tipr: An R package for sensitivity analyses forunmeasured confounders
Lucy D’Agostino McGowan
Abstract
The strength of evidence provided by epidemiological and observational studies is inherently limited by the potential for unmeasured confounding. We focus on three key quantities: the observed bound of the confidence interval closest to the null, the relationship between an unmeasured confounder and the outcome, for example a plausible residual effect size for an unmeasured continuous or binary confounder, and the relationship between an unmeasured confounder and the exposure, for example a realistic mean difference or prevalence difference for this hypothetical confounder between exposure groups. Building on the methods put forth by Cornfield et al.
Topics & Concepts
Sensitivity (control systems)ConfoundingR packageStatisticsComputer scienceMathematicsEngineeringElectronic engineeringAdvanced Causal Inference TechniquesStatistical Methods and InferenceStatistical Methods and Bayesian Inference