The workshop will consider sensitivity analysis for different forms of bias in epidemiology. It will begin with confounding, focusing on a new metric to evaluate sensitivity to unmeasured confounding called the E-value. The E-value is the minimum strength of association, on the risk ratio scale, that an unmeasured confounder would need to have with both the exposure and the outcome, conditional on the measured covariates, to fully explain away the exposure-outcome association. E-value calculations for risk ratios, outcomes differences, odds ratios, and hazard ratios will be discussed. The E-value can be calculated in a straightforward way from study results and its use could help unify assessment of unmeasured confounding. The workshop will proceed by describing very recent analogous easy-to-implement approaches to also address differential measurement error and selection bias. We will conclude by presenting recent extensions allowing sensitivity analysis for all three forms of bias. The methods, taken as a whole, will constitute a straightforward comprehensive approach to bias analysis.
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