non-linear

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

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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.