Basic Sensitivity Analysis of Epidemiological Results

[Up] [Top]

Documentation for package ‘episensr’ version 2.1.0

Help Pages

%>% 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.conf_legacy Legacy version of 'probsens.irr.conf()'.
probsens.irr_legacy Legacy version of 'probsens.irr()'.
probsens_conf Uncontrolled 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_legacy Legacy version of 'probsens()'.
probsens_sel Selection bias.
selection Selection bias.