SAMBA: Selection and Misclassification Bias Adjustment for Logistic Regression Models (original) (raw)
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 |
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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 |
License: | GPL-3 |
NeedsCompilation: | no |
Materials: | README NEWS |
CRAN checks: | SAMBA results |
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