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Abstract Details
Activity Number:
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463
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Type:
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Contributed
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Date/Time:
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Wednesday, August 3, 2011 : 8:30 AM to 10:20 AM
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Sponsor:
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Section on Statistics in Epidemiology
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Abstract - #300723 |
Title:
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Measurement Error and Misclassification in Covariates: Impacts on the Main and Interaction Effects and the Bayesian Adjustment
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Author(s):
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Shahadut Hossain*+
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Companies:
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United Arab Emirates University
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Address:
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Department of Statistics, Al Ain, , United Arab Emirates
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Keywords:
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Measurement Error ;
Misclassification ;
Errors-in-variables ;
Exposure Model
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Abstract:
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Measurement error in quantitative factors or misclassifications in categorical factors or both are unavoidable in most epidemiological studies. If ignored, such measurement errors and/or misclassifications can seriously distort the estimated effects of the risk factors on the disease outcome of interest. Thus, adjustments for measurement errors and/or misclassifications are necessary to have accurate estimates of the effects of the suspected risk factors, and their interactions on the disease risks. In this talk, I will discuss a general Bayesian flexible parametric framework for eliminating the deleterious impacts of measurement errors and/or misclassification while estimating the main and the interaction effects in logistic regression model. The application of the proposed flexible method will be illustrated with reference to gene-environment interaction studies. Being a parametric framework, the method is unified and thus can be applied to any member of the generalized linear models, and to the non-linear regression models as well.
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Authors who are presenting talks have a * after their name.
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