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Activity Number: 497
Type: Contributed
Date/Time: Wednesday, August 3, 2016 : 8:30 AM to 10:20 AM
Sponsor: Section on Statistics in Epidemiology
Abstract #321398 View Presentation
Title: Using Propensity Scores to Infer Causal Effects on Heart Health from Chemotherapy Treatment of Breast Cancer Patients
Author(s): John Craycroft* and Maiying Kong and Carrie Lenneman
Companies: University of Louisville and University of Louisville and University of Louisville
Keywords: propensity scores ; observational study ; causal inference ; average treatment effect ; breast cancer ; cardiotoxicity

Arguably, the practice of statistics involves becoming skilled at gleaning deeper insights from a dataset or research study than may be available from simplistic analysis. One technique admirably demonstrating this aspect of the beauty of statistics is the use of propensity scores to enable causal inference from observational data. Propensity scores, defined as the probability of receiving a particular treatment given a set of covariates, are balancing scores, i.e., the distribution of the covariates given the propensity score is balanced. Consequently, we can estimate average treatment effects from observational data, going well beyond the "correlation analysis" that a naïve summary may rest with. In this presentation, we describe the results of extensive simulations comparing three propensity score methods (stratification, inverse probability of treatment weighting, and doubly robust estimation) and explain how each method may affect the estimation of average treatment effect. Results are applied to a study of the effect of breast cancer chemotherapy treatment regimen on subsequent development of cardiotoxicity.

Authors who are presenting talks have a * after their name.

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