I am analyzing some patient data for a medical study that has a duration of several years.
Once a year, the patients are expected to visit the doctor, where they get four treatments, say A, B, C, D. Afterwards, to get an indication for the effect of each treatment the patient is scored on a numerical scale (same scale for each treatment).
For various reasons I have a lot of missing data because a patient may:
1. Miss the appointment, and I end up with missing data on specific years.
2. Not get all four treatments due to reasons related to the health of the patient or other technical reasons.
I am interested in both:
1. Finding whether there are differences in the mean score for each treatment separately over time.
2. Finding whether at specific point in time, there are differences in the mean score between the treatments.
Since the data for each year are not independent, the right tests to use to check whether there are differences in the mean scores for each treatment for different years is e.g., paired t-test or repeated measures ANOVA.
To have complete sets of data over the years, I must have different samples, i.e., I can find X patients that had treatment A for n years and then Y different patients that had treatment B for n years. For each group I would then perform e.g., paired t-test and then draw conclusions from it.
Is this methodology sound? Or are there other factors that I need to take into account for having a correct comparison between different groups?