title: Skew-t partially linear mixed-effects models for AIDS clinical studies.
abstract: We propose partially linear mixed-effects models with asymmetry and missingness to investigate the relationship between two biomarkers in clinical studies. The proposed models take into account irregular time effects commonly observed in clinical studies under a semiparametric model framework. In addition, commonly assumed symmetric distributions for model errors are substituted by asymmetric distribution to account for skewness. Further, informative missing data mechanism is accounted for. A Bayesian approach is developed to perform parameter estimation simultaneously. The proposed model and method are applied to an AIDS dataset and comparisons with alternative models are performed.
authors.affiliation: a Department of Epidemiology and Biostatistics , State University of New York , Albany , New York , USA.
authors.forename: Tao
authors.initials: T
authors.lastname: Lu
mesh_terms: D000163:Acquired Immunodeficiency Syndrome / Q000628:therapy*; D001499:Bayes Theorem; D015415:Biomarkers / Q000032:analysis*; D002986:Clinical Trials as Topic; D015658:HIV Infections / Q000628:therapy*; D006801:Humans; D016014:Linear Models
chemical_list: D015415:Biomarkers
keywords: Bayesian inference; longitudinal data; missing data; partially linear mixed-effects models; skew distribution