Assessing large sample bias in misspecified model scenarios with reference to exposure model misspecification in errors-in-variable regression: A new computational approach

In this paper, we develop a numerical method for evaluating the large sample bias in estimated regression coefficients arising due to exposure model misspecification while adjusting for measurement errors in errors-in-variable regression. The application of the proposed method has been demonstrated in the case of a logistic errors-in-variable regression model. The method is based on the combination of Monte-Carlo, numerical and, in some special cases, analytic integration techniques. The proposed method facilitates the investigation of the limiting bias in the estimated regression parameters based on a single data set rather than on repeated data sets as required by the conventional repeated sample method. Simulation studies demonstrate that the proposed method provides very similar estimates of bias in the estimated regression parameters under exposure model misspecification in logistic errors-in-variable regression with a higher degree of precision as compared to the conventional repeated sample method.