I was given data to analyze for a study looking at the effects of a treatment on iron levels at four different time points (before treatment, the day treatment ended, 4 weeks after treatment, and 2-4 months after treatment). There is no control group. They are looking to see if there are significant increases in iron levels at each of the 3 post-treatment time points to the before treatment (baseline) level. Eleven patients had baseline levels but only 8 patients had complete data for all 4 time points ($n$=11, 10, 9, and 8 for each time point). Not only were iron levels measured, but two other laboratory measures were taken at each time point to be compared to baseline.
I have a few questions as to how to analyze this. I first thought an RM ANOVA would be appropriate to analyze this data, but I was concerned about the small sample size, the loss of data, and the non-normal distribution of the data. I then considered comparing each post-treatment measure to baseline using Wilcoxon signed-rank tests, but then I run into the issue of multiple comparisons. However, I have read some literature that downplays needing to run multiple comparisons. So overall, I'm dealing with small sample sizes, incomplete data, and multiple comparisons (and whether or not its necessary).
I hope this all made sense. I'm new to CrossValidated and was directed here by a colleague as a place to learn from experienced statisticians, so I'd appreciate any advice! Thanks!
Edited to add raw data from comment:
There are four total time points and the outcome variable is continuous. For example, the results at each time point look similar to this:
Baseline (n=11): [2, 7, 7, 3, 6, 3, 2, 4, 4, 3, 14] 1st Post (n=10): [167, 200, 45, 132, ., 245, 199, 177, 134, 298, 111] 2nd Post (n=9): [75, 43, 23, 98, 87, ., 300, ., 118, 202, 156] 3rd Post (n=8): [23, 34, 98, 112, ., 200, ., 156, 54, 18, .]