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 <pcbrendel at gmail.com> BugReports: https://github.com/pcbrendel/multibias/issues License: MIT + file LICENSE 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 form https://CRAN.R-project.org/package=multibias to link to this page.
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