imputation

Joint Models for Incomplete Longitudinal and Survival Data

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Working with Incomplete Data: When One-size-fits-all does not fit

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Imputation of Missing Covariates in Longitudinal Data

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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.

R package JointAI

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