Appendix_1_online_supp – Supplemental material for Individual Value Clarification Methods Based on Conjoint Analysis: A Systematic Review of Common Practice in Task Design, Statistical Analysis, and Presentation of Results

<p>Supplemental material, Appendix_1_online_supp for Individual Value Clarification Methods Based on Conjoint Analysis: A Systematic Review of Common Practice in Task Design, Statistical Analysis, and Presentation of Results by Marieke G. M. Weernink, Janine A. van Til, Holly O. Witteman, Liana Fraenkel and Maarten J. IJzerman in Medical Decision Making</p

Appendix_2_online_supp – Supplemental material for Individual Value Clarification Methods Based on Conjoint Analysis: A Systematic Review of Common Practice in Task Design, Statistical Analysis, and Presentation of Results

<p>Supplemental material, Appendix_2_online_supp for Individual Value Clarification Methods Based on Conjoint Analysis: A Systematic Review of Common Practice in Task Design, Statistical Analysis, and Presentation of Results by Marieke G. M. Weernink, Janine A. van Til, Holly O. Witteman, Liana Fraenkel and Maarten J. IJzerman in Medical Decision Making</p

Involving Patients in Weighting Benefits and Harms of Treatment in Parkinson's Disease

<div><p>Introduction</p><p>Little is known about how patients weigh benefits and harms of available treatments for Parkinson’s Disease (oral medication, deep brain stimulation, infusion therapy). In this study we have (1) elicited patient preferences for benefits, side effects and process characteristics of treatments and (2) measured patients’ preferred and perceived involvement in decision-making about treatment.</p><p>Methods</p><p>Preferences were elicited using a best-worst scaling case 2 experiment. Attributes were selected based on 18 patient-interviews: treatment modality, tremor, slowness of movement, posture and balance problems, drowsiness, dizziness, and dyskinesia. Subsequently, a questionnaire was distributed in which patients were asked to indicate the most and least desirable attribute in nine possible treatment scenarios. Conditional logistic analysis and latent class analysis were used to estimate preference weights and identify subgroups. Patients also indicated their preferred and perceived degree of involvement in treatment decision-making (ranging from active to collaborative to passive).</p><p>Results</p><p>Two preference patterns were found in the patient sample (N = 192). One class of patients focused largely on optimising the process of care, while the other class focused more on controlling motor-symptoms. Patients who had experienced advanced treatments, had a shorter disease duration, or were still employed were more likely to belong to the latter class. For both classes, the benefits of treatment were more influential than the described side effects. Furthermore, many patients (45%) preferred to take the lead in treatment decisions, however 10.8% perceived a more passive or collaborative role instead.</p><p>Discussion</p><p>Patients weighted the benefits and side effects of treatment differently, indicating there is no “one-size-fits-all” approach to choosing treatments. Moreover, many patients preferred an active role in decision-making about treatment. Both results stress the need for physicians to know what is important to patients and to share treatment decisions to ensure that patients receive the treatment that aligns with their preferences.</p></div

Questions and answer-categories used to assess patient’s preferred and perceived decision role in treatment decision making.

<p>Questions and answer-categories used to assess patient’s preferred and perceived decision role in treatment decision making.</p

Background, socio-demographic and clinical characteristics (N = 229).

<p>Background, socio-demographic and clinical characteristics (N = 229).</p

Treatment desirability in Parkinson’s Disease based on conditional logit analysis and latent class analysis (N = 192).

<p>Treatment desirability in Parkinson’s Disease based on conditional logit analysis and latent class analysis (N = 192).</p

Example of a treatment profile in which the patient had to indicate the least and most desirable characteristic of treatment.

<p>Example of a treatment profile in which the patient had to indicate the least and most desirable characteristic of treatment.</p

Importance weights of the attributes estimated from the conditional logit analysis and latent class analysis.

<p>Importance weights of the attributes estimated from the conditional logit analysis and latent class analysis.</p