SAMBA: Selection and Misclassification Bias Adjustment for Logistic Regression Models

Health research using data from electronic health records (EHR) has gained popularity, but misclassification of EHR-derived disease status and lack of representativeness of the study sample can result in substantial bias in effect estimates and can impact power and type I error for association tests. Here, the assumed target of inference is the relationship between binary disease status and predictors modeled using a logistic regression model. 'SAMBA' implements several methods for obtaining bias-corrected point estimates along with valid standard errors as proposed in Beesley and Mukherjee (2020) <doi:10.1101/2019.12.26.19015859>, currently under review.

Version: 0.9.0
Imports: stats, optimx, survey
Suggests: knitr, rmarkdown, ggplot2, scales, MASS
Published: 2020-02-20
Author: Alexander Rix [cre], Lauren Beesley [aut]
Maintainer: Alexander Rix <alexrix at>
License: GPL-3
NeedsCompilation: no
Materials: README NEWS
CRAN checks: SAMBA results


Reference manual: SAMBA.pdf
Vignettes: UsingSAMBA
Package source: SAMBA_0.9.0.tar.gz
Windows binaries: r-prerelease:, r-release:, r-oldrel:
macOS binaries: r-prerelease: SAMBA_0.9.0.tgz, r-release: SAMBA_0.9.0.tgz, r-oldrel: SAMBA_0.9.0.tgz


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