R

EP16: Missing Data in Clinical Research

Multiple Imputation to Handle Missing Values in Clinical Research

BST02: Using R for Statistics in Medical Research

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

Joint Analysis and Imputation of Incomplete Data in R

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Dealing with missing values in multivariate joint models for longitudinal and survival data

**Background:** Chronic hepatitis C is a severe and increasing public health issue. Although nowadays most patients can be cured, the infection is often undetected until symptoms of permanent liver damage become apparent, putting patients at a …

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.

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