Data Analysis Strategies for Repeated Measures Data in Clinical Trials

Purpose Clinical trials have received much attention in recent years,but data analyses have concentrated on a single longitudinal variable. This paper compared different statistical methods in SAS pro-cedures for repeated measures in randomized clinical trial and helps you understand their methodologies and advantages. Methods Using an example to illustrate many of the key similarities and difference to various statistical models. Results There are two important data analysis strategies for comparing the difference effect between the two treatment groups. With respect to the two data analysis strategies, statistical methods are classified as traditional methods and statistical methods for repeated measurements'. (1) Traditional statistics include independent t test,analysis of variance and analysis of covariance; (2) Statistical method for repeated measurement includes Generalized Linear Modei and Generalized Linear Mixed Model. A characteri-stic of repeated measurement data in clinical trials is that observation on the same subject are correlated, and the closer the time point,the high correlation. Hence,statistical analysis must address the issue of covariation between measurements on the same subject. The Generalized Linear Modei is not a statistical technique for analyzing correlated outcome data,but the MIXED procedure of the SAS system provides a rich selection. Conclusions Continuous data in clinical trial always involves data consist of multiple measurements on individuals, within individuals data are usually positive correlated. Both strategies offers analyses that account for between group differences,but most situations, the Generalized Linear Mixed Modei is the most preferable for repeated measurement data in clinical trial. It covers both random effects and serial correlation and allows for missing data.