Comparison of beta-binomial regression model approaches to analyze health related quality of life data
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Health related quality of life (HRQoL) has become an increasingly important indicator of health status in clinical trials and epidemiological research. Moreover, the study of the relationship of HRQoL with patients' and disease's characteristics has become one of the primary aims of many HRQoL studies. HRQoL 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 di erent approaches in the literature: i) beta-binomial distribution with a logistic link; and ii) hierarchical generalized linear models (HGLMs). None of the existing literature in the analysis of HRQoL 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 HRQoL outcomes in patients with Chronic Obstructive Pulmonary Disease (COPD), where the use of both approaches yields to contradictory results in terms of covariate e ects signi cance and consequently the interpretation of the most relevant factors in HRQoL. 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 HRQoL survey data for practitioners, providing an R package.