Package: episensr 1.3.0

Denis Haine

episensr: Basic Sensitivity Analysis of Epidemiological Results

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).

Authors:Denis Haine [aut, cre]

episensr_1.3.0.tar.gz
episensr_1.3.0.zip(r-4.7-any)episensr_1.3.0.zip(r-4.6-any)episensr_1.3.0.zip(r-4.5-any)
episensr_1.3.0.tgz(r-4.6-any)episensr_1.3.0.tgz(r-4.5-any)
episensr_1.3.0.tar.gz(r-4.7-any)episensr_1.3.0.tar.gz(r-4.6-any)
episensr_1.3.0.tgz(r-4.6-emscripten)
manual.pdf |manual.html✨
DESCRIPTION |NEWS
card.svg |card.png
episensr/json (API)

# Install 'episensr' in R:
install.packages('episensr', repos = c('https://epiverse-connect.r-universe.dev', 'https://cloud.r-project.org'))

Bug tracker:https://github.com/dhaine/episensr/issues

On CRAN:

Conda:

biasepidemiologysensitivity-analysisstatistics

5.93 score 14 stars 1 packages 102 scripts 738 downloads 1 mentions 20 exports 60 dependencies

Last updated from:ff3badbe31. Checks:7 NOTE, 2 OK. Indexed: no.

TargetResultTimeFilesSyslog
linux-develNOTE161
source / vignettesOK252
linux-releaseNOTE230
macos-releaseNOTE118
macos-oldrelNOTE150
windows-develNOTE159
windows-releaseNOTE136
windows-oldrelNOTE131
wasm-releaseOK116

Exports:%>%boot.biasconfoundersconfounders.arrayconfounders.emmconfounders.evalueconfounders.extconfounders.limitconfounders.polymbiasmisclassificationmisclassification.covmultidimBiasmultiple.biasprobsensprobsens.confprobsens.irrprobsens.irr.confprobsens.selselection

Dependencies:actuarbase64encbootcachemclicpp11curldagittydplyrexpintfarverfastmapforcatsgenericsggdagggforceggplot2ggraphggrepelgluegraphlayoutsgridExtragtableigraphisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmemoisepillarpkgconfigpolyclippurrrR6RColorBrewerRcppRcppArmadillorlangS7scalesstringistringrsystemfontstibbletidygraphtidyrtidyselecttrapezoidtriangletweenrutf8V8vctrsviridisviridisLitewithr

Quantitative Bias Analysis for Epidemiologic Data
Selection Bias | Misclassification Bias | Uncontrolled Confounders | Bootstrapping
Rendered fromepisensr.Rmdusingknitr::rmarkdown

Last update: 2023-08-29
Started: 2017-01-03

Multiple Bias Modeling
Rendered fromc_multiple_bias.Rmdusingknitr::rmarkdown

Last update: 2023-05-16
Started: 2020-10-16

Additional Sensitivity Analyses
Array Approach for Unmeasured Confounders | Simple Sensitivity Analyses in the Absence of External Information: Array Approach | Using Additional Information: External Adjustment | Unmeasured Confounding and E-value
Rendered fromd_other_sens.Rmdusingknitr::rmarkdown

Last update: 2023-05-16
Started: 2020-10-16

Probabilistic Sensitivity Analysis
Probabilistic Sensitivity Analysis for Exposure Misclassification | Probabilistic Sensitivity Analysis for Uncontrolled Confounding
Rendered fromb_probabilistic.Rmdusingknitr::rmarkdown

Last update: 2021-07-08
Started: 2020-10-16

Readme and manuals

Help Manual

Help pageTopics
episensr: Basic sensitivity analysis of epidemiological resultsepisensr-package episensr
Pipe bias functions%>%
Bootstrap resampling for selection and misclassification bias models.boot.bias
Sensitivity analysis to correct for unknown or unmeasured confounding without effect modificationconfounders
Sensitivity analysis for unmeasured confounders based on confounding imbalance among exposed and unexposedconfounders.array
Sensitivity analysis to correct for unknown or unmeasured confounding in the presence of effect modificationconfounders.emm
Compute E-value to assess bias due to unmeasured confounder.confounders.evalue
Sensitivity analysis for unmeasured confounders based on external adjustmentconfounders.ext
Bounding the bias limits of unmeasured confounding.confounders.limit
Sensitivity analysis to correct for unknown or unmeasured polychotomous confounding without effect modificationconfounders.poly
Sensitivity analysis to correct for selection bias caused by M bias.mbias
Sensitivity analysis for disease or exposure misclassification.misclassification
Sensitivity analysis for covariate misclassification.misclassification.cov
Multidimensional sensitivity analysis for different sources of biasmultidimBias
Extract adjusted 2-by-2 table from episensr objectmultiple.bias
Plot of bootstrap simulation output for selection and misclassification biasplot.episensr.booted
Plot(s) of probabilistic bias analysesplot.episensr.probsens
Plot DAGs before and after conditioning on collider (M bias)plot.mbias
Print associations for episensr classprint.episensr
Print bootstrapped confidence intervalsprint.episensr.booted
Print association corrected for M biasprint.mbias
Probabilistic sensitivity analysis.probsens
Probabilistic sensitivity analysis for unmeasured confounding.probsens.conf
Probabilistic sensitivity analysis for exposure misclassification of person-time data and random error.probsens.irr
Probabilistic sensitivity analysis for unmeasured confounding of person-time data and random error.probsens.irr.conf
Probabilistic sensitivity analysis for selection bias.probsens.sel
Sensitivity analysis to correct for selection bias.selection