Misclassification is present in nearly every epidemiologic study, yet is rarely quantified in
analysis in favor of a focus on random error. In this review, we discuss past and present …

Sensitivity analysis is useful in assessing how robust an association is to potential
unmeasured or uncontrolled confounding. This article introduces a new measure called the …

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

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 …

The technological ability to make personal measurements of toxicant exposures is growing
rapidly. While this can decrease measurement error and therefore help reduce attenuation …

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 …

To identify the estimand in missing data problems and observational studies, it is common to
base the statistical estimation on the 'missingness at random'and 'no unmeasured …

In the last few years, stakeholders in the scientific community have raised alarms about a
perceived lack of reproducibility of scientific results. In reaction, guidelines for journals have …

Objective To estimate the degree to which fine particulate (PM2. 5) air pollution is associated
with systemic autoimmune rheumatic diseases (SARDs). Methods We used population …

In this paper we present a framework for addressing a variety of engineering design
challenges with limited empirical data and partial information. This framework includes …