Overadjustment is defined inconsistently. This term is meant to describe control (eg, by
regression adjustment, stratification, or restriction) for a variable that either increases net …

Control for relevant covariates is unquestionably of importance in drawing inferences about
causation from observational data. Determining which covariates warrant or require control …

When epidemiologic studies are conducted in a subset of the population, selection bias can
threaten the validity of causal inference. This bias can occur whether or not that selected …

Most epidemiology textbooks that discuss models are vague on details of model selection.
This lack of detail may be understandable since selection should be strongly influenced by …

Epidemiologists often use the potential outcomes framework to cast causal inference as a
missing data problem. Here, we demonstrate how bias due to measurement error can be …

Standard variable selection procedures, primarily developed for the construction of outcome
prediction models, are routinely applied when assessing exposure effects in observational …

The term “selection bias” encompasses various biases in epidemiology. We describe
examples of selection bias in case-control studies (eg, inappropriate selection of controls) …

Preterm delivery is a powerful predictor of newborn morbidity and mortality. Such problems
are due to not only immaturity but also the pathologic factors (such as infection) that cause …

In choosing covariates for adjustment or inclusion in propensity score analysis, researchers
must weigh the benefit of reducing confounding bias carried by those covariates against the …

Inferring causation from non‐randomized studies of exposure requires that exposure groups
can be balanced with respect to prognostic factors for the outcome. Although there is broad …