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Khairnar R , Pugh SL , Sandler HM , Lee WR , Villalonga Olives E , Mullins CD , Palumbo FB , Bruner DW , Shaya FT , Bentzen SM , Shah AB , Malone SC , Michalski JM , Dayes IS , Seaward SA , Albert M , Currey AD , Pisansky TM , Chen Y , Horwitz EM , DeNittis AS , Feng FY , Mishra MV
Mapping expanded prostate cancer index composite to EQ5D utilities to inform economic evaluations in prostate cancer: Secondary analysis of NRG/RTOG 0415
PLoS One. 2021 ;16(4) :e0249123
AbstractPURPOSE: The Expanded Prostate Cancer Index Composite (EPIC) is the most commonly used patient reported outcome (PRO) tool in prostate cancer (PC) clinical trials, but health utilities associated with the different health states assessed with this tool are unknown, limiting our ability to perform cost-utility analyses. This study aimed to map EPIC tool to EuroQoL-5D-3L (EQ5D) to generate EQ5D health utilities. METHODS AND MATERIALS: This is a secondary analysis of a prospective, randomized non-inferiority clinical trial, conducted between 04/2006 and 12/2009 at cancer centers across the United States, Canada, and Switzerland. Eligible patients included men >18 years with a known diagnosis of low-risk PC. Patient HRQoL data were collected using EPIC and health utilities were obtained using EQ5D. Data were divided into an estimation sample (n = 765, 70%) and a validation sample (n = 327, 30%). The mapping algorithms that capture the relationship between the instruments were estimated using ordinary least squares (OLS), Tobit, and two-part models. Five-fold cross-validation (in-sample) was used to compare the predictive performance of the estimated models. Final models were selected based on root mean square error (RMSE). RESULTS: A total of 565 patients in the estimation sample had complete information on both EPIC and EQ5D questionnaires at baseline. Mean observed EQ5D utility was 0.90±0.13 (range: 0.28-1) with 55% of patients in full health. OLS models outperformed their counterpart Tobit and two-part models for all pre-determined model specifications. The best model fit was: "EQ5D utility = 0.248541 + 0.000748*(Urinary Function) + 0.001134*(Urinary Bother) + 0.000968*(Hormonal Function) + 0.004404*(Hormonal Bother)- 0.376487*(Zubrod) + 0.003562*(Urinary Function*Zubrod)"; RMSE was 0.10462. CONCLUSIONS: This is the first study to identify a comprehensive set of mapping algorithms to generate EQ5D utilities from EPIC domain/ sub-domain scores. The study results will help estimate quality-adjusted life-years in PC economic evaluations.
Notes1932-6203 Khairnar, Rahul Orcid: 0000-0002-2373-0431 Pugh, Stephanie L Sandler, Howard M Lee, W Robert Villalonga Olives, Ester Mullins, C Daniel Palumbo, Francis B Bruner, Deborah W Shaya, Fadia T Bentzen, Soren M Shah, Amit B Malone, Shawn C Michalski, Jeff M Dayes, Ian S Seaward, Samantha A Albert, Michele Currey, Adam D Pisansky, Thomas M Chen, Yuhchyau Horwitz, Eric M DeNittis, Albert S Feng, Felix Y Mishra, Mark V Orcid: 0000-0002-2588-1575 U10 CA180822/CA/NCI NIH HHS/United States U10 CA180868/CA/NCI NIH HHS/United States Journal Article PLoS One. 2021 Apr 14;16(4):e0249123. doi: 10.1371/journal.pone.0249123. eCollection 2021.