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Abstract Details

Activity Number: 469
Type: Contributed
Date/Time: Wednesday, August 1, 2012 : 8:30 AM to 10:20 AM
Sponsor: Biometrics Section
Abstract - #305382
Title: Causal Mediation Analysis with Latent Mediators
Author(s): Jeffrey M Albert*+
Companies: Case Western Reserve University
Address: School of Medicine WG43, Cleveland, OH, 44106-4945, United States
Keywords: EM algorithm ; factor analysis ; measurement error ; Monte Carlo ; potential outcome ; structural equations model

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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