A hierarchical zero-inflated log-normal model for skewed responses

作     者：Li, Ning Elashoff, David A. Robbins, Wendie A. Xun, Lin 

作者机构：Univ Florida Dept Epidemiol & Biostat Coll Publ Hlth & Hlth Profess Gainesville FL 32610 USA Univ Calif Los Angeles Sch Publ Hlth Dept Biostat Los Angeles CA 90024 USA Univ Calif Los Angeles Sch Nursing Los Angeles CA 90024 USA 

出 版 物：《STATISTICAL METHODS IN MEDICAL RESEARCH》 (医学研究统计方法)

年 卷 期：2011年第20卷第3期

页      面：175-189页

核心收录：

学科分类：0710[理学-生物学] 1204[管理学-公共管理] 02[经济学] 0202[经济学-应用经济学] 020208[经济学-统计学] 1001[医学-基础医学(可授医学、理学学位)] 07[理学] 0714[理学-统计学（可授理学、经济学学位）] 070103[理学-概率论与数理统计] 0701[理学-数学] 

基　　金：University of California UCLA Center for Occupational and Environmental Health 

主　　题：expectation-maximisation algorithm Spermatozoa maximum likelihood estimation Comet Assay Outcome Measures Hierarchical maximum likelihood 

摘      要：Although considerable attention has been given to zero-inflated count data, research on zero-inflated log-normal data is limited. In this article, we consider a study to examine human sperm cell DNA damage obtained from single-cell electrophoresis (COMET assay) experiment in which the outcome measures present a typical example of log-normal data with excess zeros. The problem is further complicated by the fact that each study subject has multiple outcomes at each of up to three visits separated by six-week intervals. Previous methods for zero-inflated log-normal data are based on either simple experimental designs, where comparison of means of zero-inflated log-normal data across different experiment groups is of primary interest, or longitudinal measurements, where only one observation is available for each subject at each visit. Their methods cannot be applied when multiple observations per visit are possible and both inter- and intra-subject variations are present. Our zero-inflated model extends the previous methods by incorporating a hierarchical structure using latent random variables to take into account both inter- and intra-subject variations in zero-inflated log-normal data. An EM algorithm has been developed to obtain the Maximum likelihood estimates of the parameters and their standard errors can be estimated by parametric bootstrap. The model is illustrated using the COMET assay data.