Eliciting Patient Treatment Preferences for Osteoporosis

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DESCRIPTION (provided by applicant): Dr. Fraenkel is a junior faculty member whose goal is to become an independent investigator in a renowned academic center. The K23 award will provide her with the support and protected time necessary to obtain further formal training, develop her skills and firmly establish her research career. Because of the numbers of persons affected, the public health impact of treating osteoporosis is enormous. However, at the individual patient level, many will not derive any benefit from treatment. Each individual patient's preference for treatment will therefore depend on how he or she weighs the risk of future morbidity and possible mortality over the uncertain risk of long-term toxicity, bothersome adverse effects, and costs related to treatment. Consequently, treatment decisions should be based on physician expertise and explicitly derived patient preferences. The objectives of this proposal are to use Adaptive Conjoint Analysis to: i) examine patient preferences for specific pharmaceutical agents, ii) determine the amount of added benefit patients require before accepting the administration (i.e. daily subcutaneous injections) and costs associated with parathyroid hormone, and iii) estimate how the availability of an extremely convenient option (e.g.: once yearly bisphosphonate) would impact on patient preferences and the market share of osteoporosis medications. The data derived from this study will demonstrate patients' strength of preference for available and promising new therapies, the influence of specific treatment characteristics on choice, and the reasons underlying individual patient's preferences. They will also assess how patient sociodemographic characteristics, self-reported health status, and health beliefs relate to treatment preferences; and determine whether patients and their treating physicians consider ACA to be an acceptable tool to elicit preferences in clinical practice. Measurement of patient preferences in patients at the actual time of decision-making is an important step towards a long-term goal of developing a practical tool to improve incorporation of patient preferences into complex medical decisions.