doi:10.1093/ije/dyad001>.">

multibias: Multiple Bias Analysis in Causal Inference (original) (raw)

Quantify the causal effect of a binary exposure on a binary outcome with adjustment for multiple biases. The functions can simultaneously adjust for any combination of uncontrolled confounding, exposure/outcome misclassification, and selection bias. The underlying method generalizes the concept of combining inverse probability of selection weighting with predictive value weighting. Simultaneous multi-bias analysis can be used to enhance the validity and transparency of real-world evidence obtained from observational, longitudinal studies. Based on the work from Paul Brendel, Aracelis Torres, and Onyebuchi Arah (2023) <doi:10.1093/ije/dyad001>.

Version: 1.7.2
Depends: R (≥ 4.2.0)
Imports: dplyr (≥ 1.1.3), lifecycle (≥ 1.0.3), magrittr (≥ 2.0.3), rlang (≥ 1.1.1), broom (≥ 1.0.5), purrr (≥ 1.0.0), ggplot2 (≥ 3.5.0)
Suggests: knitr, rmarkdown, MASS, testthat (≥ 3.0.0), vdiffr (≥ 1.0.5)
Published: 2025-06-15
DOI: 10.32614/CRAN.package.multibias
Author: Paul Brendel [aut, cre, cph]
Maintainer: Paul Brendel
BugReports: https://github.com/pcbrendel/multibias/issues
License: MIT + file
URL: https://github.com/pcbrendel/multibias,http://www.paulbrendel.com/multibias/
NeedsCompilation: no
Materials: README, NEWS
CRAN checks: multibias results

Documentation:

Downloads:

Linking:

Please use the canonical formhttps://CRAN.R-project.org/package=multibiasto link to this page.