Package index • episensr

Overview

Package overview

episensr-package episensr: episensr: Basic Sensitivity Analysis for Epidemiological Results

Selection bias

selection() probsens.sel(): Selection bias.

mbias(): Sensitivity analysis to correct for selection bias caused by M bias.

plot(<mbias>): Plot DAGs before and after conditioning on collider (M bias)

Uncontrolled Confounders

confounders() confounders.emm() confounders.poly() probsens_conf(): Uncontrolled confounding

confounders.array(): Sensitivity analysis for unmeasured confounders based on confounding imbalance among exposed and unexposed

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.

probsens.irr.conf(): Probabilistic sensitivity analysis for unmeasured confounding of person-time data and random error.

Misclassification

misclass() probsens(): Misclassification of exposure or outcome

plot(<episensr.probsens>): Plot(s) of probabilistic bias analyses

misclass_cov(): Covariate misclassification

probsens.irr(): Probabilistic sensitivity analysis for exposure misclassification of person-time data and random error.

Bootstrap

Bootstrap resampling for selection and misclassification bias

boot.bias(): Bootstrap resampling for selection and misclassification bias models.

plot(<episensr.booted>): Plot of bootstrap simulation output for selection and misclassification bias

Multidimensional Bias Analysis

multidimBias(): Multidimensional sensitivity analysis for different sources of bias

Miscellaneous

Functions to pipe one function to the other, and print procedures.

%>%: Pipe bias functions

print(<episensr>): Print associations for episensr class

print(<episensr.booted>): Print bootstrapped confidence intervals

print(<mbias>): Print association corrected for M bias

Superseded functions

Probabilistic bias analyses have updated calculations. These updated versions should be preferred but if you need to run an old analysis, you can easily revert by using these functions below (see help in the respective probsens() functions for more detailed explanations).

probsens.conf_legacy() superseded: Legacy version of probsens.conf().

probsens.irr.conf_legacy() superseded: Legacy version of probsens.irr.conf().

probsens.irr_legacy() superseded: Legacy version of probsens.irr().

probsens_legacy() superseded: Legacy version of probsens().