# Can anyone help me on my research project

#### Jeffrey Lau

##### New Member
I am newbie in biostatistics but I am now going to analyze some data, sample size is 25.
It is a study without control, all are the interventional group.
I do measurements at three different time points (t0,t1,t2) with pre-intervention and post-intervention at each time point.
Therefore, there are two sets of data.
The first one is the pre/post intervention data. I would like to compare the pre/post value to show that in fact the intervention has nearly "no" effect (non-significant change of value). It is because I get these data from 3 different time points, there will be 25 * 3 = 75 set of data. Am I going to use paired t-test?

For the second one, it is more complicated. I want to compare the change from pre-value from 3 different time point to see its increment/improvement is significant. However, thetime points (t1,t2) has some variation (as it is not feasible to collect data at the same time point for every cases). What can I do?

Thank you very much!!!!!!

#### Karabiner

##### TS Contributor
You cannot perform t-test, because it would treat the 3 measurements of the 25 subsjects as if there were 75 independent subjects. You could use repeated-measures analysis of variance, with 2 within-subject factors, "time point" (3 levels), and "time of measurement" (pre vs. post intervention).

Mind that a non-significant result for "time of measurement" would not proof that there is no change between pre and post. There may be an effect, but the test failed to detect it, for example due to too low statistical power (absence of evidence for an effect is not evidence of absence). "Proof" of absence would require an -> equivalence test approach, which is not easy to carry out and probably needs a much larger sample size

In the second case, a simple approach would be 2 separate repeated-measures ANOVAs, each with the repeated-measures factor "time point" (t0/t1 and t0/2, respectively), and in each analysis you add time between t0 and t1 (or t2, respectively) as a interval scaled predictor.

With kind regards

Karabiner

#### Jeffrey Lau

##### New Member
You cannot perform t-test, because it would treat the 3 measurements of the 25 subsjects as if there were 75 independent subjects. You could use repeated-measures analysis of variance, with 2 within-subject factors, "time point" (3 levels), and "time of measurement" (pre vs. post intervention).

Mind that a non-significant result for "time of measurement" would not proof that there is no change between pre and post. There may be an effect, but the test failed to detect it, for example due to too low statistical power (absence of evidence for an effect is not evidence of absence). "Proof" of absence would require an -> equivalence test approach, which is not easy to carry out and probably needs a much larger sample size

In the second case, a simple approach would be 2 separate repeated-measures ANOVAs, each with the repeated-measures factor "time point" (t0/t1 and t0/2, respectively), and in each analysis you add time between t0 and t1 (or t2, respectively) as a interval scaled predictor.

With kind regards

Karabiner