Predicting preference-based utility values using partial proportional odds models.

ABSTRACT:

Background

The majority of analyses on utility data have used ordinary least square (OLS) regressions to explore potential relationships. The aim of this paper is to explore the benefits of response mapping onto health dimension profiles to generate preference-based utility scores using partial proportional odds models (PPOM).

Methods

Models are estimated using EQ-5D data collected in the Health Survey for England and the predicted utility scores are compared with those obtained using OLS regressions. Explanatory variables include age, acute illness, educational level, general health, deprivation and survey year. The expected EQ-5D scores for the PPOMs are obtained by weighting the predicted probabilities of scoring one, two or three for the five health dimensions by the cor

SUBMITTER: Ara R 

PROVIDER: S-EPMC4118278 | biostudies-literature | 2014 Jul

REPOSITORIES: biostudies-literature