Welcoming insight from the experts on this:

Let's say I am running a clinical trial with two treatment A and B and collecting data on some measure at Baseline, Month 3, Month 6, and Month 12. This is balanced design with no missing data.

I want to see if there is a difference of treatment effect at Month 12:

Approaches:

1. Point wise t-test at Month 12. Let's say at 12 month, we have the difference in score and Std Err: 16.04 and 4.3262 respectively with t and p-value: 1.78 and 0.0162.

2. Fit a Linear model with treatment and visit as factor with treatment*visit interaction using proc glm (we know it is wrong because scores are related for the same subject across the visit, hence violation of fundamental assumption, but we are doing it anyway, just to see what happened) - we get Estimate of the difference and Std Err: 16.04 and 8.7784 respectively with t and p-value as 1.827 and 0.0710

3. Fit a sophisticated mixed model with proc mixed and repeated statement and find the right co-variance structure etc. and we get - Estimate of the difference in score and Std Err: 16.04 and 9.0009 with t and p-value: 1.78 and 0.0785 respectively.

These are the questions:

a. Why should I bother to use modeling - sophisticated or not?

b. What's wrong, if any, if we just go with point-wise analysis since this looks much better?

Thanks,

SU