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Abstract Details
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Activity Number:
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8
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Type:
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Invited
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Date/Time:
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Sunday, July 31, 2011 : 2:00 PM to 3:50 PM
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Sponsor:
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Section on Statistical Consulting
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| Abstract - #300133 |
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Title:
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Bayesian Joint Modeling of IRT and Mediation Pathways
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Author(s):
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Lili Ding and Bin Huang*+
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Companies:
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Cincinnati Children's Hospital Medical Center and Cincinnati Children's Hospital Medical Center
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Address:
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Division of Biostatistics and Epidemiology, Cincinnati, OH, 45229,
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Keywords:
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IRT ;
Bayesian ;
Patient Report Outcome ;
Mediator ;
Direct and Indirect Effect ;
Joint Modeling
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Abstract:
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Conventional Item Response Theory (IRT) assumes the latent abilities are exchangeable with all individuals. However, when applied to health related studies involving patient report outcome (PRO), the latent clinical abilities often vary from one patient to another, and often are a mixture of different clusters. Explanatory item response models (De Boeck and Wilson) and their generalization proposed modeling patient characteristics. In this talk, we consider mixture modeling of latent clinical abilities. Specifically, we examine its impact on assessing direct and indirect effect, when the mediator variable is a PRO. We illustrate the advantage of Bayesian joint modeling using simulation and case studies, compared with classic IRT and explanatory IRT.
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The address information is for the authors that have a + after their name.
Authors who are presenting talks have a * after their name.
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