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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

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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.
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Voetnoten
1
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.
 
2
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.
 
3
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].
 
Literatuur
1.
go back to reference Brazier, J., Ratcliffe, J., Salomon, J. A., & Tsuchiya, A. (2017). Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press. Brazier, J., Ratcliffe, J., Salomon, J. A., & Tsuchiya, A. (2017). Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press.
3.
go back to reference GBD 2015 Mortality and Causes of Death Collaborators. (2016). Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: A systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1459–1544. https://doi.org/10.1016/s0140-6736(16)31012-1.CrossRef GBD 2015 Mortality and Causes of Death Collaborators. (2016). Global, regional, and national life expectancy, all-cause mortality, and cause-specific mortality for 249 causes of death, 1980–2015: A systematic analysis for the Global Burden of Disease Study 2015. The Lancet, 388(10053), 1459–1544. https://​doi.​org/​10.​1016/​s0140-6736(16)31012-1.CrossRef
7.
go back to reference Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., et al. (1993). The European Organization for Research and Treatment of Cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology. JNCI: Journal of the National Cancer Institute. https://doi.org/10.1093/jnci/85.5.365.CrossRefPubMed Aaronson, N. K., Ahmedzai, S., Bergman, B., Bullinger, M., Cull, A., Duez, N. J., et al. (1993). The European Organization for Research and Treatment of Cancer QLQ-C30: A quality-of-life instrument for use in international clinical trials in oncology. JNCI: Journal of the National Cancer Institute. https://​doi.​org/​10.​1093/​jnci/​85.​5.​365.CrossRefPubMed
8.
go back to reference Richardson, J., McKie, J., & Bariola, E. (2014). Multi attribute utility instruments and their use. In A. J. Culyer (Ed.), Encyclopedia of health economics (pp. 341–357). San Diego: Elsevier Science.CrossRef Richardson, J., McKie, J., & Bariola, E. (2014). Multi attribute utility instruments and their use. In A. J. Culyer (Ed.), Encyclopedia of health economics (pp. 341–357). San Diego: Elsevier Science.CrossRef
11.
go back to reference Rabin, R., Oemar, M., Oppe, M., Janssen, B., & Herdman, M. (2011). EQ-5D-5L user guide: Basic information on how to use the EQ-5D-5L instruments. Rotterdam: EuroQoL Group. Rabin, R., Oemar, M., Oppe, M., Janssen, B., & Herdman, M. (2011). EQ-5D-5L user guide: Basic information on how to use the EQ-5D-5L instruments. Rotterdam: EuroQoL Group.
12.
go back to reference NICE (National Institute for Health and Care Excellence). (2013). Guide to the methods of technology appraisal. London: National Health Service. Retrieved September 18, 2017, from http://www.nice.org.uk. NICE (National Institute for Health and Care Excellence). (2013). Guide to the methods of technology appraisal. London: National Health Service. Retrieved September 18, 2017, from http://​www.​nice.​org.​uk.
34.
go back to reference Fox, J. (2015). Applied regression analysis and generalized linear models. Thousand Oaks: SAGE. Fox, J. (2015). Applied regression analysis and generalized linear models. Thousand Oaks: SAGE.
36.
go back to reference Basu, A. (2005). Extended generalized linear models: Simultaneous estimation of flexible link and variance functions. The Stata Journal, 5(4), 501–516. Basu, A. (2005). Extended generalized linear models: Simultaneous estimation of flexible link and variance functions. The Stata Journal, 5(4), 501–516.
37.
go back to reference Papke, L. E., & Wooldridge, J. M. (1996). Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics, 11(6), 619–632.CrossRef Papke, L. E., & Wooldridge, J. M. (1996). Econometric methods for fractional response variables with an application to 401(k) plan participation rates. Journal of Applied Econometrics, 11(6), 619–632.CrossRef
40.
go back to reference Swearingen, C. J., Castro, M. S. M., & Bursac, Z. (2012). Inflated beta regression: Zero, one, and everything in between. In Paper presented at the SAS global forum, pp. 325–2012. Swearingen, C. J., Castro, M. S. M., & Bursac, Z. (2012). Inflated beta regression: Zero, one, and everything in between. In Paper presented at the SAS global forum, pp. 325–2012.
45.
go back to reference Lin, L. I. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics, 45(1), 255–268.CrossRefPubMed Lin, L. I. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics, 45(1), 255–268.CrossRefPubMed
48.
Metagegevens
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