Confounding is an important source of bias, but it is often misunderstood. We consider how
confounding occurs and how to address confounding using examples. Study results are …

Confounding biases study results when the effect of the exposure on the outcome mixes with
the effects of other risk and protective factors for the outcome that are present differentially by …

Observational studies, when done well, enable inferences of causal associations. However,
most observational studies cannot achieve inferences regarding causality, because of the …

Prospective and retrospective cohorts and case-control studies are some of the most
important study designs in epidemiology because, under certain assumptions, they can …

Cross‐sectional studies serve many purposes, and the cross‐sectional design is the most
relevant design when assessing the prevalence of disease, attitudes and knowledge among …

Confounding occurs when the subpopulations of the source population being compared
would have different outcomes over the risk period under study, even if they were subject to …

A critical assumption of causal inference is that of no unmeasured confounding: for
estimated exposure effects to have valid causal interpretations, a sufficient set of predictors …

In research addressing causal questions about relations between exposures and outcomes,
confounding is an issue when effects of interrelated exposures on an outcome are confused …

Abstract Information bias occurs when any information used in a study is either measured or
recorded inaccurately. This paper describes some of the most common types of information …

Confounding is a bias due to the presence of common causes of the exposure and the
outcome. Subject‐matter knowledge can be used to draw causal directed acyclic graphs …