Semiparametric Bayesian analysis of gene-environment interactions with error in measurement of environmental covariates and missing genetic data

Lobach, I; Mallick, B; Carroll, RJ

Semiparametric Bayesian analysis of gene-environment interactions with error in measurement of environmental covariates and missing genetic data

Lobach, I Mallick, B Carroll, RJ

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Publication Type:: Journal Article
Citation:: Statistics and its Interface, 2011, 4 (3), pp. 305 - 316
Issue Date:: 2011-12-01

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Field	Value	Language
dc.contributor.author	Lobach, I	en_US
dc.contributor.author	Mallick, B	en_US
dc.contributor.author	Carroll, RJ	en_US
dc.date.issued	2011-12-01	en_US
dc.identifier.citation	Statistics and its Interface, 2011, 4 (3), pp. 305 - 316	en_US
dc.identifier.issn	1938-7989	en_US
dc.identifier.uri	http://hdl.handle.net/10453/117669
dc.description.abstract	Case-control studies are widely used to detect geneenvironment interactions in the etiology of complex diseases. Many variables that are of interest to biomedical researchers are difficult to measure on an individual level, e.g. nutrient intake, cigarette smoking exposure, long-term toxic exposure. Measurement error causes bias in parameter estimates, thus masking key features of data and leading to loss of power and spurious/masked associations. We develop a Bayesian methodology for analysis of case-control studies for the case when measurement error is present in an environmental covariate and the genetic variable has missing data. This approach offers several advantages. It allows prior information to enter the model to make estimation and inference more precise. The environmental covariates measured exactly are modeled completely nonparametrically. Further, information about the probability of disease can be incorporated in the estimation procedure to improve quality of parameter estimates, what cannot be done in conventional case-control studies. A unique feature of the procedure under investigation is that the analysis is based on a pseudo-likelihood function therefore conventional Bayesian techniques may not be technically correct. We propose an approach using Markov Chain Monte Carlo sampling as well as a computationally simple method based on an asymptotic posterior distribution. Simulation experiments demonstrated that our method produced parameter estimates that are nearly unbiased even for small sample sizes. An application of our method is illustrated using a population-based case-control study of the association between calcium intake with the risk of colorectal adenoma development.	en_US
dc.relation.ispartof	Statistics and its Interface	en_US
dc.title	Semiparametric Bayesian analysis of gene-environment interactions with error in measurement of environmental covariates and missing genetic data	en_US
dc.type	Journal Article
utslib.citation.volume	3	en_US
utslib.citation.volume	4	en_US
utslib.for	010401 Applied Statistics	en_US
utslib.for	0104 Statistics	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Science
pubs.organisational-group	/University of Technology Sydney/Faculty of Science/School of Mathematical and Physical Sciences
utslib.copyright.status	open_access
pubs.issue	3	en_US
pubs.publication-status	Published	en_US
pubs.volume	4	en_US

Abstract:

Case-control studies are widely used to detect geneenvironment interactions in the etiology of complex diseases. Many variables that are of interest to biomedical researchers are difficult to measure on an individual level, e.g. nutrient intake, cigarette smoking exposure, long-term toxic exposure. Measurement error causes bias in parameter estimates, thus masking key features of data and leading to loss of power and spurious/masked associations. We develop a Bayesian methodology for analysis of case-control studies for the case when measurement error is present in an environmental covariate and the genetic variable has missing data. This approach offers several advantages. It allows prior information to enter the model to make estimation and inference more precise. The environmental covariates measured exactly are modeled completely nonparametrically. Further, information about the probability of disease can be incorporated in the estimation procedure to improve quality of parameter estimates, what cannot be done in conventional case-control studies. A unique feature of the procedure under investigation is that the analysis is based on a pseudo-likelihood function therefore conventional Bayesian techniques may not be technically correct. We propose an approach using Markov Chain Monte Carlo sampling as well as a computationally simple method based on an asymptotic posterior distribution. Simulation experiments demonstrated that our method produced parameter estimates that are nearly unbiased even for small sample sizes. An application of our method is illustrated using a population-based case-control study of the association between calcium intake with the risk of colorectal adenoma development.

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http://hdl.handle.net/10453/117669