SAMBA: 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) <doi:10.1101/2019.12.26.19015859>, currently under review.

Package details

AuthorAlexander Rix [cre], Lauren Beesley [aut]
MaintainerAlexander Rix <>
Package repositoryView on CRAN
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SAMBA documentation built on Feb. 20, 2020, 9:07 a.m.