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:: Volume 3, Number 1 (Spring & Summer 2015 2015) ::
Hakim Jorjani J 2015, 3(1): 98-113 Back to browse issues page
Correction the Bias of Odds Ratio resulting from the Misclassification of Exposures in the Study of Environmental Risk Factors of Lung Cancer using Bayesian Methods
Alireza Abadi, Bagher pahlavanzade , Keramat Nourijelyani, Seyed Mostafa Hosseini
PhD student in Biostatistics Department of Biostatistics, School of Allied Medical Sciences, Shahid Beheshti University of Medical Sciences, Tehran, Iran , db.pahlavan@gmail.com
Abstract:   (1771 Views)

Background & Objective: Inability to measure exact exposure in epidemiological studies is a common problem in many studies, especially cross-sectional studies. Depending on the extent of misclassification, results may be affected. Existing methods for solving this problem require a lot of time and money and it is not practical for some of the exposures. Recently, new methods have been proposed in 1:1 matched case–control studies that have solved these problems to some extent. In the present study we have aimed to extend the existing Bayesian method to adjust for misclassification in matched case–control Studies with 1:2 matching.

Methods: Here, the standard Dirichlet prior distribution for a multinomial model was extended to allow the data of exposure–disease (OR) parameter to be imported into the model excluding other parameters. Information that exist in literature about association between exposure and disease were used as prior information about OR. In order to correct the misclassification Sensitivity Analysis was accomplished and the results were obtained under three Bayesian Methods.

Results: The results of naïve Bayesian model were similar to the classic model. The second Bayesian model by employing prior information about the OR, was heavily affected by these information.

The third proposed model provides maximum bias adjustment for the risk of heavy metals, smoking and drug abuse. This model showed that heavy metals are not an important risk factor although raw model (logistic regression Classic) detected this exposure as an influencing factor on the incidence of lung cancer. Sensitivity analysis showed that third model is robust regarding to different levels of Sensitivity and Specificity.

Conclusion: The present study showed that although in most of exposures the results of the second and third model were similar but the proposed model would be able to correct the misclassification to some extent.

Keywords: Misclassification, Bayesian Methods, Sensitivity Analysis, Lung Cancer
Full-Text [PDF 818 kb]   (467 Downloads)    
Type of Study: Original paper | Subject: General
Received: 2015/10/8 | Accepted: 2015/10/8 | Published: 2015/10/8
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Abadi A, pahlavanzade B, Nourijelyani K, Hosseini S M. Correction the Bias of Odds Ratio resulting from the Misclassification of Exposures in the Study of Environmental Risk Factors of Lung Cancer using Bayesian Methods. Hakim Jorjani J. 2015; 3 (1) :98-113
URL: http://goums.ac.ir/jorjanijournal/article-1-341-en.html
Volume 3, Number 1 (Spring & Summer 2015 2015) Back to browse issues page
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