structural multilevel modelling in combination with misclassification andmeasurement error in covariates

Many multilevel epidemiological frameworks are impressed by measurementerror or misclassification in covariates. it has been well established that errors in covariatesdegrade the quality of statistical inference, lead to biased estimates and a lossof power to detect associations between covariates and the outcome variable. in thispaper, sponsoring various features of discrete and continuous error-prone variables, weconsider multilevel settings with misclassified and mismeasured covariates. contemplatingstructural likelihood-based strategy, we develop an estimation and inferencemethod that accomodates both sources of errors simultaneously using the multivariategauss-hermite quadrature technique to approximate the likelihood function numerically.simulation results show that the proposed method performs well when correctingfor covariate measurement error and non-differential misclassification in terms of bias,empirical standard error, root of mean squared error as well as the coverage rate.