EHRmuse - Multi-Cohort Selection Bias Correction using IPW and AIPW
Methods
Comprehensive toolkit for addressing selection bias in
binary disease models across diverse non-probability samples,
each with unique selection mechanisms. It utilizes Inverse
Probability Weighting (IPW) and Augmented Inverse Probability
Weighting (AIPW) methods to reduce selection bias effectively
in multiple non-probability cohorts by integrating data from
either individual-level or summary-level external sources. The
package also provides a variety of variance estimation
techniques. Please refer to Kundu et al.
<doi:10.48550/arXiv.2412.00228>.
