CRAN/E | SAMBA

SAMBA

Selection and Misclassification Bias Adjustment for Logistic Regression Models

Installation

About

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.

Key Metrics

Version 0.9.0
Published 2020-02-20 1580 days ago
Needs compilation? no
License GPL-3
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Maintainer

Maintainer

Alexander Rix

Authors

Alexander Rix

cre

Lauren Beesley

aut

Material

README
NEWS
Reference manual
Package source

Vignettes

UsingSAMBA

macOS

r-release

arm64

r-oldrel

arm64

r-release

x86_64

r-oldrel

x86_64

Windows

r-devel

x86_64

r-release

x86_64

r-oldrel

x86_64

Imports

stats
optimx
survey

Suggests

knitr
rmarkdown
ggplot2
scales
MASS