Comparison of beta-binomial regression model approaches to analyze health-related quality of life data

• Published in: Statistical methods in medical research 2018-10, Vol.27 (10), p.2989-3009
• Main Authors: Najera-Zuloaga, Josu, Lee, Dae-Jin, Arostegui, Inmaculada
• Format: Article
• Language: English
• Subjects: Adequacy; Binomial distribution; Chronic obstructive pulmonary disease; Clinical research; Clinical trials; Computer simulation; Epidemiology; Health; Health status; Linear analysis; Medical research; Patients; Polls & surveys; Quality of life; Random variables; Regression analysis; Regression models; Simulation; Statistical models
• ISSN: 0962-2802; 1477-0334
• DOI: 10.1177/0962280217690413

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.