Evaluation of Parameter Estimation Methods for Determination of Covariance Structure in Repeated Data With Equal and Unequal Time Intervals

Abstract

This study was completed with the aim of researching the effect of repeated measurement data with equal and unequal time intervals on the selection of the covariance structure and the parameter estimation methods of maximum likelihood (ML) and restricted maximum likelihood (REML). With this aim, blood glucose values took from the tail vein of 63 healthy rats administered plant extracts at varying rates over 21 days were used. Accordingly, while the most appropriate covariance structure was determined as Factor Analitic (FA(1)) for the data set at equal time intervals, the most appropriate covariance structure was determined as ANTE (1) for the dataset with unequal time intervals. There was no apparent difference difference determined for the performance of the parameter estimation methods of ML and REML for both data sets. In conclusion, while it is possible to try all homogeneous and heterogeneous variance-covariance structures to select the appropriate variance-covariance structure for equal time interval data sets, priority should be given to structures that take account of the time interval for unequal time interval data sets.