Performing a Two-Sample Test of the Means

In clinical trial analyses you may want to test the means of two independent populations to determine if they are significantly different. For example, let s say that you have the lab test value LDL and you want to know if the change-from-baseline value is significantly different after treatment between active chug and placebo. If you assume the change from baseline for LDL, ldl change, is normally distributed, you can run a two-sample t-test in SAS like this  

This PROC TTEST runs a two-sample f-test to compare the LDL change-from-baseline means for active drug and placebo. ODS OUTPUT is used to send the p-values to a data set called pvalue and to send the test of equal mean variances to a data set called variance test. The final pvalue DATA step checks the test for unequal variances. If the test for unequal variances is significant at the alpha =. 05 level, then the mean variances are unequal and the unequal variances p-value is kept. If the test for unequal variances is insignificant, then the equal variances p-value is kept. The final pvalue data set contains the Probt variable, which is the p-value you want. 

If the two sample populations are not normally distributed, then you can use the nonparametric Wilcoxon rank sum test to compare the population means. The following SAS code compares the ldl change change-from-baseline means for active drug and placebo  

The output data set pvalue contains numerous Wilcoxon test statistics. Assuming that you want the two-sided normal approximation test p-value, the variable in the pvalue data set that you want is called P2 WIL.  

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