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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), currently under review.


SAMBA can be downloaded from Github via the R Package devtools

devtools::install_github("umich-cphds/SAMBA", build_opts = c())


Once you have SAMBA installed, you can type


in R to bring up a tutorial on SAMBA and how to use it.


For questions and comments about the implementation, please contact Alexander Rix ( For questions about the method, contact Lauren Beesley (


Statistical inference for association studies using electronic health records: handling both selection bias and outcome misclassification Lauren J Beesley, Bhramar Mukherjee medRxiv 2019.12.26.19015859