Objective: It is not always possible to make measurements at the same time points from all patients in the field of health. Transactions may begin at different times, even if they are performed at equal intervals in each individual. Occasionally, measurements are made at different time intervals as a requirement of the process. In order to obtain accurate results from the analysis of such repeated measurement data, an accurate model of covariance is needed. In repeated data analysis, determining the most appropriate variance covariance structure for data is essential for the correct solution. The aim of this study is to test the effects of repeated measurement data and determine the differences that may occur when the variance covariance model is selected incorrectly and to test the final decision. Material and Methods: In this study, the data on the variance covariance structure and SAS package program coding system were analyzed by using different variance covariance structures and the effect on the analysis of the selection of incorrect variance covariance structure was investigated. Findings: In the study, the most appropriate variance covariance structure for the analysis of repeated measured data was determined as Unstructured (UN) variance covariance model with minimum BIC value. When the data were analyzed by Univariate (UN) varicence covariance model, the drug effect was found to be significant. When analyzed by GLM, the drug effect was insignificant. Conclusion: The use of the GLM solution technique in the analysis of incomplete data may result in erroneous decisions. For this reason, it is recommended to use MIXED solution technique for repeated measurements. Otherwise, unrealistic decisions are made.