Despite the fact that randomization is the gold standard for estimating causal relationships,
many questions in prevention science are often left to be answered through …

Unmeasured confounding may undermine the validity of causal inference with observational
studies. Sensitivity analysis provides an attractive way to partially circumvent this issue by …

Background The impact of unmeasured confounders on causal associations can be studied
by means of sensitivity analyses. Although several sensitivity analyses are available, these …

The term confounding by indication is increasingly used in the literature, although the
concept has lost much of its original meaning. The literature includes instances where …

Purpose We review statistical methods for assessing the possible impact of bias due to
unmeasured confounding in real world data analysis and provide detailed …

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 …

Uncontrolled confounding due to unmeasured confounders biases causal inference in
health science studies using observational and imperfect experimental designs. The …

Purpose Observational pharmacoepidemiological studies can provide valuable information
on the effectiveness or safety of interventions in the real world, but one major challenge is …

Noncausal associations between exposures and outcomes are a threat to validity of causal
inference in observational studies. Many techniques have been developed for study design …

Uncontrolled confounding in observational studies gives rise to biased effect estimates.
Sensitivity analysis techniques can be useful in assessing the magnitude of these biases. In …