Myeloma Patient Value Mapping: A Discrete Choice Experiment on Myeloma Treatment Preferences in the UK
Authors Fifer S, Galinsky J, Richard S
Received 30 April 2020
Accepted for publication 2 July 2020
Published 28 July 2020 Volume 2020:14 Pages 1283—1293
Checked for plagiarism Yes
Review by Single anonymous peer review
Peer reviewer comments 2
Editor who approved publication: Dr Johnny Chen
Simon Fifer,1 Jayne Galinsky,2 Sarah Richard3
1Community and Patient Preference Research (CaPPRe), Sydney, NSW, Australia; 2Myeloma UK, Edinburgh, Scotland; 3PRMA, Edinburgh, Scotland
Correspondence: Simon Fifer
Community and Patient Preference Research (CaPPRe), Sydney, NSW, Australia
Tel +61 403 862 091
Background: Myeloma is an incurable life-threatening hematological cancer. Recent treatment developments have seen improvements in survival; however, while patients are living longer, they are living with symptoms and treatment side effects.
Objective: To evaluate myeloma patients’ preferences for treatment using a discrete choice experiment (DCE). This study set out to define the relative importance of key treatment attributes, characterize the risk-benefit trade-offs in patients’ decision-making, and to analyze the predictive power of basic demographic factors.
Methods: Four hundred seventy-five myeloma patients in the UK were invited to participate by Myeloma UK. Data were collected using DCEs through an online survey. The DCEs presented patients with 10 choice scenarios, each with 2 treatment options described by 7 attributes, and a “no treatment” option. The DCE data were modelled using a latent class model (LCM). The effects of demographic characteristics were also examined.
Results: Not surprisingly, average survival was most important to all patients but there were significant contrasts between the class preferences. The LCM revealed two classes of patients. Patients in Class 1 placed greater importance on average survival and mild-to-moderate side effects, whereas patients in Class 2 focused on the mode of administration and the average out-of-pocket costs. Patients living with others and those diagnosed in the last 5 years were more likely to be in Class 1.
Conclusion: Different treatment features were not valued equally among all myeloma patients. This has important implications for healthcare policy decisions and could be used to guide decisions around the value of new myeloma medicines.
Keywords: discrete choice experiment, patient preferences, myeloma, health technology assessment, dashboards
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