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, …

Imputation of incomplete covariates in longitudinal data: Can Bayesian non-parametric methods prevent model-misspecification?

**Context:** This work is motivated by a study in Type II diabetes patients and their progression to diabetic retinopathy. Specifically, physicians are interested in identifying risk factors, longitudinal and baseline, for progression. An important …

Analysis and Imputation Using the R Package JointAI

Imputation model misspecification: how robust are Bayesian methods?

Missing values complicate analyses in many studies. Nevertheless, the availability nowadays of methods, such as Multiple Imputation (MI) in standard software, has enabled researchers to perform statistical analysis accounting for missing data. More …

Imputation of missing covariates: when standard methods may fail

Our work is motivated by examples from two large cohort studies, the Generation R Study and the Rotterdam Study, in which the analysis models of interest involved non-linear effects, interaction terms or had a longitudinal outcome. As is the case for …