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) , currently under review.


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0.9.0 by Alexander Rix, a year ago

Browse source code at

Authors: Alexander Rix [cre] , Lauren Beesley [aut]

Documentation:   PDF Manual  

GPL-3 license

Imports stats, optimx, survey

Suggests knitr, rmarkdown, ggplot2, scales, MASS

See at CRAN