Can patients diagnosed with schizophrenia complete choice-based conjoint analysis tasks?
ABSTRACT Schizophrenia is a severe mental illness associated with hallucinations, delusions, apathy, poor social functioning, and impaired cognition. Researchers and funders have been hesitant to focus efforts on treatment preferences of patients with schizophrenia because of the perceived cognitive burden that research methods, such as conjoint analysis, place on them.
The objective of this study was to test if patients diagnosed with schizophrenia were able to complete a choice-based conjoint analysis (often referred to as discrete-choice experiments) and to test if meaningful trade-offs were being made.
German outpatients diagnosed with schizophrenia were eligible to participate in this study if they were aged 18-65 years, had received treatment for at least 1 year and were not experiencing acute symptoms. Conjoint analysis tasks were based on six attributes, each with two levels, which were identified via a literature review and focus groups. A psychologist in a professional interview facility presented each respondent with the eight tasks with little explanation. All interviews were recorded, transcribed, and analyzed to verify that respondents understood the tasks. Preferences were assessed using logistic regression, with a correction for clustering.
We found evidence that the 21 patients diagnosed with schizophrenia participating in the study could complete conjoint analysis tasks in a meaningful way. Patients not only related to the scenarios presented in conjoint tasks, but explicitly stated that they used their own preferences to judge which scenarios were better. Statistical analysis confirmed all hypotheses about the attributes (i.e. all attributes had the expected sign). Having a supportive physician, not feeling slowed, and improvements in stressful situations (p < 0.01) were the most important attributes.
We found that patients diagnosed with schizophrenia can complete conjoint analysis tasks, that they base their decisions on their own preferences, and that patients make trade-offs between attributes.
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ABSTRACT: In conjunction with other important movements in contemporary medicine, including evidence-based medicine (EBM), health technology assessment (HTA) has promoted a culture of critical evaluation. Despite this impact, institutional and methodological challenges are associated with HTA. For example, only in recent years has HTA attempted an open dialogue with patients; however, this is normally done by giving them a "seat" at the HTA decision-making table, rather than by more scientific means. The aim of this study was to develop a working definition of patient-based HTA, to identify the current barriers to adopting a patient-based model, and to formulate a vision of how a patient-based HTA could be used to promote patient empowerment and patient-centered care. In the ideal setting, a patient-based HTA would promote patient knowledge by providing access to information and promoting an informed dialogue between patients and their healthcare professionals. To implement a patient-based HTA, the focus must turn to the patient's issues and incorporate each patient's unique perspective and preferences. Processes must change to increase patient participation in all levels of HTA and aim to promote empowered patients who can make informed decisions. Present-day HTA is broad and has numerous stakeholders, with none so important as the patient. By asking patient-oriented questions in HTA and better involving patients throughout the entire process, we can easily promote patient empowerment, and as such make patients more capable to play a more active role in healthcare decision making.International Journal of Technology Assessment in Health Care 02/2007; 23(1):30-5. · 1.55 Impact Factor
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ABSTRACT: One focus of health economics is the trade-off between limited resources and the (health) needs of a community. Cost-effectiveness analysis (CEA), while being one of the most accepted evaluation methodologies in health economics, does not account for many important costs and benefits of health care interventions. Some health economists have attempted to modify CEA to account for these deficiencies, while others have been working on alternative methodologies. One group of alternative methodologies can be described as stated preference techniques. These aim to measure both health and non-health outcomes (ie costs and benefits), and include qualitative analysis, conjoint analysis (often referred to as discrete choice analysis/modelling) and willingness to pay (or contingent valuation). This paper provides an overview of stated preference techniques in health economics, with particular focus on their strengths as compared with traditional evaluation methods in health care. The limitations and policy implications of these methods are also discussed.Applied Health Economics and Health Policy 02/2003; 2(4):213-24.
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ABSTRACT: To investigate the impact of health policies on individual well-being, estimate the value to society of new interventions or policies, or predict demand for healthcare, we need information about individuals' preferences. Economists usually use market-based data to analyze preferences, but such data are limited in the healthcare context. Discrete choice experiments are a potentially valuable tool for elicitation and analysis of preferences and thus, for economic analysis of health and health programs. This paper reviews the use of discrete choice experiments to measure consumers' preferences for health and healthcare. The paper provides an overview of the approach and discusses issues that arise when using discrete choice experiments to assess individuals' preferences for health and healthcare.Expert Review of Pharmacoeconomics & Outcomes Research 08/2002; 2(4):319-26. · 1.67 Impact Factor