I need to compare the transport of a compound across 2 different cell lines (non-transfected vs. transfected) over time.
My goal is to compare the mean transport between Cell 1 and Cell 2 at time 20 min, then at time 40 min, then at 60 min and 120 min. I am measuring this transport in the same system over time (stacked values are from the exact same system) in triplicate for each cell.
My results table looks somewhat like in the attached file.
The idea is to compare the mean values between Cell 1 and Cell 2. And repeat this comparison for each time point. (Eg.: At time 20 compare (1+1.1+1.1)/3 to (2.1+3.0+2.7)/3 and do the same for the following time points).
Which statistical test would be more appropriate for this scenario? I've seen multiple t-test (with Bonferroni or Holm-Sidak correction for multiple comparisons), as well as 2 way ANOVA and Repeated measure ANOVA but am not sure on how to choose between them.
As it involves multiple measurements in the different time frame I suggest you should go for Repeated measures ANOVA
Multiple paired t-tests are the worst option, particularily if you don't use apooled variance estimate.
I think, repeated measures ANOVA actually means exactly this: to eventually have "paired t-test" but basedon a pooled variance estimate. However, this is still suboptimal because it assumes that data from adjacent time-points are intependent, what they are obviousely not.
To my opinion, a hierarchical (multilevel) regression model would be the most approriate (and most modern and most powerful) approach.
Dear Leticia Vieira ,
whatever test you will use, the main point to consider is that your time-points are not independent. Therefore, your approach have to take in account the dependence.
I think the most suitable is repeated measurements ANOVA.
Considering. Cell type, time and their interaction as fixed factors.
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