I want to exmine whether three interventions have different effects over time on an outcome that has been measured only two times (two assessments before and after treatment: baseline and follow-up) in a RCT. I would like to adjust for baseline differences, but this is kind of tricky because of only two assessments. I have read a lot of articles that suggest to use an ANCOVA approach with only two assessments, but that leads to the problem that cases with missing values are listwise deleted. I would like to use mixed models (in long format) to include all available data (also cases with missing data).
My questions are: 1) Is it reasonable to use baseline and follow-up assessments as the outcome/dependent variable with treatment group (3 groups), time and treatment*time interaction as fixed effects with random intercepts and (at the same time) to adjust for baseline scores as a covariate (also as fixed effects)when using mixed models?
2) If the answer is yes: Should I also include the baseline*time interaction even if I have only one more assessment (follow-up)?
I have found a similar question (Baseline adjustment in mixed models), but I am not sure whether this applies also for only two measurements. In this link I have not found any references showing, that adjusting for baseline scores is allowed while at the same time baseline scores are part of the outcome. I have consulted several statistic books and articles and always found that adjusting for baseline (as a fixed effect) is possible if it is not part of the outcome/dependent measures. It would be great if you have any articles/literature showing that this (baseline as outcome AND as a covariate) is a proper way to do a statistical analysis (if that is actually the case).