EHR

LDER-GE improves the accuracy of estimating the phenotypic variance component explained by genome-wide GE interactions using large-scale biobank association summary statistics.

Faculty: Hongyu Zhao, PhD

Download: LDER-GE package

Platform: R

Reference: academic.oup.com (LDER-GE)

Phenotype Embedding Similarity-based Rare Disease Gene Mapping.

Faculty: Hongyu Zhao, PhD

Download: PERADIGM package

Platform: R

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.

Faculty: Bhramar Mukherjee, PhD

Download: Cran R / SAMBA package

Platform: R

Reference: doi.org (SAMBA)

A Python package for synthetic Electronic Health Records (EHR) data generation benchmarking.

Faculty: Bhramar Mukherjee, PhD

Download: GitHub / synthEHRella package

Platform: Python

Reference: doi.org (synthEHRella)