R

Joint Analysis and Imputation of Incomplete Data in R

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

R package JointAI

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

Shiny application: Non-linear effects

R shiny application to test for the need of non-linear effects using splines in linear, logistic, poisson and Cox regression models

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 …

Bayesian Imputation of Missing Covariates

Doctoral Dissertation

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 …