Zero-inflated Poisson and negative binomial models for longitudinal measures with heterogeneous random effects
Year: 2015
Type: article
Abstract: Longitudinal zero-inflated count data arise frequently in substance use research when assessing the effects of behavioral and pharmacological interventions.Zero-inflated count models (e.g.zero-inflated Poisson or zero-inflated negative binomial) with random effects have been developed to analyze this type of data.In random effects zero-inflated count models, the random effects covariance matrix is typically assumed to be homogeneous (constant across subjects).However, in many situations this matrix may be heterogeneous (differ by measured covariates).In this paper, we extend zero-inflated count models to account for random effects heterogeneity by modeling their variance as a function of covariates.We show via simulation that ignoring intervention and covariate-specific heterogeneity can produce biased estimates of covariate and random effect estimates.Moreover, those biased estimates can be rectified by correctly modeling the random effects covariance structure.The methodological development is motivated by and applied to the Combined Pharmacotherapies and Behavioral Interventions for Alcohol Dependence (COMBINE) study, the largest clinical trial of alcohol dependence performed in United States with 1383 individuals.
Source: DukeSpace (Duke University)
Authors: Haiyuan Zhu, Sheng Luo, Stacia M. DeSantis
Language: English
Subfield: Statistics and Probability
Field: Mathematics
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2761996387