Jorjani Biomedicine Journal

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.

Background and Objective: Discriminant analysis and logistic regression are classical methods for classifying data in several studies. However, these models do not lead in valid results due to not meeting all necessary assumptions. The purpose of this study was to classify the number of Children Ever Born (CEB) using decision tree model in order to present an efficient method to classify demographic data.

Methods: In the present study, CART tree model with Gini splitting rule was fitted to classify the number of CEB in fertility behavior of at least once married 15-49 year-old women, in Semnan-2012. 405 women aged 15-49 years old comprised the survey sample.

Results: Women in first and second birth cohorts who had married at an early age had 3 CEB while women who had married at an older age had 2 CEB. Women in third birth cohort who had married at an early age and were employed, had 2 CEB while unemployed women in this cohort whose type of marriages were familial and non-familial had 0 and 1 CEB respectively. Women in the third birth cohort who were married in older age had 1 CEB.

Conclusion: Among important advantages of CART model are the simplicity in interpretation, using distribution-free measures, considering missing data and outliers for construction trees which has increased the usage of this method. Therefore, this method is a suitable way for classifying demographic data in comparison to other classical modeling methods in the conditions that necessary assumptions are not met.