Skip to main content
main-content
Top

Tip

Swipe om te navigeren naar een ander artikel

Gepubliceerd in: Quality of Life Research 7/2018

22-03-2018

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

Auteurs: Admassu N. Lamu, Gang Chen, Thor Gamst-Klaussen, Jan Abel Olsen

Gepubliceerd in: Quality of Life Research | Uitgave 7/2018

Log in om toegang te krijgen
share
DELEN

Deel dit onderdeel of sectie (kopieer de link)

  • Optie A:
    Klik op de rechtermuisknop op de link en selecteer de optie “linkadres kopiëren”
  • Optie B:
    Deel de link per e-mail

Abstract

Purpose

To develop mapping algorithms that transform Diabetes-39 (D-39) scores onto EQ-5D-5L utility values for each of eight recently published country-specific EQ-5D-5L value sets, and to compare mapping functions across the EQ-5D-5L value sets.

Methods

Data include 924 individuals with self-reported diabetes from six countries. The D-39 dimensions, age and gender were used as potential predictors for EQ-5D-5L utilities, which were scored using value sets from eight countries (England, Netherland, Spain, Canada, Uruguay, China, Japan and Korea). Ordinary least squares, generalised linear model, beta binomial regression, fractional regression, MM estimation and censored least absolute deviation were used to estimate the mapping algorithms. The optimal algorithm for each country-specific value set was primarily selected based on normalised root mean square error (NRMSE), normalised mean absolute error (NMAE) and adjusted-r2. Cross-validation with fivefold approach was conducted to test the generalizability of each model.

Results

The fractional regression model with loglog as a link function consistently performed best in all country-specific value sets. For instance, the NRMSE (0.1282) and NMAE (0.0914) were the lowest, while adjusted-r2 was the highest (52.5%) when the English value set was considered. Among D-39 dimensions, the energy and mobility was the only one that was consistently significant for all models.

Conclusions

The D-39 can be mapped onto the EQ-5D-5L utilities with good predictive accuracy. The fractional regression model, which is appropriate for handling bounded outcomes, outperformed other candidate methods in all country-specific value sets. However, the regression coefficients differed reflecting preference heterogeneity across countries.
Bijlagen
Alleen toegankelijk voor geautoriseerde gebruikers
Voetnoten
1
For example, the transformed score for energy and mobility (EM) domain is calculated by subtracting the sum of reverse-coded responses across all 15 items (say, X) from its minimum score (15) and divided by the range (maximum (105) minus minimum scores). That is, the algorithm for computing the EM score is: (X-15)/(105-15).
 
2
MM estimation estimates the regression parameter using S estimation, which minimises the scale of the residual from M estimation and then proceed with M estimation. The S in ‘S estimation’ stands for ‘scale’ of the residual, M in ‘M estimation’ for ‘maximum likelihood type’ and MM in ‘MM estimation’ stands for ‘minimising M estimation’. For detail, see Yohai [37].
 
3
RMSE and MAE were adjusted for scale differences, and normalised RMSE and normalised MAE were reported in the present study.
 
Literatuur
4.
go back to reference Brazier, J., Ratcliffe, J., Saloman, J., & Tsuchiya, A. (2017). Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press. Brazier, J., Ratcliffe, J., Saloman, J., & Tsuchiya, A. (2017). Measuring and valuing health benefits for economic evaluation. Oxford: Oxford University Press.
5.
go back to reference WHO. (2016). Global report on diabetes. France: World Health Organization. WHO. (2016). Global report on diabetes. France: World Health Organization.
6.
go back to reference IDF. (2015). Diabetes Atlas (7th edn.). Brussels: International Diabetes Federation (IDF). IDF. (2015). Diabetes Atlas (7th edn.). Brussels: International Diabetes Federation (IDF).
7.
go back to reference Drummond, M. F., Sculpher, M. J., Torrance, G. W., O’Brien, B. J., & Stoddart, G. L. (2015). Methods for the economic evaluation of health care programme (4th edn.). Oxford: Oxford University Press: Oxford. Drummond, M. F., Sculpher, M. J., Torrance, G. W., O’Brien, B. J., & Stoddart, G. L. (2015). Methods for the economic evaluation of health care programme (4th edn.). Oxford: Oxford University Press: Oxford.
8.
go back to reference Boyer, J. G., & Earp, J. A. (1997). The development of an instrument for assessing the quality of life of people with diabetes: Diabetes-39. Medical Care, 35(5), 440–453. CrossRefPubMed Boyer, J. G., & Earp, J. A. (1997). The development of an instrument for assessing the quality of life of people with diabetes: Diabetes-39. Medical Care, 35(5), 440–453. CrossRefPubMed
9.
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
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.
33.
go back to reference Jobson, J. (2012). Applied multivariate data analysis: Volume II: Categorical and multivariate methods. New York: Springer. Jobson, J. (2012). Applied multivariate data analysis: Volume II: Categorical and multivariate methods. New York: Springer.
34.
go back to reference Yaremko, R. M., Harari, H., Harrison, R. C., & Lynn, E. (2013). Handbook of research and quantitative methods in psychology: For students and professionals. Abingdon: Taylor & Francis. CrossRef Yaremko, R. M., Harari, H., Harrison, R. C., & Lynn, E. (2013). Handbook of research and quantitative methods in psychology: For students and professionals. Abingdon: Taylor & Francis. CrossRef
37.
go back to reference Yohai, V. J. (1987). High breakdown-point and high efficiency robust estimates for regression. The Annals of Statistics, 15(2), 642–656. CrossRef Yohai, V. J. (1987). High breakdown-point and high efficiency robust estimates for regression. The Annals of Statistics, 15(2), 642–656. CrossRef
41.
go back to reference Hao, L., & Naiman, D. Q. (2007). Quantile regression. Thousand Oaks: SAGE Publications. CrossRef Hao, L., & Naiman, D. Q. (2007). Quantile regression. Thousand Oaks: SAGE Publications. CrossRef
42.
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
52.
go back to reference Barnhart, H. X., Haber, M., & Song, J. (2002). Overall concordance correlation coefficient for evaluating agreement among multiple observers. Biometrics, 58(4), 1020–1027. CrossRefPubMed Barnhart, H. X., Haber, M., & Song, J. (2002). Overall concordance correlation coefficient for evaluating agreement among multiple observers. Biometrics, 58(4), 1020–1027. CrossRefPubMed
53.
go back to reference Andrews, G., & Slade, T. (2001). Interpreting scores on the Kessler Psychological Distress Scale (K10). Australian and New Zealand Journal of Public Health, 25(6), 494–497. CrossRefPubMed Andrews, G., & Slade, T. (2001). Interpreting scores on the Kessler Psychological Distress Scale (K10). Australian and New Zealand Journal of Public Health, 25(6), 494–497. CrossRefPubMed
Metagegevens
Titel
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
Auteurs
Admassu N. Lamu
Gang Chen
Thor Gamst-Klaussen
Jan Abel Olsen
Publicatiedatum
22-03-2018
Uitgeverij
Springer International Publishing
Gepubliceerd in
Quality of Life Research / Uitgave 7/2018
Print ISSN: 0962-9343
Elektronisch ISSN: 1573-2649
DOI
https://doi.org/10.1007/s11136-018-1840-5