John D Hartman1, Benjamin M Craig2. 1. Department of Health Sciences and Administration, University of West Florida, Pensacola, FL, USA. jhartman@uwf.edu. 2. Department of Economics, University of South Florida, Tampa, USA.
Abstract
PURPOSE: Summarizing patient-reported outcomes (PROs) on a quality-adjusted life year (QALY) scale is an essential component to any economic evaluation comparing alternative medical treatments. While multiple studies have compared PRO items and instruments based on their psychometric properties, no study has compared the preference-based summary of the EQ-5D-3L and Patient Reported Outcomes Measurement Information System (PROMIS-29) instruments. As part of this comparison, a major aim of this manuscript is to transform PROMIS-29 utility values to an EQ-5D-3L scale. METHODS: A nationally representative survey of 2623 US adults completed the 29-item PROMIS health profile instrument (PROMIS-29) and the 3-level version of the EQ-5D instrument (EQ-5D-3L). Their responses were summarized on a health utility scale using published estimates. Using regression analysis, PROMIS-29 and EQ-5D-3L utility weights were compared with each other as well as with self-reported general health. RESULTS: PROMIS-29 utility weights were much lower than the EQ-5D-3L weights. However, a correlation coefficient of 0.769 between the utility values of the two instruments suggests that the main discordance is simply a difference in scale between the measures. It is also possible to map PROMIS-29 utility weights onto an EQ-5D-3L scale. EQ-5D-3L losses equal .1784 × (PROMIS-29 Losses).7286. CONCLUSIONS: The published estimates of the PROMIS-29 produce lower utility values than many other health instruments. Mapping the PROMIS-29 estimates to an EQ-5D-3L scale alleviates this issue and allows for a more straightforward comparison between the PROMIS-29 and other common health instruments.
PURPOSE: Summarizing patient-reported outcomes (PROs) on a quality-adjusted life year (QALY) scale is an essential component to any economic evaluation comparing alternative medical treatments. While multiple studies have compared PRO items and instruments based on their psychometric properties, no study has compared the preference-based summary of the EQ-5D-3L and Patient Reported Outcomes Measurement Information System (PROMIS-29) instruments. As part of this comparison, a major aim of this manuscript is to transform PROMIS-29 utility values to an EQ-5D-3L scale. METHODS: A nationally representative survey of 2623 US adults completed the 29-item PROMIS health profile instrument (PROMIS-29) and the 3-level version of the EQ-5D instrument (EQ-5D-3L). Their responses were summarized on a health utility scale using published estimates. Using regression analysis, PROMIS-29 and EQ-5D-3L utility weights were compared with each other as well as with self-reported general health. RESULTS: PROMIS-29 utility weights were much lower than the EQ-5D-3L weights. However, a correlation coefficient of 0.769 between the utility values of the two instruments suggests that the main discordance is simply a difference in scale between the measures. It is also possible to map PROMIS-29 utility weights onto an EQ-5D-3L scale. EQ-5D-3L losses equal .1784 × (PROMIS-29 Losses).7286. CONCLUSIONS: The published estimates of the PROMIS-29 produce lower utility values than many other health instruments. Mapping the PROMIS-29 estimates to an EQ-5D-3L scale alleviates this issue and allows for a more straightforward comparison between the PROMIS-29 and other common health instruments.
Entities:
Keywords:
EQ-5D; HRQoL; Health preference scores; Health state utility values; Health-related quality of life; PROMIS; Patient-reported outcomes; QALY
Authors: C A Marra; S A Marion; D P Guh; M Najafzadeh; F Wolfe; J M Esdaile; A E Clarke; M A Gignac; A H Anis Journal: J Clin Epidemiol Date: 2006-12-22 Impact factor: 6.437
Authors: Dennis G Fryback; Nancy Cross Dunham; Mari Palta; Janel Hanmer; Jennifer Buechner; Dasha Cherepanov; Shani A Herrington; Ron D Hays; Robert M Kaplan; Theodore G Ganiats; David Feeny; Paul Kind Journal: Med Care Date: 2007-12 Impact factor: 2.983
Authors: Tianxin Pan; Brendan Mulhern; Rosalie Viney; Richard Norman; An Tran-Duy; Janel Hanmer; Nancy Devlin Journal: Qual Life Res Date: 2021-06-28 Impact factor: 4.147
Authors: Ena Niño de Guzmán; Laura Martínez García; Ana I González; Monique Heijmans; Jorge Huaringa; Kaisa Immonen; Lyudmil Ninov; Carola Orrego-Villagrán; Javier Pérez-Bracchiglione; Karla Salas-Gama; Andrés Viteri-García; Pablo Alonso-Coello Journal: F1000Res Date: 2020-02-18
Authors: Geoffrey P Wilkin; Stéphane Poitras; John Clohisy; Etienne Belzile; Ira Zaltz; George Grammatopoulos; Gerd Melkus; Kawan Rakhra; Tim Ramsay; Kednapa Thavorn; Paul E Beaulé Journal: Trials Date: 2020-08-18 Impact factor: 2.279