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Marginalized transition shared random effects models for longitudinal binary data with nonignorable dropout |
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| Authors: | Myungok Lee Keunbaik Lee JungBok Lee |
| Affiliation: | 1. Sekolah Pelita Harapan International Jl. Dago Permai No. 1, Komplek Dago Villas Lippo Cikarang, Bekasi, Indonesia;2. Department of Statistics, Sungkyunkwan University, Seoul, Korea;3. Department of Clinical Epidemiology and Biostatistics, Asan Medical Center and University of Ulsan College of Medicine, Seoul, Korea |
| 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. |
| Keywords: | Categorical data Generalized linear models Missing data Newton– Raphson Serial dependence |