I'm working on a study that involves measuring breathing effort of an individual over short periods of time (specifically breath distention). The dependent variable is based on a new therapeutic and control groups. There are an equal number of individuals in each group (which is actually a small miracle of coincidence considering recruitment goals are often not met).
However, a variance in the "length" (sample rate) of the reads occurs from the devices used to measure the breath distention. The probes are sampling at 70Hz, and given results from each reading, paired with actual video observation, will give a composite value for each observation (again at 70Hz).
We eliminate "bad" reads (where the body was moving, or the probes aren't in sync, etc), and have good empirical methods for doing so. This normally means we end up with between 1 and 2.5 min of "good" uninterrupted read time for each individual.
If you are sampling at 70Hz, the difference of 90s leads to substantial difference in records for any one individual. I would consider this difference in time to be an independent variable, not likely related to the interventions performed (though can't strictly rule that out).
The data sets for each individual seem normally distributed, and the control groups also seem normally distributed. Therefore, I'm inclined to ignore the differences in records per individual, and continue with an ANOVA for the single time points.
Would it be more appropriate to trim or transform the data so that the sampling was weighted or normalized per individual?
I had a hard time finding anything addressing different sample rates over identical sample size, so forgive me if I missed something obvious.