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Gepubliceerd in:

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].

Brazier, J., Ratcliffe, J., Salomon, J. A., & Tsuchiya, A. (2017). Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press.

Harris, R. A., Washington, A. E., Nease, R. F., & Kuppermann, M. (2004). Cost utility of prenatal diagnosis and the risk-based threshold. The Lancet, 363(9405), 276–282. https://doi.org/10.1016/S0140-6736(03)15385-8. 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

Lowy, D. R., & Collins, F. S. (2016). Aiming high—changing the trajectory for cancer. New England Journal of Medicine, 374(20), 1901–1904. https://doi.org/10.1056/NEJMp1600894. CrossRefPubMed

Hanahan, D. (2014). Rethinking the war on cancer. The Lancet, 383(9916), 558–563. https://doi.org/10.1016/s0140-6736(13)62226-6. CrossRef

Garau, M., Shah, K. K., Mason, A. R., Wang, Q., Towse, A., & Drummond, M. F. (2011). Using QALYs in cancer. PharmacoEconomics, 29(8), 673–685. https://doi.org/10.2165/11588250-000000000-00000. 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

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

Wisløff, T., Hagen, G., Hamidi, V., Movik, E., Klemp, M., & Olsen, J. A. (2014). Estimating QALY gains in applied studies: A review of cost-utility analyses published in 2010. Pharmacoeconomics, 32(4), 367–375. https://doi.org/10.1007/s40273-014-0136-z. CrossRefPubMedPubMedCentral

10.

Herdman, M., Gudex, C., Lloyd, A., Janssen, M., Kind, P., Parkin, D., Bonsel, G., & Badia, X. (2011). Development and preliminary testing of the new five-level version of EQ-5D (EQ-5D-5L). Quality of Life Research, 20(10), 1727–1736. https://doi.org/10.1007/s11136-011-9903-x. CrossRefPubMedPubMedCentral

11.

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.

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.

13.

Brazier, J. E., Yang, Y., Tsuchiya, A., & Rowen, D. L. (2010). A review of studies mapping (or cross walking) non-preference based measures of health to generic preference-based measures. The European Journal of Health Economics. https://doi.org/10.1007/s10198-009-0168-z. CrossRefPubMed

14.

Dakin, H., Abel, L., Burns, R., & Yang, Y. (2018). Review and critical appraisal of studies mapping from quality of life or clinical measures to EQ-5D: An online database and application of the MAPS statement. Health and Quality of Life Outcomes, 16(1), 31. https://doi.org/10.1186/s12955-018-0857-3. CrossRefPubMedPubMedCentral

15.

Kim, S. H., Jo, M.-W., Kim, H.-J., & Ahn, J.-H. (2012). Mapping EORTC QLQ-C30 onto EQ-5D for the assessment of cancer patients. Health and Quality of Life Outcomes, 10(1), 151. https://doi.org/10.1186/1477-7525-10-151. CrossRefPubMedPubMedCentral

16.

Kontodimopoulos, N., Aletras, V. H., Paliouras, D., & Niakas, D. (2009). Mapping the cancer-specific EORTC QLQ-C30 to the preference-based EQ-5D, SF-6D, and 15D instruments. Value in Health. https://doi.org/10.1111/j.1524-4733.2009.00569.x. CrossRefPubMed

17.

Marriott, E. R., van Hazel, G., Gibbs, P., & Hatswell, A. J. (2017). Mapping EORTC-QLQ-C30 to EQ-5D-3L in patients with colorectal cancer. Journal of Medical Economics, 20(2), 193–199. https://doi.org/10.1080/13696998.2016.1241788. CrossRefPubMed

18.

McKenzie, L., & van der Pol, M. (2009). Mapping the EORTC QLQ C-30 onto the EQ-5D instrument: The potential to estimate QALYs without generic preference data. Value in Health, 12(1), 167–171. https://doi.org/10.1111/j.1524-4733.2008.00405.x. CrossRefPubMed

19.

Versteegh, M. M., Leunis, A., Luime, J. J., Boggild, M., Uyl-de Groot, C. A., & Stolk, E. A. (2012). Mapping QLQ-C30, HAQ, and MSIS-29 on EQ-5D. Medical Decision Making. https://doi.org/10.1177/0272989x11427761. CrossRefPubMed

20.

Khan, I., Morris, S., Pashayan, N., Matata, B., Bashir, Z., & Maguirre, J. (2016). Comparing the mapping between EQ-5D-5L, EQ-5D-3L and the EORTC-QLQ-C30 in non-small cell lung cancer patients. Health and Quality of Life Outcomes, 14, 60. https://doi.org/10.1186/s12955-016-0455-1. CrossRefPubMedPubMedCentral

21.

