Predicting EuroQoL 5 Dimensions 5 Levels (EQ-5D-5L) Utilities from Older People's Quality of Life Brief Questionnaire (OPQoL-Brief) Scores
- PMID: 28623629
- DOI: 10.1007/s40271-017-0259-3
Predicting EuroQoL 5 Dimensions 5 Levels (EQ-5D-5L) Utilities from Older People's Quality of Life Brief Questionnaire (OPQoL-Brief) Scores
Abstract
Background and objective: Economic evaluation of healthcare treatment and services targeted at older people requires measurement of utility-based quality-of-life outcomes but it is not always possible to collect such outcome data. It may, however, be possible to estimate these outcomes using non-utility measures of quality of life where the latter have been collected. The objective of this study was to develop a regression-based algorithm to map a non-utility-based outcome, the Older People's Quality of Life brief questionnaire (OPQoL-brief), onto a utility-based outcome, the EuroQoL 5 Dimensions 5 Levels (EQ-5D-5L).
Methods: The estimation sample comprised 330 community-based Australian older people (>65 years), while the validation sample consisted of 293 older people from a separate study. Six regression techniques were employed to estimate utilities from OPQoL-brief. The predictive accuracy of 54 regression models (six regression techniques × nine model specifications) was assessed using six criteria: mean absolute error (MAE), root mean squared error (RMSE), correlation, distribution of predicted utilities, distribution of residuals, and proportion of predictions with absolute errors <0.05.
Results: The 54 regression models predicted EQ-5D-5L utilities that performed differently when assessed by the six criteria. However, best results were obtained from an ordinary least squares (OLS) model where all 13 OPQoL-brief items were included as continuous variables (OLS 4). RMSE and MAE estimates for this model (0.2201 and 0.1638, respectively) were within the range of published estimates.
Conclusions: It is possible to predict valid utilities from OPQoL-brief using regression methods. We recommend OLS model (4) for this exercise.
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