Repeated measure analysis is used when all members of a random sample are measured under a number of different conditions. As the sample is exposed to each condition in turn, the measurement of the dependent variable is repeated. Using a standard ANOVA in this case is not appropriate because it fails to model the correlation between the repeated measures. One should be clear about the difference between a repeated measures design and a simple multivariate design. For both, sample members are measured on several occasions, or trials, but in the repeated measures design, each trial represents the measurement of the same characteristic under a different condition.

In **SAS PROC GLM** is used to carry out repeated measure analysis.

**Syntax**

The basic syntax for PROC GLM in SAS is:

PROC GLM DATA=dataset;

CLASS variable;

MODEL variables = group / NOUNI ;

REPEATED TRIAL n;

Following is the description of the parameters used :

**dataset**is the name of the dataset.**CLASS**gives the variables the variable used as classification variable.**MODEL**defines the model to be fit using certain variables form the dataset.**REPEATED**defines the number of repeated measures of each group to test the hypothesis.

**Example**

Consider the example below in which we have two groups of people subjected to test of effect of a drug. The reaction time of each person is recorded for each of the four drug types tested. Here 5 trials are done for each group of people to see the strength of correlation between the effect of the four drug types.

DATA temp;

INPUT person group $ r1 r2 r3 r4;

CARDS;

1 A 2 1 6 5

2 A 5 4 11 9

3 A 6 14 12 10

4 A 2 4 5 8

5 A 0 5 10 9

6 B 9 11 16 13

7 B 12 4 13 14

8 B 15 9 13 8

9 B 6 8 12 5

10 B 5 7 11 9

;

RUN;

PROC PRINT DATA=temp ;

RUN;

PROC GLM DATA=temp;

CLASS group;

MODEL r1-r4 = group / NOUNI ;

REPEATED trial 5;

RUN;

When the above code is executed, we get the following result: