The analyses of clinical and epidemiologic studies are often based on some kind of regression analysis, mainly linear regression and logistic models. These analyses are often affected by the fact that one or more of the predictors are measured with error. The error in the predictors is also known to bias the estimates and hypothesis testing results. One of the procedures frequently used to handle such problem in order to reduce the measurement errors is the method of regression calibration for predicting the continuous covariate. The idea is to predict the true value of error-prone predictor from the observed data, then to use the predicted value for the analyses. In this research we develop four calibration procedures, namely probit, comple- mentary log-log, logit, and logistic calibration procedures for corrections of the measurement error and/or the misclassifica- tion error to predict the true values for the misclassification explanatory variables used in generalized linear models. The processes give the predicted true values of a binary explanatory variable using the calibration techniques then use these predicted values to fit the three models such that the probit, the complementary log-log, and the logit models under the binary response. All of which are investigated by considering the mean square error (MSE) in 1,000 simulation studies in each case of the known parameters and conditions. The results show that the proposed working calibration techniques that can perform adequately well are the probit, logistic, and logit calibration procedures. Both the probit calibration procedure and the probit model are superior to the logistic and logit calibrations due to the smallest MSE. Furthermore, the probit model-parameter estimates also improve the effects of the misclassification explanatory variable. Only the complementary log-log model and its calibration technique are appropriate when measurement error is moderate and sample size is high.
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