imputation

EP16: Missing Data in Clinical Research

Multiple Imputation to Handle Missing Values in 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 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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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 …

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