WiP Seminar: Dr. Ehsan Karim

Mohammad Ehsanul (Ehsan) Karim, PhD, MSc
Scientist, CHÉOS
Assistant Professor, School of Population and Public Health, UBC

Use of machine learning estimators in residual confounding control in pharmacoepidemiologic studies

Studies conducted using observational databases are commonly criticized for the lack of complete information on potential confounders. A massive amount of diagnoses, procedures, and medication codes that are regularly captured in the claims and survey databases are usually not used in a regular epidemiological study. In effect estimation studies, the high-dimensional propensity score (hdPS) algorithm is a framework that enables us to utilize such information as proxies of unobserved information and that has been shown to reduce bias. Some of the machine learning methods that can select or rank variables have been shown to perform as a suitable alternative to this hdPS framework. In this methods talk, using a reproducible dataset as a motivating example, the performance of cross-fit and doubly robust estimators will be highlighted compared to both hdPS and machine learning methods, and practical recommendations will be discussed.

This is a hybrid event, you may attend in person or virtually. Please register and indicate your preference. Click here to register (required): https://ubc.zoom.us/meeting/register/u5ctdOGvrDkuHtMmsQDr32UYUUMfWgWyrymK