Oya Kalaycioglu

Models for data that are missing not at random in health studies

Missing data due to attrition occurs in almost all longitudinal trials and observational studies. None of the standard statistical models such as multilevel or marginal models are valid when missing data are missing not at random (MNAR). To account for MNAR, model based approaches have been proposed which can broadly be divided into two classes, pattern mixture and selection models. These models make untestable assumptions and cannot be fitted easily using standard statistical software. One of the main objectives is to evaluate fully the performance of the existing models and to identify the best method and extend it if necessary. Based on these work recommendations on analysing MNAR data will be made to applied statisticians.