مقایسه مدل‌های آمیخته خطی تعمیم‌یافته و مدل‌های خطی تعمیم‌یافته در تعیین عوامل مرتبط با بیماری دیابت نوع2 در استان یزد

. Comparison of Generalized Linear Mixed and Generalized Linear Models in Determining Type II Diabetes Related Factors in Yazd Fallahzadeh H(Ph.D)1,Rahmanian M(Ph.D)2,Emadi M(Ph.D)3,Asadi F(M.Sc)4 1. Professor of Biostatistics, Department of Biostatistics, Shahid Sadoughi University of Medical Sciences, Yazd, Iran. 2. Corresponding Author: Graduate student of Biostatistics, Shahid Sadoughi University of Medical Sciences, Yazd, Iran 3. Professor of Endocrine Diseases And Metabolism, Diabetes Research Center, Yazd, Iran. 4. Associate professor, Department of Statistics, Ferdowsi University of Mashhad, Iran. Abstract Introduction: Diabetes mellitus is a chronic disease, it&rsquo;s prevalence is very high and is increasing recently. In this study, in addition to determining type II diabetes related factors, we compared the generalized linear and generalized linear mixed models. Methods: Data is related to research project to investigate the epidemiological characteristics of diabetes in adults aged 30 years and older in the province of Yazd. In this study, 2,795 people were screened with a blood glucose test for diabetes. We for data analysis by the mixed logistic and ordinary logistic regression used the R software. Results: In this study ,four variables of family history of diabetes, age, body mass index and waist circumference to hip circumference were significant in both models (p-value <.001).Job was a significant variable in the ordinary logistic regression model in level significant .1 but not significant in the mixed logistic regression model. The education, area of housing and gender not significant in neither logistic mixed model nor ordinary logistic model. According to the values of the odds ratio also, we saw quite differences between the two models. Judging from standard error of the coefficients and comparison of the their values in both models seen underestimate in ordinary logistic regression model Conclusion: The use of generalized linear mixed models lead to more accurate results and prevents underestimated standard error of the coefficients.