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
DOI:	10.32614/CRAN.package.SAMBA
Author:	Alexander Rix [cre], Lauren Beesley [aut]
Maintainer:	Alexander Rix <alexrix at umich.edu>
License:	GPL-3
NeedsCompilation:	no
Materials:	README NEWS
CRAN checks:	SAMBA results

Documentation:

Reference manual:	SAMBA.pdf
Vignettes:	UsingSAMBA (source, R code)

Downloads:

Package source:	SAMBA_0.9.0.tar.gz
Windows binaries:	r-devel: SAMBA_0.9.0.zip, r-release: SAMBA_0.9.0.zip, r-oldrel: SAMBA_0.9.0.zip
macOS binaries:	r-release (arm64): SAMBA_0.9.0.tgz, r-oldrel (arm64): SAMBA_0.9.0.tgz, r-release (x86_64): SAMBA_0.9.0.tgz, r-oldrel (x86_64): SAMBA_0.9.0.tgz

Reverse dependencies:

Reverse imports:

COMBO

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