Basic Sensitivity Analysis of Epidemiological Results
Bootstrap resampling for selection and misclassification bias models.
Sensitivity analysis for unmeasured confounders based on confounding i...
Sensitivity analysis to correct for unknown or unmeasured confounding ...
Compute E-value to assess bias due to unmeasured confounder.
Sensitivity analysis for unmeasured confounders based on external adju...
Bounding the bias limits of unmeasured confounding.
Sensitivity analysis to correct for unknown or unmeasured polychotomou...
Sensitivity analysis to correct for unknown or unmeasured confounding ...
episensr: Basic sensitivity analysis of epidemiological results
Sensitivity analysis to correct for selection bias caused by M bias.
Sensitivity analysis for covariate misclassification.
Sensitivity analysis for disease or exposure misclassification.
Multidimensional sensitivity analysis for different sources of bias
Extract adjusted 2-by-2 table from episensr object
Pipe bias functions
Plot of bootstrap simulation output for selection and misclassificatio...
Plot(s) of probabilistic bias analyses
Plot DAGs before and after conditioning on collider (M bias)
Print bootstrapped confidence intervals
Print associations for episensr class
Print association corrected for M bias
Probabilistic sensitivity analysis for unmeasured confounding.
Probabilistic sensitivity analysis for unmeasured confounding of perso...
Probabilistic sensitivity analysis for exposure misclassification of p...
Probabilistic sensitivity analysis.
Probabilistic sensitivity analysis for selection bias.
Sensitivity analysis to correct for selection bias.
Basic sensitivity analysis of the observed relative risks adjusting for unmeasured confounding and misclassification of the exposure/outcome, or both. It follows the bias analysis methods and examples from the book by Lash T.L, Fox M.P, and Fink A.K. "Applying Quantitative Bias Analysis to Epidemiologic Data", ('Springer', 2021).
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