Mixed Effects Models for Complex Data

Reviews incomplete data problems in mixed-effects models for longitudinal studies and discusses various approaches, including some commonly used simple methods, EM algorithms, and multiple imputation methods. This work also covers other models for longitudinal data, such as marginal models with missing values.