R

Missing Data - Challenges and Solutions

How black-box use of imputation can cause bias

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, …

Joint Analysis and Imputation of Incomplete Data in R

R package JointAI for analysis of incomplete data in the Bayesian framework.

JointAI: Joint Analysis and Imputation of Incomplete Data in R

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 …

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 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 …