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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 #321439 View Presentation
Title: Propensity Scoring Methods for Ordinal Treatments
Author(s): Thomas Greene* and Stacia DeSantis and Michael D. Swartz
Companies: The University of Texas Health Science Center at Houston and The University of Texas Health Science Center at Houston and The University of Texas Health Science Center at Houston
Keywords: Propensity Score ; Ordinal Treatment ; Causal Inference ; Observational ; Non-Randomized ; Proportional Odds

Propensity scoring methodology is commonly used when estimating causal effects of treatment in non-randomized observational studies. Historically, observational studies have focused on comparing binary treatments. However, recent developments in theory and application have allowed for accurate causal inference in studies with more than two treatments. These treatments can be multinomial, ordinal, or even continuous. For ordinal dosing schemes, the only technique established to conduct propensity score analysis involves the assumption of proportional odds for the treatment and matching based on the linear predictor given by McCullagh's ordinal logit model. If this assumption is violated, using the linear predictor could provide an inaccurate propensity score and biased causal effect. This analysis conducts a simulation study under various violations of the proportional odds assumption to investigate the implications of propensity score model misspecification for ordinal doses. Potential methods to address this violation are presented and applied to a relevant data set.

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

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