subject perieod sequence treatment AUC
1 1 AB A 3981.76
2 1 AB A 5573.62
3 1 AB A 5821.52
4 1 AB A 8558.99
5 1 BA B 3958.93
6 1 BA B 5145.91
7 1 BA B 689.6
8 1 BA B 3812.02
1 2 AB B 4446.05
2 2 AB B 4847.12
3 2 AB B 4274.72
4 2 AB B 4839.49
5 2 BA A 3219.92
6 2 BA A 4587.32
7 2 BA A 4436.7
8 2 BA A 6168.8
像上面的crossover有两组，如果subject(sequence) 为random effect,那么比较两个treatment可以这样：
proc mixed data=test;
class treatment period sequence subject;
model AUC=treatment period sequence
random subject(sequence);
Run;
当然用proc glm也一样可以搞定，但是现在有2个人是对照组，即
9 1 C C 4302.5
10 1 C C 5321.1
9 2 C C 4731.5
10 2 C C 5482.9
现在我要分析比较3个组，比较A,B,C 3组，求出sequence，treatment，period的P值，这个应该当成什么来做？或者data结构要变吗？repeat anova? or crossover anova?
It becomes no crossover dessign no more by adding subject 9/10. Many terms are non-estimable. For example, you cannot distinguish the Period effect and the carry-over effect for Treatment C.
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