Basic Sensitivity Analysis of Epidemiological Results

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Documentation for package ‘episensr’ version 2.0.0

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%>% Pipe bias functions
boot.bias Bootstrap resampling for selection and misclassification bias models.
confounders Uncontrolled confounding
confounders.array Sensitivity analysis for unmeasured confounders based on confounding imbalance among exposed and unexposed
confounders.emm Uncontrolled confounding
confounders.evalue Compute E-value to assess bias due to unmeasured confounder.
confounders.ext Sensitivity analysis for unmeasured confounders based on external adjustment
confounders.limit Bounding the bias limits of unmeasured confounding.
confounders.poly Uncontrolled confounding
mbias Sensitivity analysis to correct for selection bias caused by M bias.
misclass Misclassification of exposure or outcome
misclass_cov Covariate misclassification
multidimBias Multidimensional sensitivity analysis for different sources of bias
plot.episensr.booted Plot of bootstrap simulation output for selection and misclassification bias
plot.episensr.probsens Plot(s) of probabilistic bias analyses
plot.mbias Plot DAGs before and after conditioning on collider (M bias)
print.episensr Print associations for episensr class
print.episensr.booted Print bootstrapped confidence intervals
print.mbias Print association corrected for M bias
probsens Misclassification of exposure or outcome
probsens.conf_legacy Legacy version of 'probsens.conf()'.
probsens.irr Probabilistic sensitivity analysis for exposure misclassification of person-time data and random error.
probsens.irr.conf Probabilistic sensitivity analysis for unmeasured confounding of person-time data and random error.
probsens.irr.conf_legacy Legacy version of 'probsens.irr.conf()'.
probsens.irr_legacy Legacy version of 'probsens.irr()'.
probsens.sel Selection bias.
probsens_conf Uncontrolled confounding
probsens_legacy Legacy version of 'probsens()'.
selection Selection bias.