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Bayesian methods in case-control stu...

Zhang, Li.

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Bayesian methods in case-control studies with applications in genetic epidemiology.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Bayesian methods in case-control studies with applications in genetic epidemiology./
作者:	Zhang, Li.
面頁冊數:	152 p.
附註:	Source: Dissertation Abstracts International, Volume: 67-08, Section: B, page: 4509.
Contained By:	Dissertation Abstracts International67-08B.
標題:	Biology, Biostatistics. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=3228879
ISBN:	9780542808357

Bayesian methods in case-control studies with applications in genetic epidemiology.
Zhang, Li.

Bayesian methods in case-control studies with applications in genetic epidemiology. - 152 p.

Source: Dissertation Abstracts International, Volume: 67-08, Section: B, page: 4509.

Thesis (Ph.D.)--University of Florida, 2006.

The fundamental idea behind case-control studies is to compare selected persons having a disease (the cases) with those not having the disease (the controls) by assessing to what extent they have been exposed to the disease's possible risk factors. The natural likelihood to use for a case-control study is a "retrospective" likelihood, i.e. a likelihood based on the probability of exposure given disease status. I prove the equivalence of posterior inference for the log odds ratios parameters based on prospective and retrospective likelihoods in stratified case-control studies in which some of the exposure variables could be missing completely at random.

ISBN: 9780542808357Subjects--Topical Terms:

1018416
Biology, Biostatistics.

Bayesian methods in case-control studies with applications in genetic epidemiology.
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500 $a Source: Dissertation Abstracts International, Volume: 67-08, Section: B, page: 4509.
500 $a Advisers: Malay Ghosh; Bhramar Mukherjee.
502 $a Thesis (Ph.D.)--University of Florida, 2006.
520 $a The fundamental idea behind case-control studies is to compare selected persons having a disease (the cases) with those not having the disease (the controls) by assessing to what extent they have been exposed to the disease's possible risk factors. The natural likelihood to use for a case-control study is a "retrospective" likelihood, i.e. a likelihood based on the probability of exposure given disease status. I prove the equivalence of posterior inference for the log odds ratios parameters based on prospective and retrospective likelihoods in stratified case-control studies in which some of the exposure variables could be missing completely at random.
520 $a My dissertation also addresses three problems in the domain of genetic epidemiology to explore a variety of disease-gene association and gene-environment interaction.
520 $a First, I consider the problem of detecting association between a disease and a candidate gene in the presence of population admixture. I propose a two-stage parametric Bayesian approach implemented via Markov chain Monte Carlo (MCMC) numerical integration technique, which first estimates the posterior probability of different unknown population substructures and then integrates this information into a disease-gene association model through the technique of Bayesian model averaging. Thus, the uncertainty in estimating the population substructure is taken into account while providing credible intervals for parameters in the disease-gene association model.
520 $a Second, I present a Bayesian semiparametric approach to model the effect of stratification variables under the assumption of gene-environment independence in the control population conditional on some other covariates to study the gene-environment interaction. I take account of stratum heterogeneity in the exposure distribution by adopting the Dirichlet process mixture (DPM) of normal prior to the distribution of the environmental exposure and a flexible model for the distribution of the genetic factor. I illustrate the methods by applying them to an Israeli ovarian cancer study to investigate the effect of BRCA1/2 mutations, oral contraceptive use and parity in the development of ovarian cancer.
520 $a Third, I consider analysis of unmatched case-control studies in which binary exposures are potentially misclassified. I describe a relative simple approach to adjust the estimation of the parameters of interest in gene-environment association studies in the presence of misclassification and by exploiting the G-E independence assumption. Concluding remarks and directions for future work are included in the end.
590 $a School code: 0070.
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650 4 $a Health Sciences, Epidemiology. $3 1019544
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790 1 0 $a Mukherjee, Bhramar, $e advisor
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