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-4.5)episensr_1.3.0.zip(r-4.4)episensr_1.3.0.zip(r-4.3)
episensr_1.3.0.tgz(r-4.4-any)episensr_1.3.0.tgz(r-4.3-any)
episensr_1.3.0.tar.gz(r-4.5-noble)episensr_1.3.0.tar.gz(r-4.4-noble)
episensr_1.3.0.tgz(r-4.4-emscripten)episensr_1.3.0.tgz(r-4.3-emscripten)
episensr.pdf |episensr.html✨
episensr/json (API)
NEWS

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

Peer review:

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

On CRAN:

biasepidemiologysensitivity-analysisstatistics

6.44 score 12 stars 1 packages 38 scripts 438 downloads 1 mentions 20 exports 65 dependencies

Last updated 9 months agofrom:fcacc9272f. Checks:OK: 5 NOTE: 2. Indexed: yes.

TargetResultDate
Doc / VignettesOKNov 08 2024
R-4.5-winNOTENov 08 2024
R-4.5-linuxNOTENov 08 2024
R-4.4-winOKNov 08 2024
R-4.4-macOKNov 08 2024
R-4.3-winOKNov 08 2024
R-4.3-macOKNov 08 2024

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

Dependencies:actuarassertthatbootcachemclicolorspacecpp11curldagittydplyrexpintfansifarverfastmapforcatsgenericsggdagggforceggplot2ggraphggrepelgluegraphlayoutsgridExtragtableigraphisobandjsonlitelabelinglatticelifecyclemagrittrMASSMatrixmemoisemgcvmunsellnlmepillarpkgconfigpolyclippurrrR6RColorBrewerRcppRcppArmadilloRcppEigenrlangscalesstringistringrsystemfontstibbletidygraphtidyrtidyselecttrapezoidtriangletweenrutf8V8vctrsviridisviridisLitewithr

Additional Sensitivity Analyses

Denis Haine

Rendered fromd_other_sens.Rmdusingknitr::rmarkdownon Nov 08 2024.

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

Multiple Bias Modeling

Denis Haine

Rendered fromc_multiple_bias.Rmdusingknitr::rmarkdownon Nov 08 2024.

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

Probabilistic Sensitivity Analysis

Denis Haine

Rendered fromb_probabilistic.Rmdusingknitr::rmarkdownon Nov 08 2024.

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

Quantitative Bias Analysis for Epidemiologic Data

Denis Haine

Rendered fromepisensr.Rmdusingknitr::rmarkdownon Nov 08 2024.

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

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