Stacked generalization is an ensemble method that allows researchers to combine several
different prediction algorithms into one. Since its introduction in the early 1990s, the method …

Recent methodological innovation is giving rise to an increasing number of applied papers
in medical and epidemiological journals in which natural direct and indirect effects are …

Robins’ generalized methods (g methods) provide consistent estimates of contrasts (eg
differences, ratios) of potential outcomes under a less restrictive set of identification conditions …

All statistical estimates from data have uncertainty due to sampling variability. A standard
error is one measure of uncertainty of a sample estimate (such as the mean of a set of …

Background: The parametric g-formula can be used to estimate the effect of a policy, intervention,
or treatment. Unlike standard regression approaches, the parametric g-formula can be …

Generalized linear models (GLMs) are often used with binary outcomes to estimate odds
ratios. Though not as widely appreciated, GLMs can also be used to quantify risk differences, …

Inverse probability–weighted marginal structural models with binary exposures are common
in epidemiology. Constructing inverse probability weights for a continuous exposure can be …

Social epidemiologists often seek to determine the mechanisms that underlie health
disparities. This work is typically based on mediation procedures that may not be justified with …

Background: The relationship between ambient temperature and risk of delivery is poorly
understood. We examined the association between heat and risk of delivery among preterm …

Unlike parametric regression, machine learning (ML) methods do not generally require
precise knowledge of the true data-generating mechanisms. As such, numerous authors have …