Zero-augmented beta-prime model for multilevel semi-continuous data: a Bayesian inference

BMC Medical Research Methodology

卷:22

Research

Naser Kamyari¹  Maryam Seyedtabib²  Abbas Moghimbeigi³  Ali Reza Soltanian⁴  Hossein Mahjub⁵ 

[1] Department of Biostatistics and Epidemiology, School of Health, Abadan University of Medical Sciences, Abadan, Iran;

[2] Department of Biostatistics and Epidemiology, School of Health, Ahvaz Jundishapur University of Medical Sciences, Ahvaz, Iran;

[3] Department of Biostatistics and Epidemiology, School of Health, Research Center for Health, Safety and Environment, Alborz University of Medical Sciences, Karaj, Iran;

[4] Modeling of Noncommunicable Diseases Research Center, Hamadan University of Medical Sciences, Street of Mahdieh, Hamadan, Iran;

[5] Research Center for Health Sciences, School of Public Health, Hamadan University of Medical Sciences, Hamadan, Iran;

关键词: Bayesian framework;    Non-negative data;    Two-part mixed-effects model;    Skew distributions;    Pharmaceutical expenditure;   

DOI  :  10.1186/s12874-022-01736-0

 received in 2022-06-01, accepted in 2022-09-27,  发布年份 2022

来源: Springer

【 摘 要 】

Semi-continuous data characterized by an excessive proportion of zeros and right-skewed continuous positive values appear frequently in medical research. One example would be the pharmaceutical expenditure (PE) data for which a substantial proportion of subjects investigated may report zero. Two-part mixed-effects models have been developed to analyse clustered measures of semi-continuous data from multilevel studies. In this study, we propose a new flexible two-part mixed-effects model with skew distributions for nested semi-continuous cost data under the framework of a Bayesian approach. The proposed model specification consists of two mixed-effects models linked by the correlated random effects: Part I) a model on the occurrence of positive values using a generalized logistic mixed model; and Part II) a model on the magnitude of positive values using a linear mixed model where the model errors follow skew distributions including beta-prime (BP). The proposed method is illustrated with pharmaceutical expenditure data from a multilevel observational study and the analytic results are reported by comparing potential models under different skew distributions. Simulation studies are conducted to assess the performance of the proposed model. The DIC3, LPML, WAIC, and LOO as the Bayesian model selection criteria and measures of divergence used to compare the models.

【 授权许可】

CC BY   
© The Author(s) 2022

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