Sensitivity Analyses for Unmeasured Confounders

Abstract Purpose of Review This review expands on sensitivity analyses for unmeasured confounding techniques, demonstrating state-of-the-art methods as well specifying which should be used under various scenarios, depending the information about a potential confounder available to researcher. Recent Findings Methods assess how sensitive an observed estimate is have been developed decades. advancements allowed incorporation measured confounders in these assessments, updating quantify impact confounder, whether specified terms magnitude effect from regression standpoint, example, risk ratio, or with respect percent variation outcome exposure explained by confounder. Additionally, single number summaries, such E-value robustness value, proposed allow ease computation when less known specific Summary paper aimed provide and tools implement appropriate research settings what assumed We provided mathematical justification, recommendations, R code implementation methods.