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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 umich.edu> |
| 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-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 imports: | COMBO |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
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