Estimating health utilities using QOL data among patients with gyn malignancies

使用妇科恶性肿瘤患者的生活质量数据估计健康效用

• 批准号: 8322222
• 负责人: PATRICK O. MONAHAN
• 金额: $ 8.05万
• 依托单位: INDIANA UNIV-PURDUE UNIV AT INDIANAPOLIS
• 依托单位国家: 美国
• 财政年份: 2012
• 资助国家: 美国
• 起止时间: 2012-05-01 至 2014-04-30
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/8322222
• 关键词: Aftercare; Algorithms; American; Brachytherapy; Breast; Cancer Patient; Caring; Clinical; Clinical Trials; Clinical Trials Database; Colorectal Cancer; Comorbidity; Cost Control; Data; Data Collection; Decision Making; Diagnosis; Dimensions; Disease; Economics; Gynecologic; Health; Health Care Costs; Life; Literature; Living Costs; Maintenance Therapy; Malignant Female Reproductive System Neoplasm; Malignant Neoplasms; Malignant neoplasm of cervix uteri; Malignant neoplasm of ovary; Maps; Measures; Methods; Participant; Patients; Performance; Population; Publishing; Quality of life; Quality-Adjusted Life Years; Research; Research Personnel; Research Project Grants; Resources; Staging; Subgroup; Surveys; Woman; Work; base; bevacizumab; cancer therapy; comparative effectiveness; cost effectiveness; design; effectiveness research; improved; indexing; instrument; patient population; prospective; systematic review; treatment strategy

DESCRIPTION (provided by applicant): This research project is designed to determine if algorithms that use quality of life (QOL) data to predict utilities, which are used in the calculaton of a quality-adjusted life year (QALY) in economic analyses, are comparable to traditional methods of utility assessment. Gynecologic cancers are underrepresented in the published cost-effectiveness literature in part due to the lack of appropriate prospective utilities data collection within clinical trials. However, prospective QOL data are frequently collected within clinical trials. The ability to accurately predict utility values from QOL surveys could open existing, large clinical trial databases for more accurate cost-effectiveness and comparative effectiveness research. This study will prospectively and longitudinally collect utility and qualit of life data from gynecologic cancer patients before, during and after treatment. The utility-prediction algorithms will be compared to utility instruments and will be enhanced to be applicable to the gynecologic cancer patient population. Establishing the performance of utility-estimating algorithms using QOL data in this population will enhance the ability of investigators to conduct comparative effectiveness research of gynecologic cancer treatment strategies. PUBLIC HEALTH RELEVANCE: Gynecologic cancers are underrepresented in the published cost-effectiveness literature in part due to the lack of appropriate prospective utilities data collection within clinical trials. The ability to accurately predict utilities from data that are aready collected within clinical trials could open existing, large clinical trial databases for more accurte cost-effectiveness and comparative effectiveness research. This study is designed to assess and enhance the performance of existing algorithms that have been developed in other cancers for use in gynecologic cancer populations.

描述（由申请人提供）：本研究项目旨在确定使用生活质量（QOL）数据预测效用的算法（用于经济分析中质量调整生命年（QALY）的计算）是否与效用评估的传统方法相当。妇科癌症在已发表的成本效益文献中代表性不足，部分原因是临床试验中缺乏适当的前瞻性效用数据收集。然而，前瞻性QOL数据经常在临床试验中收集。从QOL调查中准确预测效用值的能力可以打开现有的大型临床试验数据库，以进行更准确的成本效益和比较有效性研究。本研究将前瞻性和纵向收集妇科癌症患者治疗前、治疗中和治疗后的效用和生活质量数据。效用预测算法将与效用工具进行比较，并将得到增强，以适用于妇科癌症患者人群。在这一人群中使用QOL数据建立效用估计算法的性能将提高研究者进行妇科癌症治疗策略的比较有效性研究的能力。 公共卫生关系：妇科癌症在已发表的成本效益文献中代表性不足，部分原因是临床试验中缺乏适当的前瞻性效用数据收集。从临床试验中收集的数据中准确预测效用的能力可以打开现有的大型临床试验数据库，以进行更准确的成本效益和比较有效性研究。本研究旨在评估和增强在其他癌症中开发的用于妇科癌症人群的现有算法的性能。