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Abstract Details
Activity Number:
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469
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Type:
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Contributed
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Date/Time:
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Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
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Sponsor:
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Biometrics Section
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Abstract - #305382 |
Title:
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Causal Mediation Analysis with Latent Mediators
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Author(s):
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Jeffrey M Albert*+
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Companies:
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Case Western Reserve University
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Address:
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School of Medicine WG43, Cleveland, OH, 44106-4945, United States
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Keywords:
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EM algorithm ;
factor analysis ;
measurement error ;
Monte Carlo ;
potential outcome ;
structural equations model
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Abstract:
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Health researchers have been increasingly interested in assessing the direct effect of a treatment or exposure on a response variable, as well as its indirect (or mediation) effect through causal intermediate variables (or mediators). Such a causal mediation analysis is intended to elucidate the mechanisms through which the exposure acts on a response. Standard approaches to mediation analysis assume one, or a small number of, observed mediators. However, models with unobserved (or latent) mediators may be of interest. Latent mediators may be of several types including, a mixing variable (to allow heterogeneity in response), an underlying variable measured with error, and a factor (measured by a set of observed responses). Using a potential outcomes framework, we develop inference methods for these various types of latent variables based on a Monte Carlo EM algorithm approach. The methods are applied to data from a study of the effect of childhood social environmental factors on dental caries in adolescence. A simulation study is conducted to further study properties of the proposed methods.
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Authors who are presenting talks have a * after their name.
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