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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
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
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.
Health stats visible at Monitor.