SAMBA: Selection and Misclassification Bias Adjustment for Logistic
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.
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