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Personal preferences for personalised trials among patients with chronic diseases
An empirical Bayesian analysis of a conjoint survey
Cheung, Y. K., Wood, D., Zhang, K., Ridenour, T. A., Derby, L., St Onge, T., Duan, N., Duer-Hefele, J., Davidson, K. W., Kronish, I., & Moise, N. (2020). Personal preferences for personalised trials among patients with chronic diseases: An empirical Bayesian analysis of a conjoint survey. BMJ Open, 10(6), Article 036056. https://doi.org/10.1136/bmjopen-2019-036056
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.
