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

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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Missing Data - Challenges and Solutions

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 …

Dealing with missing covariates in epidemiologic studies: A comparison between multiple imputation and a full Bayesian approach

Incomplete data are generally a challenge to the analysis of most large studies. The current gold standard to account for missing data is multiple imputation, and more specifically multiple imputation with chained equations (MICE). Numerous studies …