Controlling for too many potential confounders can lead to or aggravate problems of data
sparsity or multicollinearity, particularly when the number of covariates is large in relation to …

Longitudinal studies, where data are repeatedly collected on subjects over a period, are
common in medical research. When estimating the effect of a time‐varying treatment or …

In diverse fields of empirical research—including many in the biological sciences—attempts
are made to decompose the effect of an exposure on an outcome into its effects via a …

Background: Mendelian randomization uses genetic variants, assumed to be instrumental
variables for a particular exposure, to estimate the causal effect of that exposure on an …

The mediation formula for the identification of natural (in) direct effects has facilitated
mediation analyses that better respect the nature of the data, with greater consideration of …

Community-acquired lower respiratory tract infections (LRTI) and pneumonia (CAP) are
common causes of morbidity and mortality among those aged≥ 65 years; a growing …

Multiple imputation is a popular way to handle missing data. Automated procedures are
widely available in standard software. However, such automated procedures may hide many …

Foetal alcohol spectrum disorders (FASDs) are a leading cause of developmental disability.
Prenatal alcohol use is the sole necessary cause of FASD, but it is not always sufficient …

Estimating causal effects from incomplete data requires additional and inherently untestable
assumptions regarding the mechanism giving rise to the missing data. We show that using …

Propensity scores are widely adopted in observational research because they enable
adjustment for high‐dimensional confounders without requiring models for their association …