Grant Number:	1R03CA168351-01 Interpret this number
Primary Investigator:	Monahan, Patrick
Organization:	Indiana University Indianapolis
Project Title:	Estimating Health Utilities Using Qol Data Among Patients with Gyn Malignancies
Fiscal Year:	2012

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.





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