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Quality of Life Research

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01-09-2018

Testing alternative regression models to predict utilities: mapping the QLQ-C30 onto the EQ-5D-5L and the SF-6D

Auteurs: Admassu N. Lamu, Jan Abel Olsen

Gepubliceerd in: Quality of Life Research | Uitgave 11/2018

Abstract

Purpose

The purpose of the study was to compare alternative statistical techniques to find the best approach for converting QLQ-C30 scores onto EQ-5D-5L and SF-6D utilities, and to estimate the mapping algorithms that best predict these health state utilities.

Methods

772 cancer patients described their health along the cancer-specific instrument (QLQ-C30) and two generic preference-based instruments (EQ-5D-5L and SF-6D). Seven alternative regression models were applied: ordinary least squares, generalized linear model, extended estimating equations (EEE), fractional regression model, beta binomial (BB) regression, logistic quantile regression and censored least absolute deviation. Normalized mean absolute error (NMAE), normalized root mean square error (NRMSE), r-squared (r²) and concordance correlation coefficient (CCC) were used as model performance criteria. Cross-validation was conducted by randomly splitting internal dataset into two equally sized groups to test the generalizability of each model.

Results

In predicting EQ-5D-5L utilities, the BB regression performed best. It gave better predictive accuracy in terms of all criteria in the full sample, as well as in the validation sample. In predicting SF-6D, the EEE performed best. It outperformed in all criteria: NRMSE = 0.1004, NMAE = 0.0798, CCC = 0.842 and r² = 72.7% in the full sample, and NRMSE = 0.1037, NMAE = 0.0821, CCC = 0.8345 and r² = 71.4% in cross-validation.

Conclusions

When only QLQ-C30 data are available, mapping provides an alternative approach to obtain health state utility data for use in cost-effectiveness analyses. Among seven alternative regression models, the BB and the EEE gave the most accurate predictions for EQ-5D-5L and SF-6D, respectively.

vorige artikel Mapping between HAQ-DI and EQ-5D-5L in a Chinese patient population

volgende artikel Modelling a preference-based index for EQ-5D using a non-parametric Bayesian method

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Response mapping produced the highest error (measured in terms of MAE and RMSE) among other models. In response mapping, exact prediction of health state requires correct prediction for each dimension of the target instrument, which rarely achieved in practice. Thus, it can be severely penalized when incorrect prediction is made.

Power variance structure is preferred as it includes the variance of several standard distributions (such as Poisson, gamma or inverse Gaussian) used for modelling health outcomes.

The EEE is estimated by pglm, a user defined Stata command, which shows better convergence properties when the outcome variable is scaled by dividing by its mean [36].

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Titel: Testing alternative regression models to predict utilities: mapping the QLQ-C30 onto the EQ-5D-5L and the SF-6D
Auteurs: Admassu N. Lamu
Jan Abel Olsen
Publicatiedatum: 01-09-2018
Uitgeverij: Springer International Publishing
Gepubliceerd in: Quality of Life Research / Uitgave 11/2018
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI: https://doi.org/10.1007/s11136-018-1981-6

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