R package JointAI

R package for Joint Analysis and Imputation of incomplete data in R using the Bayesian framework

Imputation of incomplete covariates in longitudinal data: Can Bayesian non-parametric methods prevent model-misspecification?

**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 …

Analysis and Imputation Using the R Package JointAI

Imputation model misspecification: how robust are Bayesian methods?

Missing values complicate analyses in many studies. Nevertheless, the availability nowadays of methods, such as Multiple Imputation (MI) in standard software, has enabled researchers to perform statistical analysis accounting for missing data. More …

Imputation of missing covariates: when standard methods may fail

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 …