title: Marginalized transition shared random effects models for longitudinal binary data with nonignorable dropout.
abstract: In longitudinal studies investigators frequently have to assess and address potential biases introduced by missing data. New methods are proposed for modeling longitudinal categorical data with nonignorable dropout using marginalized transition models and shared random effects models. Random effects are introduced for both serial dependence of outcomes and nonignorable missingness. Fisher-scoring and Quasi-Newton algorithms are developed for parameter estimation. Methods are illustrated with a real dataset.
authors.affiliation: Sekolah Pelita Harapan International Jl. Dago Permai No. 1, Komplek Dago Villas Lippo Cikarang, Bekasi, 17550, Indonesia.
authors.forename: Myungok
authors.initials: M
authors.lastname: Lee
authors.forename: Keunbaik
authors.initials: K
authors.lastname: Lee
authors.forename: Jungbok
authors.initials: J
authors.lastname: Lee
mesh_terms: D000328:Adult; D000368:Aged; D001699:Biometry / Q000379:methods*; D003627:Data Interpretation, Statistical; D005260:Female; D006801:Humans; D008137:Longitudinal Studies; D008297:Male; D024821:Metabolic Syndrome / Q000453:epidemiology; D008875:Middle Aged; D015233:Models, Statistical; D015999:Multivariate Analysis; D010352:Patient Dropouts / Q000706:statistics & numerical data*
keywords: Categorical data; Generalized linear models; Missing data; Newton-Raphson; Serial dependence