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  "Title": "Selection and Misclassification Bias Adjustment for Logistic\nRegression Models",
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  "Description": "Health research using data from electronic health records\n(EHR) has gained popularity, but misclassification of\nEHR-derived disease status and lack of representativeness of\nthe study sample can result in substantial bias in effect\nestimates and can impact power and type I error for association\ntests. Here, the assumed target of inference is the\nrelationship between binary disease status and predictors\nmodeled using a logistic regression model. 'SAMBA' implements\nseveral methods for obtaining bias-corrected point estimates\nalong with valid standard errors as proposed in Beesley and\nMukherjee (2020) <doi:10.1111/biom.13400>, Biometrics.",
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