Missing data is a common occurrence in clinical research. Missing data occurs when the
value of the variables of interest are not measured or recorded for all subjects in the sample …

This methodological guidance article discusses the elements of a high-quality meta-analysis
that is conducted within the context of a systematic review. Meta-analysis, a set of statistical …

Background Adequate maternal thyroid function is important for an uncomplicated
pregnancy. Although multiple observational studies have evaluated the association between …

Simulation studies are computer experiments that involve creating data by pseudo‐random
sampling. A key strength of simulation studies is the ability to understand the behavior of …

Background Multiple imputation (MI) is now widely used to handle missing data in
longitudinal studies. Several MI techniques have been proposed to impute incomplete …

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 …

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 …

Abstract The UN's Sustainable Development Goals for 2030 aim on one hand at inclusive
growth and eradicating poverty, and on the other at preserving environments. The relation …

Missing data are an unavoidable complication in many machine learning tasks. When data
are 'missing at random'there exist a range of tools and techniques to deal with the issue …

Meta-analyses contribute critically to cumulative science, but they can produce misleading
conclusions if their constituent primary studies are biased, for example by unmeasured …