Power of Simulation Extrapolation in Correction of Covariates Measured with Errors

Abstract

Statistics is one of the most vibrant disciplines where research is inevitable. Most researches in statistics are concerned with the measurement of values of variables in order to make valid conclusions for decision making. Often, researchers do not use the exact values of the variables since it’s difficult to establish the exact value of variables during data collection. This study aimed at using simulation studies to ascertain the power of Simulation Extrapolation (SIMEX) in correcting the bias of coefficients of a logistic regression model with one covariate measured with error. The corrected coefficient values of the model can then be used to predict the exact values of the explanatory variable. The Mean Square Error and the coverage probability were used to test the adequacy of the different model's estimates. The study showed that the use of SIMEX with the quadratic fitting method would give significantly good estimates of the model’s predictors’ coefficients. For further studies, the researcher recommends the study to be done using other models and with multiple covariates measured with errors.