Missing values pose a complication many applied researchers need to deal with, however, the handling of missing values is usually not the focus of the research. As a consequence, standard imputation methods that are readily available in software, …
R package JointAI for analysis of incomplete data in the Bayesian framework.
Missing data occur in many types of studies and typically complicate the analysis. Multiple imputation, either using joint modelling or the more flexible fully conditional specification approach, are popular and work well in standard settings. In …
**Context:** This work is motivated by a study in Type II diabetes patients and their progression to diabetic retinopathy. Specifically, physicians are interested in identifying risk factors, longitudinal and baseline, for progression. An important …
Our work is motivated by examples from two large cohort studies, the Generation R Study and the Rotterdam Study, in which the analysis models of interest involved non-linear effects, interaction terms or had a longitudinal outcome. As is the case for …