Missing data are ubiquitous in medical research, yet there is still uncertainty over when
restricting to the complete records is likely to be acceptable, when more complex methods …

This article reviews recent advances in missing data research using graphical models to
represent multivariate dependencies. We first examine the limitations of traditional …

Background National and global recommendations for BMI cutoffs to trigger action to prevent
obesity-related complications like type 2 diabetes among non-White populations are …

Objectives Researchers are concerned whether multiple imputation (MI) or complete case
analysis should be used when a large proportion of data are missing. We aimed to provide …

Background Missing data are unavoidable in epidemiological research, potentially leading
to bias and loss of precision. Multiple imputation (MI) is widely advocated as an …

Background & Aims Accumulating animal studies have demonstrated the harmful
contribution of ambient air pollution (AP) to metabolic dysfunction-associated fatty liver …

Missing data are ubiquitous in medical research. Although there is increasing guidance on
how to handle missing data, practice is changing slowly and misapprehensions abound …

Large-scale cross-sectional and cohort studies have transformed our understanding of the
genetic and environmental determinants of health outcomes. However, the …

Principled methods with which to appropriately analyze missing data have long existed;
however, broad implementation of these methods remains challenging. In this and 2 …

Parkinson's disease is a neurodegenerative movement disorder that currently has no
disease-modifying treatment, partly owing to inefficiencies in drug target identification and …