multibias

multibias-package

<img src="figures/logo.png?package=multibias&version=1.6.3" style="float: right" alt="logo" width="120">
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) tools:::Rd_expr_doi("10.1093/ije/dyad001").

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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>.

Paul Brendel

Simultaneous Multi-Bias Adjustment

multibias-package function

Maintainer: Paul Brendel <a href="/link/pcbrendel%40gmail.com?package=multibias&version=1.6.3" data-mini-rdoc="multibias::pcbrendel@gmail.com">pcbrendel@gmail.com</a> [copyright holder]

Author

<img src='figures/logo.png' style='float: right' alt='logo' width='120' />
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) tools:::Rd_expr_doi("10.1093/ije/dyad001").

multibias: Simultaneous Multi-Bias Adjustment — multibias-package

Maintainer: Paul Brendel <a href='mailto:pcbrendel@gmail.com'>pcbrendel@gmail.com</a> [copyright holder]

multibias-package: multibias: Simultaneous Multi-Bias Adjustment

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