Impact of communities, health, and emotional-related factors on smoking use: comparison of joint modeling of mean and dispersion and Bayes’ hierarchical models on add health survey

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• 作者：Jie Pu ; Di Fang ; Jeffrey R. Wilson
• 关键词：Overdispersion ; Generalized additive models ; Generalized linear models ; Bayes’ hierarchical model ; Logistic regression
• 刊名：BMC Medical Research Methodology
• 出版年：2017
• 出版时间：December 2017
• 年：2017
• 卷：17
• 期：1
• 全文大小：407KB
• 刊物主题：Theory of Medicine/Bioethics; Statistical Theory and Methods; Statistics for Life Sciences, Medicine, Health Sciences;
• 出版者：BioMed Central
• ISSN：1471-2288
• 卷排序：17

文摘

BackgroundThe analysis of correlated binary data is commonly addressed through the use of conditional models with random effects included in the systematic component as opposed to generalized estimating equations (GEE) models that addressed the random component. Since the joint distribution of the observations is usually unknown, the conditional distribution is a natural approach. Our objective was to compare the fit of different binary models for correlated data in Tabaco use. We advocate that the joint modeling of the mean and dispersion may be at times just as adequate. We assessed the ability of these models to account for the intraclass correlation. In so doing, we concentrated on fitting logistic regression models to address smoking behaviors.