design of Personalised Trials. On average, they preferred certain attributes, such as a short time commitment and no cost. Notably, a population-level analysis correctly predicted 62% of the conjoint responses. An empirical Bayesian analysis of the conjoint data, which supported the estimation of individual-level preferences, improved the accuracy to 86%. Based on estimates of individual-level preferences, patients with chronic pain preferred a long study duration (p≤0.001). Asthma patients were less averse to participation burden in terms of data-collection frequency than patients with other conditions (p=0.002). Patients with hypertension were more cost-sensitive (p<0.001). CONCLUSION: These analyses provide a framework for elucidating individual-level preferences when implementing novel patient-centred interventions. The data showed that patient preference in Personalised Trials is highly variable, suggesting that individual differences must be accounted for when marketing Personalised Trials. These results have implications for advancing precise interventions in Personalised Trials by indicating when rigorous scientific principles, such as frequent monitoring, is feasible in a substantial subset of patients.
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spelling pubmed-72823962020-06-15 Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey Cheung, Ying Kuen Wood, Dallas Zhang, Kangkang Ridenour, Ty A Derby, Lilly St Onge, Tara Duan, Naihua Duer-Hefele, Joan Davidson, Karina W Kronish, Ian Moise, Nathalie BMJ Open Patient-Centred Medicine OBJECTIVE: To describe individual patient preferences for Personalised Trials and to identify factors and conditions associated with patient preferences. DESIGN: Each participant was presented with 18 conjoint questions via an online survey. Each question provided two choices of Personalised Trials that were defined by up to eight attributes, including treatment types, clinician involvement, study logistics and trial burden on a patient. SETTING: Online survey of adults with at least two common chronic conditions in the USA. PARTICIPANTS: A nationally representative sample of 501 individuals were recruited from the Chronic Illness Panel by Harris Poll Online. Participants were recruited from several sources, including emails, social media and telephone recruitment of the target population. MAIN OUTCOME MEASURES: The choice of Personalised Trial design that the participant preferred with each conjoint question. RESULTS: There was large variability in participants’ preferences for the design of Personalised Trials. On average, they preferred certain attributes, such as a short time commitment and no cost. Notably, a population-level analysis correctly predicted 62% of the conjoint responses. An empirical Bayesian analysis of the conjoint data, which supported the estimation of individual-level preferences, improved the accuracy to 86%. Based on estimates of individual-level preferences, patients with chronic pain preferred a long study duration (p≤0.001). Asthma patients were less averse to participation burden in terms of data-collection frequency than patients with other conditions (p=0.002). Patients with hypertension were more cost-sensitive (p<0.001). CONCLUSION: These analyses provide a framework for elucidating individual-level preferences when implementing novel patient-centred interventions. The data showed that patient preference in Personalised Trials is highly variable, suggesting that individual differences must be accounted for when marketing Personalised Trials. These results have implications for advancing precise interventions in Personalised Trials by indicating when rigorous scientific principles, such as frequent monitoring, is feasible in a substantial subset of patients. BMJ Publishing Group 2020-06-07 /pmc/articles/PMC7282396/ /pubmed/32513886 http://dx.doi.org/10.1136/bmjopen-2019-036056 Text en © Author(s) (or their employer(s)) 2020. 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/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 Patient-Centred Medicine
Cheung, Ying Kuen
Wood, Dallas
Zhang, Kangkang
Ridenour, Ty A
Derby, Lilly
St Onge, Tara
Duan, Naihua
Duer-Hefele, Joan
Davidson, Karina W
Kronish, Ian
Moise, Nathalie
Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey
title Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey
title_full Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey
title_fullStr Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey
title_full_unstemmed Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey
title_short Personal preferences for Personalised Trials among patients with chronic diseases: an empirical Bayesian analysis of a conjoint survey
title_sort personal preferences for personalised trials among patients with chronic diseases: an empirical bayesian analysis of a conjoint survey
topic Patient-Centred Medicine
url https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7282396/
https://www.ncbi.nlm.nih.gov/pubmed/32513886
http://dx.doi.org/10.1136/bmjopen-2019-036056
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