# How to analyze this pre-post control-intervention data set?

I have control and intervention groups (N = 50 and 75, respectively) for whom 15 tests (all having quantitative results) were done at baseline and at 3 months.

It is not a randomized study and the baseline values in control group ARE different from those of intervention group.

My precise question is: "Does the intervention causes a significant change in values of these tests"

What is the best method for this? Should I perform unpaired t-tests on baseline-follow up differences in 2 groups or should I use anova/regression?

Also, how do I correct for multiple tests being done here?

Edit: Data is in following format:

ID_NO   GRP prepost test1   test2
1   active  pre     10      0.074
2   control pre     11      0.053
1   active  post    10.8    0.042
2   control post    10.5    0.039
....


For anova, following can be used (in R):

summary(aov(testresult ~ GRP * prepost + Error(ID_NO/prepost), data=mydata))


Following can be used for regression:

summary(lm(testresult_difference ~ testresult_basal + GRP , data=mydata))


Unpaired t-tests can be used for testing difference (change) in controls vs change in intervention group. Similarly unpaired t-test can be used for comparing post/pre ratio in controls vs that intervention group.

Which method should I use?

• How many tests were given? If you have a lot of tests you might need to worry about inflated type I errors/p-values, so you would want to consider something like a Bonferroni correction. – robin.datadrivers Mar 7 '15 at 19:17
• @robin.datadrivers : About 15 tests have been performed. So I think correction may be needed. How do I integrate that with analysis? I have added this to my question above. – rnso Mar 8 '15 at 2:47