multi-level

Missing Data in Clinical Research (EL009)

Multiple Imputation to Handle Missing Values in Clinical Research

Bayesian Methods for Missing Covariates in Longitudinal Studies

Pre-conference course on Bayesian Methods for Missing Covariates in Longitudinal Studies at the conference of the International Biometric Society in Riga, Latvia, July 2022

Multiple Imputation of Missing Data in Simple and More Complex Settings

Pre-conference course on Multiple Imputation of Missing Data in Simple and More Complex Settings at the "Tagung der Fachgruppe Methoden & Evaluation der Deutschen Gesellschaft für Psychologie" in Kiel, Germany

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 …

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