TY - JOUR JF - srtc-jsri JO - JSRI VL - 16 IS - 1 PY - 2019 Y1 - 2019/9/01 TI - Likelihood Inference in the Random Effects Logistic Regression Model with ‎Response Misclassification and Covariate Subject to Measurement Error‎ TT - استنباط درستنمایی در مدل رگرسیونی  لوژستیکی اثرهای تصادفی  با  بدرده‌بندی پاسخ و متغیر کمکی در معرض خطای اندازه‌گیری N2 - ‎Generalized linear mixed models (GLMMs) are common methods for the analysis of clustered data‎. ‎In many longitudinal and hierarchical epidemiological frameworks‎, ‎accurate measurements of variables are invalid or expensive to be obtained and there might be situations that both the response and covariate variables are likely to be mismeasured‎. ‎Insensitivity of errors in either covariate or response variable is‎, ‎not always plausible‎. ‎With nonlinear regression models for the outcome process‎, ‎classification errors for binary responses and measurement error in covariates basically needs to be accounted for in order to make conclusive inferences‎. ‎In this article‎, ‎we provide an approach to simultaneously adjust for non-differential misclassification in the correlated binary response and classical measurement error in the covariates‎, ‎using the multivariate Gauss-Hermite quadrature technique for the approximation of the likelihood function‎. ‎Simulation studies are then conducted to inform the effects of correcting for measurement error and misclassification on the estimation of regression parameters‎. ‎The application of the multivariate Gauss-Hermite quadrature method in the conjunction of measurement error and misclassification problems is further highlighted with real-world data based on a multilevel study of contraceptive methods used by women in Bangladesh‎. SP - 255 EP - 286 AU - Ahangari, Maryam AU - golalizadeh, Mousa AU - Rezaei Ghahroodi, Zahra AD - KW - Measurement Error KW - Binary Response KW - Multivariate Gauss-Hermite Quadrature KW - Random Effects Logistic Regression Model KW - Misclassification.‎ UR - http://jsri.srtc.ac.ir/article-1-359-en.html DO - 10.52547/jsri.16.1.255 ER -