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The online version of this article (doi:10.1186/1472-6963-12-1) contains supplementary material, which is available to authorized users.
The authors declare that they have no competing interests.
Study concept and design: NU, WKP, AS & DDT. Drafting of the manuscript: NU & DDT. Critical revision of the manuscript for important intellectual content: NU, DL, MS, WKP, AS & DDT. Administrative, technical, or material support: NU, MS, WKP, AS & DDT. Study supervision: NU, WKP & DDT. All authors read and approved the final manuscript.
Patients often express strong preferences for the forms of treatment available for their disease. Incorporating these preferences into the process of treatment decision-making might improve patients' adherence to treatment, contributing to better outcomes. We describe the methodology used in a study aiming to assess treatment outcomes when patients' preferences for treatment are closely matched to recommended treatments.
Participants included patients with moderate and severe psoriasis attending outpatient dermatology clinics at the University Medical Centre Mannheim, University of Heidelberg, Germany. A self-administered online survey used conjoint analysis to measure participants' preferences for psoriasis treatment options at the initial study visit. Physicians' treatment recommendations were abstracted from each participant's medical records. The Preference Matching Index (PMI), a measure of concordance between the participant's preferences for treatment and the physician's recommended treatment, was determined for each participant at t1 (initial study visit). A clinical outcome measure, the Psoriasis Area and Severity Index, and two participant-derived outcomes assessing treatment satisfaction and health related quality of life were employed at t1, t2 (twelve weeks post-t1) and t3 (twelve weeks post-t2). Change in outcomes was assessed using repeated measures analysis of variance. The association between participants' PMI scores at t1 and outcomes at t2 and t3 was evaluated using multivariate regressions analysis.
We describe methods for capturing concordance between patients' treatment preferences and recommended treatment and for assessing its association with specific treatment outcomes. The methods are intended to promote the incorporation of patients' preferences in treatment decision-making, enhance treatment satisfaction, and improve treatment effectiveness through greater adherence.