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Patient preferences for stratified medicine in psoriasis: a discrete choice experiment
G Dalal1, S J Wright1, C M Vass1,2
1Manchester Centre for Health Economics, The University of Manchester, Manchester, M13 9PL, UK.
Background:
New technologies have enabled the potential for stratified medicine in psoriasis. It is important to understand patients' preferences to enable the informed introduction of stratified medicine, which is likely to involve a number of individual tests that could be collated into a prescribing algorithm for biological drug selection to be used in clinical practice.
Objectives:
To quantify patient preferences for an algorithm-based approach to prescribing biologics ('biologic calculator') in psoriasis.
Methods:
An online survey comprising a discrete choice experiment (DCE) was conducted to elicit the preferences of two purposive samples of adults living with psoriasis in the UK, identified from a psoriasis patient organization (Psoriasis Association) and an online panel provider (Dynata). Respondents chose between two biologic calculators and conventional prescribing described using five attributes: treatment delay; positive predictive value; negative predictive value; risk of infection; and cost saving to the National Health Service. Each participant selected their preferred alternative from six hypothetical choice sets. Additional data, including sociodemographic characteristics, were collected. Choice data were analysed using conditional logit and fully correlated random parameters logit models.
Results:
Data from 212 respondents (67 from the Psoriasis Association and 145 from Dynata) were analysed. The signs of all estimated coefficients were consistent with a priori expectations. Respondents had a strong preference for a high predictive accuracy and avoiding serious infection, but there was evidence of systematic differences in preferences between the samples.
Conclusions:
This study indicates that individuals with psoriasis would value a biologic calculator and suggested that such a biologic calculator should have sufficient accuracy to predict future response and risk of serious infection from the biologic.
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