Function reference

All functions

boot.bias()

Bootstrap resampling for selection and misclassification bias models.

confounders()

Sensitivity analysis to correct for unknown or unmeasured confounding without effect modification

confounders.array()

Sensitivity analysis for unmeasured confounders based on confounding imbalance among exposed and unexposed

confounders.emm()

Sensitivity analysis to correct for unknown or unmeasured confounding in the presence of effect modification

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()

Sensitivity analysis to correct for unknown or unmeasured polychotomous confounding without effect modification

episensr-package episensr

episensr: Basic sensitivity analysis of epidemiological results

mbias()

Sensitivity analysis to correct for selection bias caused by M bias.

misclassification()

Sensitivity analysis for disease or exposure misclassification.

misclassification.cov()

Sensitivity analysis for covariate misclassification.

multidimBias()

Multidimensional sensitivity analysis for different sources of bias

multiple.bias()

Extract adjusted 2-by-2 table from episensr object

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Pipe bias functions

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()

Probabilistic sensitivity analysis.

probsens.conf()

Probabilistic sensitivity analysis for unmeasured confounding.

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.sel()

Probabilistic sensitivity analysis for selection bias.

selection()

Sensitivity analysis to correct for selection bias.