# Best way to compare pre vs post levels in treatment vs control groups?

In a study, drug was given to tt (treatment) group and not to cc (control) group and blood levels of (say) cholesterol were tested before and after in each subject.

I have following data:

  id grp preLevel postLevel diff percentDiff prePostRatio
1  1  tt      140       110   30        0.21         1.27
2  2  tt      150       120   30        0.20         1.25
3  3  cc      135       125   10        0.07         1.08
4  4  cc      155       145   10        0.06         1.07
...


I can perform t.test or wilcox.test and compare the two groups. But what is the best parameter to analyze: absolute difference, percent difference or pre/post ratio? Or it does not matter and they will all produce same result?

Edit:

For above data should I use t.test/ wilcox.test or repeated measures anova as described on this page: http://ww2.coastal.edu/kingw/statistics/R-tutorials/repeated.html using following code with rearranged data:

id  grp  pre_or_post level
1   tt   pre         140
1   tt   post        110
2   tt   pre         150
2   tt   post        120
3   cc   pre         135
3   cc   post        125
...

aov(level ~ grp * pre_or_post + Error(id/pre_or_post), data=mydata)


Also, how will the method of analysis differ if it is a parallel group study with different subjects in treatment and controls groups or it is a cross-over study with same subjects being in treatment and control groups at different times?

• Related thread: Best practice when analysing pre-post treatment-control designs. – chl Oct 26 '14 at 16:44
• Thanks for the link. I am surprised to see that experts do not agree on analysis of this very common situation (at least in biomedical area it is very common). – rnso Oct 26 '14 at 17:29