Wong, C. K. H., Lam, C. L. K., Wan, Y. F., & Rowen, D. (2013). Predicting SF-6D from the European Organization for Treatment and Research of Cancer Quality of Life Questionnaire Scores in Patients with Colorectal Cancer. Value in Health, 16(2), 373–384. https://doi.org/10.1016/j.jval.2012.12.004. CrossRefPubMed

22.

Devlin, N. J., Shah, K. K., Feng, Y., Mulhern, B., & van Hout, B. (2017). Valuing health-related quality of life: An EQ-5D-5L value set for England. Health Economics. https://doi.org/10.1002/hec.3564. CrossRefPubMedPubMedCentral

23.

Basu, A., & Manca, A. (2012). Regression estimators for generic health-related quality of life and quality-adjusted life years. Medical Decision Making, 32(1), 56–69. https://doi.org/10.1177/0272989x11416988. CrossRefPubMed

24.

Petrou, S., Rivero-Arias, O., Dakin, H., Longworth, L., Oppe, M., Froud, R., & Gray, A. (2015). The MAPS reporting statement for studies mapping onto generic preference-based outcome measures: Explanation and elaboration. Pharmacoeconomics, 33(10), 993–1011. https://doi.org/10.1007/s40273-015-0312-9. CrossRefPubMed

25.

Richardson, J., Iezzi, A., & Maxwell, A. (2012). Cross-national comparison of twelve quality of life instruments: MIC paper 1 background, questions, instruments. Research paper 76. Retrieved November 23, 2017, from https://www.aqol.com.au/papers/researchpaper76.pdf.

26.

Kaambwa, B., Chen, G., Ratcliffe, J., Iezzi, A., Maxwell, A., & Richardson, J. (2017). Mapping between the Sydney asthma quality of life questionnaire (AQLQ-S) and five multi-attribute utility instruments (MAUIs). Pharmacoeconomics, 35(1), 111–124. https://doi.org/10.1007/s40273-016-0446-4. CrossRefPubMed

27.

Mihalopoulos, C., Chen, G., Iezzi, A., Khan, M. A., & Richardson, J. (2014). Assessing outcomes for cost-utility analysis in depression: Comparison of five multi-attribute utility instruments with two depression-specific outcome measures. The British Journal of Psychiatry, 205(5), 390–397. https://doi.org/10.1192/bjp.bp.113.136036. CrossRefPubMed

28.

Gamst-Klaussen, T., Lamu, A. N., Chen, G., & Olsen, J. A. (2018). Assessment of outcome measures for cost-utility analysis in depression: Mapping depression scales onto the EQ-5D-5L. BJPsych Open, 4(4), 160–166. https://doi.org/10.1192/bjo.2018.21. CrossRefPubMedPubMedCentral

29.

Chen, G., McKie, J., Khan, M. A., & Richardson, J. R. (2014). Deriving health utilities from the MacNew Heart Disease Quality of Life Questionnaire. European Journal of Cardiovascular Nursing, 14(5), 405–415. https://doi.org/10.1177/1474515114536096. CrossRefPubMed

30.

Lamu, A., Chen, G., Gamst-Klaussen, T., & Olsen, J. (2018). Do country-specific preference weights matter in the choice of mapping algorithms? The case of mapping the Diabetes-39 onto eight country-specific EQ-5D-5L value sets. Quality of Life Research. https://doi.org/10.1007/s11136-018-1840-5. CrossRefPubMed

31.

Brazier, J., Roberts, J., & Deverill, M. (2002). The estimation of a preference-based measure of health from the SF-36. Journal of Health Economics, 21(2), 271–292. https://doi.org/10.1016/S0167-6296(01)00130-8. CrossRefPubMed

32.

Russell, D. W. (2002). In Search of underlying dimensions: The use (and abuse) of factor analysis in personality and social psychology bulletin. Personality and Social Psychology Bulletin, 28(12), 1629–1646. https://doi.org/10.1177/014616702237645. CrossRef

33.

Fabrigar, L. R., Wegener, D. T., MacCallum, R. C., & Strahan, E. J. (1999). Evaluating the use of exploratory factor analysis in psychological research. Psychological Methods, 4(3), 272–299. https://doi.org/10.1037//1082-989x.4.3.272. CrossRef

34.

Fox, J. (2015). Applied regression analysis and generalized linear models. Thousand Oaks: SAGE.

35.

Basu, A., & Rathouz, P. J. (2005). Estimating marginal and incremental effects on health outcomes using flexible link and variance function models. Biostatistics, 6(1), 93–109. https://doi.org/10.1093/biostatistics/kxh020. CrossRefPubMed

36.

