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Activity Number:
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360
- Contributed Poster Presentations: Section on Bayesian Statistical Science
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Type:
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Contributed
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Date/Time:
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Wednesday, August 5, 2020 : 10:00 AM to 2:00 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #312206
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Title:
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Bayesian Propensity Score Analysis for Multiple Correlated Binary Outcomes
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Author(s):
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Shannon Marie Ciccarello* and Joon Jin Song
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Companies:
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Baylor University and Baylor University
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Keywords:
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Bayesian;
propensity score analysis;
multiple outcomes;
binary data
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Abstract:
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Propensity scores are used to minimize unmeasured confounding when analyzing observational data. Bayesian approaches allow for the simultaneous modeling of propensity score and treatment effects, producing more appropriate estimates of variability than their frequentist counterparts. It is increasingly the case that observational studies are used to investigate the effect of a single treatment on multiple outcomes, but outcomes are often modeled independently. We propose new Bayesian propensity score methods for jointly estimating treatment effect for multiple correlated binary outcomes. The performance of these new methods is explored via simulation study for different levels of induced correlation between outcomes and via application to estimating the effects of female employment on domestic violence.
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Authors who are presenting talks have a * after their name.
