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Marginalized transition shared random effects models for longitudinal binary data with nonignorable dropout
Author(s) -
Lee Myungok,
Lee Keunbaik,
Lee JungBok
Publication year - 2014
Publication title -
biometrical journal
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 1.108
H-Index - 63
eISSN - 1521-4036
pISSN - 0323-3847
DOI - 10.1002/bimj.201200085
Subject(s) - dropout (neural networks) , missing data , categorical variable , random effects model , longitudinal data , statistics , econometrics , computer science , binary data , binary number , mathematics , machine learning , data mining , medicine , meta analysis , arithmetic
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.