![[PDF]](/pb-assets/Icons/adobe-pdf-icon-1498649896243.png){kind=icon}
Abstract
Health-related quality of life has become an increasingly important indicator of health status in clinical trials and epidemiological research. Moreover, the study of the relationship of health-related quality of life with patients and disease characteristics has become one of the primary aims of many health-related quality of life studies. Health-related quality of life scores are usually assumed to be distributed as binomial random variables and often highly skewed. The use of the beta-binomial distribution in the regression context has been proposed to model such data; however, the beta-binomial regression has been performed by means of two different approaches in the literature: (i) beta-binomial distribution with a logistic link; and (ii) hierarchical generalized linear models. None of the existing literature in the analysis of health-related quality of life survey data has performed a comparison of both approaches in terms of adequacy and regression parameter interpretation context. This paper is motivated by the analysis of a real data application of health-related quality of life outcomes in patients with Chronic Obstructive Pulmonary Disease, where the use of both approaches yields to contradictory results in terms of covariate effects significance and consequently the interpretation of the most relevant factors in health-related quality of life. We present an explanation of the results in both methodologies through a simulation study and address the need to apply the proper approach in the analysis of health-related quality of life survey data for practitioners, providing an R package.
References
| 1. | Testa, MA, Simonson, DC. Assessment of quality of life outcomes. New Engl J Med 1996; 334: 835–840. Google Scholar | Medline | ISI |
| 2. | Goldsmith, SB . The status of health status indicators. Health Service Rep 1972; 87: 212–220. Google Scholar | Medline |
| 3. | Ware, JE, Snow, KK, Kosinski, MA SF36 Health survey, manual and interpretation guides, The Health Institute, New England Medical Center, 1993. Google Scholar |
| 4. | Arostegui, I, Nuñez-Antón, V, Quintana, JM. Analysis of the Short Form-36 (SF-36): The beta-binomial distribution approach. Stat Med 2007; 26: 1318–1342. Google Scholar | Medline | ISI |
| 5. | Arostegui, I, Nuñez-Antón, V, Quintana, JM. Statistical approach to analyse patient-reported outcomes as response variables: an application to health-related quality of life. Stat Meth Med Res 2012; 21: 189–214. Google Scholar | SAGE Journals | ISI |
| 6. | Forcina, A, Franconi, L. Regression analysis with the beta-binomial distribution. Rivista di Statistica Applicata 1988; 21: 7–12. Google Scholar |
| 7. | Lee, Y, Nelder, JA. Hierarchical generalized linear models. J Royal Stat Soc Ser B 1996; 58: 619–678. Google Scholar |
| 8. | Lee, Y, Nelder, JA, Noh, M. H-likelihood: problems and solutions. Stat Comput 2007; 17: 49–55. Google Scholar |
| 9. | Lee, W, Lee, Y. Modifications of REML algorithm for HGLMs. Stat Comput 2012; 22: 959–966. Google Scholar |
| 10. | McCulloch, CE, Searle, SR. Generalized, linear and mixed models, Hoboken, NJ: John Wiley & Sons, 2001. Google Scholar |
| 11. | McCullagh, P, Nelder, JA. Generalized linear model, London: Chapman & Hall, 1989. Google Scholar |
| 12. | Lee, Y, Nelder, JA, Pawitan, Y. Generalized linear models with random effects: unified approach via h-Likelihood, Boca Raton, FL: Chapman & Hall, 2006. Google Scholar |
| 13. | Lee, Y, Nelder, JA. Hierarchical generalized linear models: a synthesis of generalized linear models, random-effects and structured dispersions. Biometrika 2001; 88: 987–1006. Google Scholar | ISI |
| 14. | Schall, R . Estimation in generalized linear models with random effects. Biometrika 1991; 78: 719–727. Google Scholar | ISI |
| 15. | Breslow, NE, Clayton, DG. Approximate inference in generalized linear mixed models. J Am Stat Assoc 1993; 88: 9–25. Google Scholar | ISI |
| 16. | Halbert, RJ, Isonaka, S, George, D Interpreting COPD prevalence estimates: what is the true burden of disease? Chest 2003; 123: 1684–1692. Google Scholar | Medline | ISI |
| 17. | Buist, AS, Vollmer, WM, McBurnie, MA. Worldwide burden of COPD in high-and low-income countries. Part I. The burden of obstructive lung disease (BOLD) initiative. Int J Tuberculosis Lung Dis 2008; 12: 703–708. Google Scholar | Medline | ISI |
| 18. | Murray, CJ, López, AD. Alternative projections of mortality and disability by cause 1990–2020: global burden of disease study. Lancet 1997; 349: 1498–504. Google Scholar | Medline | ISI |
| 19. | Esteban, C, Quintana, JM, Moraza, J Impact of hospitalisations for exacerbations of COPD on health-related quality of life. Respir Med 2009; 103: 1201–1208. Google Scholar | Medline | ISI |
| 20. | Mahler, D, Ward, J, Waterman, L Patient-reported dyspnea in COPD reliability and association with stage of disease. Chest 2009; 136: 1473–1479. Google Scholar | Medline |
| 21. | ATS statement: guidelines for the six-minute walk test . Am J Respir Crit Care Med 2002; 166: 11–117. Google Scholar |
| 22. | Zigmond, AS, Snaith, RP. The hospital anxiety and depression scale. Acta Psychiatr Scand 1983; 67: 361–370. Google Scholar | Medline | ISI |
| 23. | Stansfeld, SA, Roberts, R, Foot, SP. Assessing the validity of the SF-36 general health survey. Qual Life Res 1997; 6: 217–224. Google Scholar | Medline |
| 24. | SF-36 Health Survey . Manual and interpretation guide, Boston, MA: The Health Institute, New England Medical Center, 1993. Google Scholar |
| 25. | Ware, JE, Kosinski, MA, Keller, SD. SF-36 Physical and Mental Health Summary Scales: A Users Manual, Boston, MA: The Health Institute, New England Medical Center, 1994. Google Scholar |
| 26. | Garratt, AM, Schmidt, L, Mackintosh, A Quality of life measurement: Bibliographic study of patient assessed health outcome measures. Br Med J 2002; 324: 1417–1421. Google Scholar | Medline |
| 27. | Arostegui, I, Nuñez-Antón, V, Quintana, JM. On the recoding of continuous and bounded indexes to a binomial form: an applications to quality-of-life scores. J Appl Stat 2013; 40: 563–582. Google Scholar |
| 28. | Rönnergard, L, Shen, X, Alam, M. hglm: A package for fitting hierarchical generalized linear models. R J 2010; 2: 20–28. Google Scholar |
