The year 2022 is the 20th anniversary of Joseph Schafer and John Graham's paper titled
“Missing data: Our view of the state of the art,” currently the most highly cited paper in the …

Multiple imputation is a straightforward method for handling missing data in a principled
fashion. This paper presents an overview of multiple imputation, including important …

In data analytics, missing data is a factor that degrades performance. Incorrect imputation of
missing values could lead to a wrong prediction. In this era of big data, when a massive …

Missing data pose challenges to real-life data analysis. Simple ad-hoc fixes, like deletion or
mean imputation, only work under highly restrictive conditions, which are often not met in …

Missing data is a significant problem impacting all domains. State-of-the-art framework for
minimizing missing data bias is multiple imputation, for which the choice of an imputation …

Many texts are excellent sources of knowledge about individual statistical tools, but the art of
data analysis is about choosing and using multiple tools. Instead of presenting isolated …

Soldiers and veterans diagnosed with PTSD benefit less from psychotherapy than non-
military populations. The current meta-analysis identified treatment predictors for …

Background Osteoarthritis (OA) is a chronic joint disease, with increasing global burden of
disability and healthcare utilisation. Recent meta-analyses have shown a range of effects of …

Exploratory factor analysis (EFA) is an extremely popular method for determining the
underlying factor structure for a set of variables. Due to its exploratory nature, EFA is …

Predictive mean matching (PMM) is a standard technique for the imputation of incomplete
continuous data. PMM imputes an actual observed value, whose predicted value is among a …