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http://dx.doi.org/10.7465/jkdi.2017.28.3.547

Analysis of health-related quality of life using Beta regression  

Jang, Eun Jin (Department of Information Statistics, Andong National University)
Publication Information
Journal of the Korean Data and Information Science Society / v.28, no.3, 2017 , pp. 547-557 More about this Journal
Abstract
The health-related quality of life data are commonly skewed and bounded with spike at the perfect health status, and the variance tended to be heteroscedastic. In this study, we have developed a prediction model for EQ-5D using linear regression model, beta regression model, and extended beta regression model with mean and precision submodel, and also compared the predictive accuracy. The extended beta regression model allows to model skewness and differences in dispersion related to covariates. Although the extended beta regression model has higher prediction accuracy than the linear regression model, the overlapped confidence intervals suggested that the extended beta regression model was superior to the linear regression model. However, the expended beta regression model could explain the heteroscedasticity and predict within the bounded range. Therefore, the expended beta regression model are appropriate for fitting the health-related quality of life data such as EQ-5D.
Keywords
Beta regression; health-related quality of life; heteroskedasticity; Korea health panel survey;
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1 Powell, J. L. (1984). Least absolute deviations estimation for the censored regression model. Journal of Econometrics, 25, 303-325.   DOI
2 Smithson, M. and Verkuilen, J. (2006). Better lemon squeezer? Maximum-likelihood regression with betadistributed dependent variables. Psychological Methods, 11, 54-71.   DOI
3 Song, T., Ding, Y., Sun, Y., He, Y. N., Qi, D., Wu, Y., Wu, B., Lang, L., Yu, K., Zhao, X., Zhu, L., Wang, S. and Yu, X. S. (2015). A population-based study on health-related quality of life among urban community residents in Shenyang, Northeast of China. BMC Public Health, 15, 921-932.   DOI
4 Tobin, J. (1958). Estimation of relationships for limited dependent variables. Econometrica, 26, 24-36.   DOI
5 Tutoglu, A., Boyaci, A. and Koca, I. (2014). Quality of life, depression, and sexual dysfunction in spouses of female patients with fibromyalgia. Rheumatology International, 34, 1079-1084.   DOI
6 Austin P. C. (2002). A comparison of methods for analyzing health-related quality-of-life measures. Value Health, 5, 329-337.   DOI
7 Bang, S. Y. (2016). Quality of life and its related factors in patients with Korean chronic obstructive pulmonary disease. Journal of the Korean Data & Information Science Society, 27, 1349-1360.   DOI
8 Conover, W. J., Johnson, M. E. and Johnson, M. M. (1981). A comparative study of tests for homogeneity of variances, with applications to the outer continental shelf bidding data. Technometrics, 23, 351-361.   DOI
9 Brazier, J. E., Roberts, J. and Deverill, M. (2002). The estimation of a preference-based measure of health from the SF-36. Journal of Health Economics, 21, 271-292.   DOI
10 Brazier, J. E. and Roberts, J. (2004). The estimation of a preference-based measure of health from the SF-12. Medical Care, 42, 851-859.   DOI
11 Conrado, D. J., Denney, W. S. and Chen, D. (2014). An updated Alzheimer's disease progression model: Incorporating non-linearity, beta regression, and a third-level random effect in NONMEM. Journal of Pharmacokinetics and Pharmacodynamics, 41, 581-598.   DOI
12 EuroQol Group (1990). EuroQol-a new facility for the measurement of health-related quality of life. Health Policy, 16, 199-208.   DOI
13 Cribari-Neto, F. and Zeileis, A. (2010). Beta regression in R. Journal of Statistical Software, 34, 1-24.
14 Dolan, P. (1997). Modeling valuations for EuroQol health states. Medical Care, 35, 1095-108.   DOI
15 Drummond, M. F., Sculpher, M. J., Torrance, G. W., O'Brien, B. J. and Stoddart, G. L. (2005). Methods for the economic evaluation of health care programmes, 3d ed., Oxford University Press, New York.
16 Ferrari, S. L. P. and Cribari-Neto, F. (2004). Beta regression for modeling rates and proportions. Journal of Applied Statistics, 31, 799-815.   DOI
17 Gheorghe, M., Brouwer, W. and van Baal, P. (2015). Did the health of the Dutch population improve between 2001 and 2008? investigating age- and gender-specific trends in quality of life. The European Journal of Health Economics, 16, 801-811.   DOI
18 Hunger, M., Baumert, J. and Holle, R. (2011). Analysis of SF-6D index data: Is beta regression appropriate? Value In Health, 4, 759-767.
19 Han J. Y. and Park H. S. (2017). Factors influencing quality of health care: Based on the Korea health panel data. Journal of the Korean Data & Information Science Society, 28, 195-206.   DOI
20 Huang, I. C., Frangakis, C., Atkinson, M. J., Willke, R. J., Leite, W. L., Vogel, W. B. and Wu, A. W. (2008). Addressing ceiling effects in health status measures: A comparison of techniques applied to measures for people with HIV disease. Health Services Research, 43, 327-339.
21 Lee, K. E. and Han, S. H. (2015). Factors affecting the health-related quality of life among male elders. International Journal of Bio-Science and Bio-Technology, 7, 65-74.
22 Jeong, S. R., Doo, Y. T., and Lee, W. K. (2016). Effect on ambulatory dental visitation frequency according to pack-years of smoking. Journal of the Korean Data & Information Science Society, 27, 419-427.   DOI
23 Jo, M. W., Yun, S. C. and Lee, S. I. (2008). Estimating quality weights for EQ-5D health states with the time trade-off method in South Korea. Value In Health, 11, 1186-1189.   DOI
24 Kent, S., Gray, A. and Schlackow, I. (2015). Mapping from the Parkinson's disease questionnaire PDQ-39 to the generic EuroQol EQ-5D-3L: The value of mixture models. Medical Decision Making, 35, 902-911.   DOI
25 Lee, Y. K., Nam, H. S., Chuang, L. H., Kim, K. Y., Yang, H. K., Kwon, I. S., Kind, P., Kweon, S. S. and Kim, Y. T. (2009). South Korean time trade-off values for EQ-5D health states: Modeling with observed values for 101 health states. Value In Health, 12, 1187-1193.   DOI
26 Longworth, L. and Rowen D. (2013). Mapping to obtain EQ-5D utility-values for use in NICE health technology assessments. Value In Health, 16, 202-210.   DOI
27 Paolino, P. (2001). Maximum likelihood estimation of models with beta-distributed dependent variables. Political Analysis, 9, 325-346.   DOI
28 Lui, F. and Eugenio, E. C. (2016). A review and comparison of Bayesian and likelihood-based inferences in beta regression and zero-or-one-inflated beta regression. Statistical Methods in Medical Research, Epub ahead of print.