R

Missing Data in Clinical Research (EL009)

Multiple Imputation to Handle Missing Values in Clinical Research

Biostatistics I & II (CC020 & CC030)

Introduction to the basic concepts and techniques of statistical data analysis.

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

Using R for Statistics in Medical Research (BST02)

An Introduction to R for Clinical Research

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

Dealing with Missing Values in Multivariate Joint Models for Longitudinal and Survival Data

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Dealing with Incomplete Covariates in Survival Models: A Bayesian Approach

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Bayesian Methods for Handling Missing Values in Complex Settings

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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 Models for Incomplete Longitudinal and Survival Data

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