Selection and Misclassification Bias Adjustment for Logistic Regression Models
Estimate parameters in the disease model approximating the observed da...
Estimate parameters in the disease model given sensitivity as a functi...
Estimate parameters in the disease model using observed data log-likel...
Estimate parameters in the disease model using observed data log-likel...
Synthetic example data for SAMBA adapted from the vignette
Estimate sensitivity
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:10.1101/2019.12.26.19015859>, currently under review.