Hello,
I have equally-spaced data (8 points in time) and four treatment groups (control, low, mid, and high). The number of subjects in each treatment group is identical, with no missing data points.
I have three key response variables on which I would like to perform statistical testing.
My thought was to perform a one-way ANOVA for each time point (8 tests per response variable). If any ANOVAs come back with a significant p-value, I would then perform Dunnett's tests to compare the 3 treatments (low, mid, high) against the control for a given time point.
I am a bit confused when it comes to calculating the worst-case Type 1 error for this scenario. Wondering if my line of thought is correct:
Worst-case: all response variable one-way ANOVAs are significant at each time point...
so... 3 (response variables) x 8 (time points) + 3 (response variables) x 3 (Dunnett's tests) x 8 (time points) = 24 + 72 = 96 total tests
Significance level = 0.05
So for a Dunnett's test to be statistically significant, it would need to have a p-value less than 0.0005 (0.05/96)???
Hope this makes sense. Little bit confused by all of this... JMP seems to use an LSD term, but I am not sure if it is taking all of the Type 1 errors into account.
Thank you!
