Package: SAMBA
Title: Selection and Misclassification Bias Adjustment for Logistic
        Regression Models
Version: 0.9.0
Authors@R: c(
    person("Alexander", "Rix", email = "alexrix@umich.edu", role = "cre"),
    person("Lauren", "Beesley", email = "lbeesley@umich.edu", role = "aut")
    )
Description: 
    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.
License: GPL-3
Encoding: UTF-8
LazyData: true
RoxygenNote: 6.1.1
Imports: stats, optimx, survey
Suggests: knitr, rmarkdown, ggplot2, scales, MASS
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2020-02-10 12:37:09 UTC; alexrix
Author: Alexander Rix [cre],
  Lauren Beesley [aut]
Maintainer: Alexander Rix <alexrix@umich.edu>
Repository: CRAN
Date/Publication: 2020-02-20 07:50:07 UTC
Built: R 4.6.0; ; 2026-04-26 06:37:43 UTC; unix
