I have a cross-over design where a sample of subjects undergoes 7 different interventions, and within each intervention there are 3 time points (pre-intervention, post-intervention1, post-intervention2).
I firstly did an overall ANOVA with factors time x intervention, to check whether there were overall differences between interventions at any time point tested.
For the within-intervention analysis, I first did a repeated measures ANOVA (alpha = .05) with factor time (3), with post-hoc multiple comparisons (pairwise t-tests) Bonferroni corrected.
However, during review in a peer-reviewed journal, a reviewer claimed that my "time" effect for a certain target intervention was not significant (p = 0.028; < 0.05) because it wasn't corrected for multiple comparisons (which I only used in the post-hoc comparison).
Is the reviewer's point valid? Do I need to correct alpha in the RM ANOVA? I've been trying to find a relevant explanation in "the interwebs", but I can't find anything conclusive. So far, the best I've come upon is this extract of a 2001 paper by Bender & Lange:
"Methods to adjust for multiple testing in studies collecting repeated measurements are rare. Despite much recent work on mixed models [38,39] with random subject effects to allow for correlation of data, there are only few multiple comparison procedures for special situations. It is difficult to develop a general adjustment method for multiple comparisons in the case of repeated measurements since these comparisons occur for between-subject factors (e.g., groups), within-subject factors (e.g., time), or both. The specific correlation structure has to be taken into account, involving many difficulties. If only comparisons for between-subject factors are of interest, one possibility is to consider the repeated measurements as multiple endpoints and use one of the methods mentioned in the previous section. However, if the repeated measurements are ordered, this information is lost by using such an approach."