American Statistical Association
New York City
Metropolitan Area Chapter

Levin Lecture Series: Fall 2019 Colloquium Seminars
Department of Biostatistics
Mailman School of Public Health
Columbia University



A PROPENSITY SCORING FRAMEWORK FOR MULTIPLE AND
CONTINUOUS TREATMENTS AND THEIR APPLICABILITY TO EHR AND GENOMIC DATA

by

Dr. Stacia DeSantis
Professor, Department of Biostatistics and Data Science
University of Texas

Host: Cody Chiuzan


Abstract

Large observational studies such as those derived from electronic health records (EHRs) and whole genome sequencing are becoming readily available to the public for data analysis. However, utilizing these complex data types to make unbiased inference about the effects of treatments or genetic loci, on clinical outcomes or phenotypes, remains challenging. We present a unified causal inference propensity score framework for multiple treatments (i.e., ordinal, categorical, and continuous) that can be applied regardless of the chosen propensity model, present its application to EHR data, and discuss its potential scalability to genetic causal inference with the goal of identifying treatments, behaviors, and genetic factors that influence health outcomes.


Date: Thursday, September 26, 2019
Time: 11:30 A.M. - 12:30 P.M.
Location: Mailman School of Public Health
Department of Biostatistics
722 West 168th Street
AR Building
8th Floor Auditorium
New York, New York

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