Abstract Details
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Activity Number:
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32
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Type:
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Contributed
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Date/Time:
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Sunday, August 4, 2013 : 2:00 PM to 3:50 PM
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Sponsor:
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Biopharmaceutical Section
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| Abstract - #309429 |
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Title:
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Large-Scale Penalized Regression for Propensity Score Estimation in Observational Health Care Data
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Author(s):
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Ivan Zorych*+ and Patrick Ryan and David Madigan
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Companies:
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Columbia University and Jenssen Research and Development LLC and Columbia University
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Keywords:
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propensity ;
balance ;
observational data ;
regression ;
penalized ;
OMOP
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Abstract:
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Propensity score adjustment is a useful tool to deal with confounding in observational data. There are different views on building the propensity score models. Here we compare two approaches. One is based on an empirical covariate selection procedure and another one relies on a Bayesian logistic regression and utilizes all covariates, sometimes many thousands, that are available from an observational database. We investigate balancing properties of both procedures using a set of drug-outcome pairs on several observational databases provided by the Observational Medical Outcome Partnership (omop.fnih.org).
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