US Valuation of the EQ-5D Health States

Development and Testing of the D1 Valuation Model

Datos Bibliográficos

ID9100429
AutoresJames W Shaw (0000-0002-7019-3720, National Cancer Institute, autor de correspondencia), Jeffrey Johnson (0000-0001-8290-2857, University of Alberta), Jeffrey A Johnson (0000-0002-3315-7210), Stephen Joel Coons (0000-0001-7977-5156, University of Arizona)
Año2005
Volumen43
Número3
Páginas203-220
Fecha de publicación2005-03-01
Peer ReviewedSí
Open AccessNo
TipoARTICLE
RevistaMedical Care (JOURNAL)
Identificadores de la revistaISSN: 0025-7079 • E-ISSN: 1537-1948
EditorialOvid Technologies (Wolters Kluwer Health) (PUBLISHER)
DOI10.1097/00005650-200503000-00003
PMID15725977
OpenAlexW2096782143
IdiomaEN
Citas recibidas150
Referencias citadas24

PURPOSE: The EQ-5D is a brief, multiattribute, preference-based health status measure. This article describes the development of a statistical model for generating US population-based EQ-5D preference weights. METHODS: A multistage probability sample was selected from the US adult civilian noninstitutional population. Respondents valued 13 of 243 EQ-5D health states using the time trade-off (TTO) method. Data for 12 states were used in econometric modeling. The TTO valuations were linearly transformed to lie on the interval [-1, 1]. Methods were investigated to account for interaction effects caused by having problems in multiple EQ-5D dimensions. Several alternative model specifications (eg, pooled least squares, random effects) also were considered. A modified split-sample approach was used to evaluate the predictive accuracy of the models. All statistical analyses took into account the clustering and disproportionate selection probabilities inherent in our sampling design. RESULTS: Our D1 model for the EQ-5D included ordinal terms to capture the effect of departures from perfect health as well as interaction effects. A random effects specification of the D1 model yielded a good fit for the observed TTO data, with an overall R of 0.38, a mean absolute error of 0.025, and 7 prediction errors exceeding 0.05 in absolute magnitude. CONCLUSIONS: The D1 model best predicts the values for observed health states. The resulting preference weight estimates represent a significant enhancement of the EQ-5D's utility for health status assessment and economic analysis in the US

Econometrics · Economics · Environmental health · EQ-5D · Meta-analysis · Ordinal data · Population · Preference · Random effects model · Sample (material) · Sample size determination · Sampling (signal processing) · Simple random sample · Statistics · Valuation (finance) · Computer Science · Economic and Environmental Valuation · Health disparities and outcomes · Health Systems, Economic Evaluations, Quality of Life · Mathematics · Medicine