# [Question] Using and interpreting a repeated measures ANOVA

Hello all,

I’ve come across a problem in some clinical data I’m attempting to analyze and I’m somewhat stuck at the moment.

For the purposes of this analysis, there are 4 variables of interest: patient ID, time, oxygen level, and a categorical assessment (call it A) made at each minute. In other words, over 60 minutes, each patient had their O2 and this categorical variable assessed.

I’m attempting to see if there is a significant difference in the O2 level based on the level of A. In other words, as a group, do patients at A=1 have a significantly different mean O2 than patients at A=2?

I initially attempted a simple one-way ANOVA, but realized that one of the assumptions (independent observations) was not satisfied since patient 1 could have an A=2 at 5 mins, but A=4 at 10 mins. Therefore, it seemed that the groups of measurements (O2 at A=1, O2 at A=2, etc.) were interrelated, since a single patient could be in all 5 groups.

I’m attempting (for the first time) a repeated measures ANOVA where my model includes all four variables – the repeating variable is time, the between-subjects unit is ID, and time, O2, and A are all IVs. My code looks like this:

``anova o2 ID time A, repeated(time) bse(ID) ``

When interpreting the output of the table, is the only F-statistic (and corresponding p-value) I care about going to be the line item for A given that’s the only variable I’m interested in comparing between groups to the O2? Or does the presence of any p-value > 0.05 (arbitrary significance level) indicate that the omnibus test fails and I need to run independent one-way t-tests (or whatever its non-parametric cousin is)?

Another consideration: my data is long-form and there are multiple observations for every level of A for O2 – should I instead use a mean O2 for each patient’s A=1, mean O2 if A=2, etc. in order to simplify the test?

Thanks for the help!

Additional info: I collapsed the measures to instead show an average O2 (avgo2) for each ID at each level of A. This seems more understandable, since time is not as important of a variable in this statistic – I instead used A as the repeated measure, as that’s what independent people are multiply measured in. The code thus changed to:

``anova avgo2 A, repeated(A) ``

Perhaps this is a more cogent way to approach the problem? It also yield non-significant tests, at which point my question remains: should we report only the F-statistic for avgo2 x A? Or should all be reported?

Thank you!

submitted by /u/iDunTrollBro