Zero-inflated count models for longitudinal measurements with heterogeneous random effects.
Zero-inflated count models for longitudinal measurements with heterogeneous random effects.
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DOI：
10.1177/0962280215588224
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发表时间：
2017-08
影响因子：
2.3
通讯作者：
DeSantis SM
中科院分区：
文献类型：
--
作者：
Zhu H;Luo S;DeSantis SM
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 these 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 effects 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 in United States with 1,383 individuals.
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