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STATISTICAL ASSESSMENT OF THE CONTRIBUTION OF A MEDIATOR TO AN EXPOSURE OUTCOME PROCESS

HUANG, BIN
http://rave.ohiolink.edu/etdc/view?acc_num=ucin1005678075

Abstract Details

2001, PhD, University of Cincinnati, Medicine : Environmental Health Sciences.
To achieve detailed understanding of an exposure-outcome association in public health studies, an investigator often needs to account for mediator(s). A mediator is a variable that occurs in a causal pathway from an independent to a dependent variable. The mediational model describes the associations among the exposure(s), mediator(s), and outcome(s). Statistics are needed to determine how much of the exposure-outcome association is due to a mediator. Although mediational models are widely applied in public health, sociological and psychological research, the statistical methods to define and test mediation effects are underdeveloped. The current available methods, path analysis and multi-step regression analyses, have some major limitations including: 1) lack of clear and meaningful definitions of mediation effects; 2) lack of significance testing procedures for the mediation effects; and 3) these methods have not been extended into a generalized form. The present study defined mediation effects, which allows for substantive accounting of the exposure-outcome process that is consistent across a class of generalized mediational models. The newly defined mediation effects have important epidemiological interpretations that are closely related to the concept of attributable risk (AR). Both linear and non-linear models were studied. Much attention has been given to the logistic mediational model due to its important role in the epidemiological studies. Asymptotic variance estimates for the mediation effects were derived using the multivariate delta method. Through Bayesian modeling and Monte Carol techniques, in particular, Markov chain Monte Carlo (MCMC), the posterior distributions for the mediation effects were estimated. Simulation studies, as well as case studies using a nationally representative database, compared the behavior of the asymptotic estimates and the non-informative Bayesian posterior estimates for the mediation effects of the linear and logistic mediational models. It was found that, for the linear mediational model, the asymptotic estimates and the non-informative Bayesian posterior estimates perform equally well; for the logistic mediational model, however, the Bayesian estimates outperform the asymptotic estimates, particularly for a small sample size.
Dr. Paul Succop (Advisor)
135 p.
Health Sciences, General
mediator; structural equation model; GLM; Bayesian Model; MCMC

Recommended Citations

Citations

  • HUANG, B. (2001). STATISTICAL ASSESSMENT OF THE CONTRIBUTION OF A MEDIATOR TO AN EXPOSURE OUTCOME PROCESS [Doctoral dissertation, University of Cincinnati]. OhioLINK Electronic Theses and Dissertations Center. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1005678075

    APA Style (7th edition)

  • HUANG, BIN. STATISTICAL ASSESSMENT OF THE CONTRIBUTION OF A MEDIATOR TO AN EXPOSURE OUTCOME PROCESS. 2001. University of Cincinnati, Doctoral dissertation. OhioLINK Electronic Theses and Dissertations Center, http://rave.ohiolink.edu/etdc/view?acc_num=ucin1005678075.

    MLA Style (8th edition)

  • HUANG, BIN. "STATISTICAL ASSESSMENT OF THE CONTRIBUTION OF A MEDIATOR TO AN EXPOSURE OUTCOME PROCESS." Doctoral dissertation, University of Cincinnati, 2001. http://rave.ohiolink.edu/etdc/view?acc_num=ucin1005678075

    Chicago Manual of Style (17th edition)

ucin1005678075
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© 2001, all rights reserved.
This open access ETD is published by University of Cincinnati and OhioLINK.