Basu, A. (2005). Extended generalized linear models: Simultaneous estimation of flexible link and variance functions. The Stata Journal, 5(4), 501–516.

37.

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

38.

Ramalho, E. A., Ramalho, J. J. S., & Murteira, J. M. R. (2011). Alternative estimating and testing empirical strategies for fractional regression models. Journal of Economic Surveys, 25(1), 19–68. https://doi.org/10.1111/j.1467-6419.2009.00602.x. CrossRef

39.

Ferrari, S. L. P., & Cribari-Neto, F. (2004). Beta regression or modeling rates and proportions. Journal of Applied Statistics, 31(7), 799–815. https://doi.org/10.1080/0266476042000214501. CrossRef

40.

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.

41.

Ospina, R., & Ferrari, S. L. P. (2012). A general class of zero-or-one inflated beta regression models. Computational Statistics & Data Analysis, 56(6), 1609–1623. https://doi.org/10.1016/j.csda.2011.10.005. CrossRef

42.

Khan, I., & Morris, S. (2014). A non-linear beta-binomial regression model for mapping EORTC QLQ-C30 to the EQ-5D-3L in lung cancer patients: A comparison with existing approaches. Health and Quality of Life Outcomes, 12, 163. https://doi.org/10.1186/s12955-014-0163-7. CrossRefPubMedPubMedCentral

43.

Bottai, M., Cai, B., & McKeown, R. E. (2010). Logistic quantile regression for bounded outcomes. Statistics in Medicine, 29(2), 309–317. https://doi.org/10.1002/sim.3781. CrossRefPubMed

44.

Powell, J. L. (1984). Least absolute deviations estimation for the censored regression model. Journal of Econometrics, 25(3), 303–325. https://doi.org/10.1016/0304-4076(84)90004-6. CrossRef

45.

Lin, L. I. (1989). A concordance correlation coefficient to evaluate reproducibility. Biometrics, 45(1), 255–268. CrossRefPubMed

46.

Wu, E. Q., Mulani, P., Farrell, M. H., & Sleep, D. (2007). Mapping FACT-P and EORTC QLQ-C30 to patient health status measured by EQ-5D in metastatic hormone-refractory prostate cancer patients. Value in Health, 10(5), 408–414. https://doi.org/10.1111/j.1524-4733.2007.00195.x. CrossRefPubMed

47.

Kim, E. J., Ko, S. K., & Kang, H. Y. (2012). Mapping the cancer-specific EORTC QLQ-C30 and EORTC QLQ-BR23 to the generic EQ-5D in metastatic breast cancer patients. Quality of Life Research. https://doi.org/10.1007/s11136-011-0037-y. CrossRefPubMedPubMedCentral

48.

Sullivan, P. W., & Ghushchyan, V. (2006). Mapping the EQ-5D index from the SF-12: US general population preferences in a nationally representative sample. Medical Decision Making: An International Journal of the Society for Medical Decision Making, 26(4), 401–409. https://doi.org/10.1177/0272989X06290496. CrossRef

49.

Crott, R., & Briggs, A. (2010). Mapping the QLQ-C30 quality of life cancer questionnaire to EQ-5D patient preferences. The European Journal of Health Economics. https://doi.org/10.1007/s10198-010-0233-7. CrossRefPubMed

50.

Ramalho, J. J. S., & da Silva, J. V. (2009). A two-part fractional regression model for the financial leverage decisions of micro, small, medium and large firms. Quantitative Finance, 9(5), 621–636. https://doi.org/10.1080/14697680802448777. CrossRef

51.

Kontodimopoulos, N. (2015). The potential for a generally applicable mapping model between QLQ-C30 and SF-6D in patients with different cancers: A comparison of regression-based methods. Quality of Life Research, 24(6), 1535–1544. https://doi.org/10.1007/s11136-014-0857-7. CrossRefPubMed

52.

Barton, G. R., Sach, T. H., Jenkinson, C., Avery, A. J., Doherty, M., & Muir, K. R. (2008). Do estimates of cost-utility based on the EQ-5D differ from those based on the mapping of utility scores? Health and Quality of Life Outcomes, 6, 51–51. https://doi.org/10.1186/1477-7525-6-51. CrossRefPubMedPubMedCentral

53.

Versteegh, M. M., Rowen, D., Brazier, J. E., & Stolk, E. A. (2010). Mapping onto EQ-5D for patients in poor health. Health and Quality of Life Outcomes. https://doi.org/10.1186/1477-7525-8-141. CrossRefPubMedPubMedCentral

54.

Olsen, J. A., Lamu, A. N., & Cairns, J. (2017). In search of a common currency: A comparison of seven EQ-5D-5L value sets. Health Economics. https://doi.org/10.1002/hec.3606. CrossRefPubMed

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

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