https://lbeesleybiostat.r-universe.devRecent package updates in lbeesleybiostatR-universehttps://github.com/lbeesleybiostat.pnghttps://lbeesleybiostat.r-universe.devWed, 03 Jun 2026 12:15:30 GMTlbeesley@umich.edu (Lauren Beesley)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:10.1111/biom.13400>, Biometrics.https://github.com/r-universe/lbeesleybiostat/actions/runs/26940709853Wed, 03 Jun 2026 12:15:30 GMTSAMBA1.0.0successhttps://lbeesleybiostat.r-universe.devhttps://github.com/cran/SAMBAUsingSAMBA.RmdUsingSAMBA.htmlUsing SAMBA2020-02-20 06:50:072026-06-03 12:15:30