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
vignette("UsingSAMBA")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 (alexrix@umich.edu). For questions about the method, contact Lauren Beesley (lbeesley@umich.edu).
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