The hardware and bandwidth for this mirror is donated by METANET, the Webhosting and Full Service-Cloud Provider.
If you wish to report a bug, or if you are interested in having us mirror your free-software or open-source project, please feel free to contact us at mirror[@]metanet.ch.
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 and methods, please 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 Biometrics 10.1111/biom.13400
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.