Towards patient-centered care for depression: Conjoint methods to tailor treatment based on preferences

Department of Family Medicine and Community Health, School of Medicine, University of Pennsylvania Philadelphia, Pennsylvania.
The patient (Impact Factor: 1.96). 01/2010; 3(3):145-157. DOI: 10.2165/11530660
Source: PubMed

ABSTRACT BACKGROUND: Although antidepressants and counseling have been shown to be effective in treating patients with depression, non-treatment or under-treatment for depression is common especially among the elderly and minorities. Previous work on patient preferences has focused on medication versus counseling, but less is known about the value patients place on attributes of medication and counseling. OBJECTIVE: Conjoint analysis has been recognized as a valuable means of assessing patient treatment preferences. We examine how conjoint analysis be used to determine the relative importance of various attributes of depression treatment at the group level as well as to determine the range of individual-level relative preference weights for specific depression treatment attributes. In addition we use conjoint analysis to predict what modifications in treatment characteristics are associated with a change in the stated preferred alternative. STUDY DESIGN: 86 adults who participated in an internet-based panel responded to an on-line discrete choice task about depression treatment. Participants chose between medication and counseling based on choice sets presented first for a "mild depression" scenario and then for a "severe depression" scenario. Participants were given 18 choice sets which varied for medication based on type of side effect (nausea, dizziness, and sexual dysfunction) and severity of side effect (mild, moderate, and severe); and for counseling based on frequency of counseling sessions (once per week or every other week) and location of the sessions (mental health professional's office, primary care doctor's office or office of a spiritual counselor). RESULTS: Treatment type (counseling vs. medication) appeared to be more important in driving treatment choice than any specific attribute that was studied. Specifically counseling was preferred by most of the respondents. After treatment type, location of treatment and frequency of treatment were important considerations. Preferred attributes were similar in both the mild and severe depression scenarios. Side effect severity appeared to be most important in driving treatment choice as compared with the other attributes studied. Individual-level relative preferences for treatment type revealed a distribution that was roughly bimodal with 27 participants who had a strong preference for counseling and 14 respondents who had a strong preference for medication. CONCLUSION: Estimating individual-level preferences for treatment type allowed us to see the variability in preferences and determine which participants had a strong affinity for medication or counseling.

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Available from: Joseph Gallo, Aug 11, 2014
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    ABSTRACT: Background and Objective In health technology assessment, the evidence obtained from clinical trials regarding multiple clinical outcomes is used to support reimbursement claims. At present, the relevance of these outcome measures for patients is, however, not systematically assessed, and judgments on their relevance may differ among patients and healthcare professionals. The analytic hierarchy process (AHP) is a technique for multi-criteria decision analysis that can be used for preference elicitation. In the present study, we explored the value of using the AHP to prioritize the relevance of outcome measures for major depression by patients, psychiatrists and psychotherapists, and to elicit preferences for alternative healthcare interventions regarding this weighted set of outcome measures. Methods Supported by the pairwise comparison technique of the AHP, a patient group and an expert group of psychiatrists and psychotherapists discussed and estimated the priorities of the clinical outcome measures of antidepressant treatment. These outcome measures included remission of depression, response to drug treatment, no relapse, (serious) adverse events, social function, no anxiety, no pain, and cognitive function. Clinical evidence on the outcomes of three antidepressants regarding these outcome measures was derived from a previous benefit assessment by the Institute for Quality and Efficiency in Health Care (IQWiG; Institut für Qualität und Wirtschaftlichkeit im Gesundheitswesen). Results The most important outcome measures according to the patients were, in order of decreasing importance: response to drug treatment, cognitive function, social function, no anxiety, remission, and no relapse. The patients and the experts showed some remarkable differences regarding the relative importance of response (weight patients = 0.37; weight experts = 0.05) and remission (weight patients = 0.09; weight experts = 0.40); however, both experts and patients agreed upon the list of the six most important measures, with experts only adding one additional outcome measure. Conclusions The AHP can easily be used to elicit patient preferences and the study has demonstrated differences between patients and experts. The AHP is useful for policy makers in combining multiple clinical outcomes of healthcare interventions grounded in randomized controlled trials in an overall health economic evaluation. This may be particularly relevant in cases where different outcome measures lead to conflicting results about the best alternative to reimburse. Alternatively, AHP may also support researchers in selecting (primary) outcome measures with the highest relevance.
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    ABSTRACT: The application of conjoint analysis (including discrete-choice experiments and other multiattribute stated-preference methods) in health has increased rapidly over the past decade. A wider acceptance of these methods is limited by an absence of consensus-based methodological standards. The International Society for Pharmacoeconomics and Outcomes Research (ISPOR) Good Research Practices for Conjoint Analysis Task Force was established to identify good research practices for conjoint-analysis applications in health. The task force met regularly to identify the important steps in a conjoint analysis, to discuss good research practices for conjoint analysis, and to develop and refine the key criteria for identifying good research practices. ISPOR members contributed to this process through an extensive consultation process. A final consensus meeting was held to revise the article using these comments, and those of a number of international reviewers. Task force findings are presented as a 10-item checklist covering: 1) research question; 2) attributes and levels; 3) construction of tasks; 4) experimental design; 5) preference elicitation; 6) instrument design; 7) data-collection plan; 8) statistical analyses; 9) results and conclusions; and 10) study presentation. A primary question relating to each of the 10 items is posed, and three sub-questions examine finer issues within items. Although the checklist should not be interpreted as endorsing any specific methodological approach to conjoint analysis, it can facilitate future training activities and discussions of good research practices for the application of conjoint-analysis methods in health care studies.
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