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 …
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**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 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, …
**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 …
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 …