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

ABSTRACT: 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.

SUBMITTER: Cheung YK

PROVIDER: S-EPMC7282396 | biostudies-literature | 2020 Jun

REPOSITORIES: biostudies-literature

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

Cheung Ying Kuen YK Wood Dallas D Zhang Kangkang K Ridenour Ty A TA Derby Lilly L St Onge Tara T Duan Naihua N Duer-Hefele Joan J Davidson Karina W KW Kronish Ian I Moise Nathalie N

BMJ open 20200607 6

<h4>Objective</h4>To describe individual patient preferences for Personalised Trials and to identify factors and conditions associated with patient preferences.<h4>Design</h4>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.<h4>Setting</h4>Online survey of adults with a ...[more]

PMID: 32513886

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Project description:BackgroundTofacitinib, an oral Janus kinase inhibitor approved for the treatment of rheumatoid arthritis (RA), provides patients with an alternative to subcutaneously or intravenously administered biologic disease-modifying antirheumatic drugs (DMARDs). Little is known about patient preference for novel RA treatments.ObjectiveTo investigate patient preferences for attributes associated with RA treatments.MethodsA choice-based conjoint survey was mailed to 1400 randomly selected commercially insured patients (aged 21-80 years) diagnosed with RA, who were continuously enrolled from May 1, 2012, through April 30, 2013, and had ≥2 medical claims for International Classification of Diseases, Ninth Revision, Clinical Modification diagnosis code 714.0 and no previous biologic DMARD use. Treatment attributes included route of administration; monthly out-of-pocket cost; frequency of administration; ability to reduce daily joint pain and swelling; likelihood of serious adverse events; improvement in the ability to perform daily tasks; and medication burden. Mean attribute importance scores were calculated after adjusting for patient demographics (eg, age, sex, years since diagnosis) using a hierarchical Bayes model. Patient preferences for each treatment attribute were ranked by the importance score. Part-worth utilities (ie, preference scores) were used to perform a conjoint market simulation.ResultsA total of 380 patients (response rate, 27.1%) returned the survey. Their mean age (± standard deviation) was 54.9 (± 9.3) years. Nonrespondents were 2 years younger (mean, 52.9 years; P = .002) but did not differ significantly from respondents in known clinical characteristics. After adjustment for demographic characteristics, mean patients' ranking of treatment attribute importance, in decreasing order, was route of administration, 34.1 (± 15.5); frequency of administration, 16.4 (± 6.8); serious adverse events, 12.0 (± 9.3); cost, 10.1 (± 6.2); medication burden, 9.8 (± 8.2); joint pain reduction, 8.9 (± 3.8); and daily tasks improvement, 8.8 (± 4.7). For the route of administration attribute, the part-worth utility was highest for the oral route. Conjoint simulation results showed that 56.4% of respondents would prefer an oral route of administration.ConclusionBased on this survey completed by 380 patients with RA, commercially insured patients with RA consider the route of administration to be the most important attribute of their RA treatment. In this study, the majority (56.4%) of patients preferred the oral route of administration over other routes. Understanding patient preferences may help to inform provider and payer decisions in treatment selection that may enhance patient adherence to therapy.

Dataset Information

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

Publications

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

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