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Activity Number:
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666
- Bayesian Penalized Regression Models
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Type:
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Contributed
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Date/Time:
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Thursday, August 2, 2018 : 10:30 AM to 12:20 PM
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Sponsor:
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Section on Bayesian Statistical Science
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Abstract #327118
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Title:
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Bayesian Sensitivity Analysis to Unmeasured Confounding for Misclassified Data
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Author(s):
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Joon Jin Song* and Qi Zhou and Yoo-Mi Chin and James Stamey
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Companies:
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Baylor University and Baylor University and Baylor University and Baylor University
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Keywords:
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Unmeasured confounder;
Misclassification;
Observational study;
Intimate partner violence
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Abstract:
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Bayesian sensitivity analysis of unmeasured confounding is proposed for observational data with misclassified responses. The approach corrects bias from error in response and examines possible change in exposure effect estimation if a binary unmeasured confounder exists. We assess the influence of unmeasured confounding on exposure effect estimation through two sensitivity parameters that characterize the associations of the unmeasured confounder with the exposure status and with the response variable. The proposed approach is illustrated in the study of the effect of female employment status on the likelihood of domestic violence. An extensive simulation study is conducted to confirm the efficacy of the proposed approach. The simulation results indicate accounting for misclassification in response and unmeasured confounding would significantly reduce the bias in exposure effect estimation.
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Authors who are presenting talks have a * after their name.
