Multiple Imputation to Handle Missing Values in Clinical Research
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
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 …
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 …
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 …