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Title: Variable selection for the random effects two-part model Authors:  Lei Liu - Washington University in St. Louis (United States) [presenting]
Xiaogang Su - University of Texas at El Paso (United States)
Dongxiao Han - Chinese Academy of Sciences (China)
Liuquan Sun - Chinese Academy of Sciences (China)
Abstract: Two-part random effects models have been applied to longitudinal studies for zero-inflated (or semi-continuous) data, characterized by a large portion of zero values and continuous non-zero (positive) values. Examples include monthly medical costs, daily alcohol drinks, etc. As the advance of information technology for data collection and storage, the number of variables available to researchers can be rather large in such studies. To avoid curse of dimensionality and facilitate decision making, it is critically important to select covariates that are truly related to the outcome. We will consider variable selection approaches and apply the ``minimum information criterion'' method to select variables in the random effects two-part model. The estimation is conducted by adaptive Gaussian quadrature which can be conveniently implemented in SAS Proc NLMIXED. The behavior of our approach is evaluated through simulation, and an application to a longitudinal alcohol dependence study is provided.