Eliciting Patient Treatment Preferences for Osteoporosis

描述（由申请人提供）： 博士Fraenkel是一名初级教师，他的目标是成为一个著名学术中心的独立研究员。K23奖将为她提供必要的支持和保护时间，以获得进一步的正式培训，发展她的技能，并牢固地建立她的研究生涯。由于受影响的人数众多，治疗骨质疏松症对公共卫生的影响是巨大的。然而，在个体患者层面，许多人不会从治疗中获益。因此，每个患者对治疗的偏好将取决于他或她如何权衡未来发病率和可能死亡率的风险，而不是长期毒性、令人烦恼的不良反应和治疗相关费用的不确定风险。因此，治疗决策应基于医生的专业知识和明确推导出的患者偏好。 本提案的目的是使用自适应联合分析来：i）检查患者对特定药剂的偏好，ii）确定患者在接受给药（即每日皮下注射）之前需要的额外获益量以及与甲状旁腺激素相关的成本，iii）估计一种极其方便的选择（例如：每年一次的双膦酸盐）将影响患者的偏好和骨质疏松症药物的市场份额。从这项研究中获得的数据将证明患者对现有和有前途的新疗法的偏好强度，特定治疗特征对选择的影响，以及个体患者偏好的原因。他们还将评估患者的社会人口学特征，自我报告的健康状况和健康信念与治疗偏好的关系;并确定患者及其治疗医生是否认为ACA是在临床实践中引起偏好的可接受工具。在患者做出决策的实际时间测量患者偏好是朝着开发实用工具以改善将患者偏好纳入复杂医疗决策的长期目标迈出的重要一步。

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.