Comparison of beta-binomial regression model approaches to analyze health-related quality of life data. (October 2018)

• Record Type: Journal Article
• Authors: Najera-Zuloaga, Josu; Lee, Dae-Jin; Arostegui, Inmaculada
• Is Part Of: Statistical methods in medical research. Volume 27:Number 10(2018)
• Page Start: 2989
• Page End: 3009
• Publication Date: 2018-10
• Subjects: Beta-binomial regression -- hierarchical GLM -- health-related quality of life -- chronic obstructive pulmonary disease -- HRQoL R package; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280217690413
• Languages: English
• ISSNs: 0962-2802

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.