Summary
Overall, COAPM yields robust results, the best results among all considered models, in all scenarios with complete data. However, when data is missing and carry-over is present, then this approach tends to overestimate the treatment effect difference. Linear models and 2-sample t-tests are robust against simulated missing values, but yield biased effect estimates when strong carry-over is present as they are not adjusted for it. Furthermore, the sample mean yields biased effect estimates when Activity interaction is present. Bayesian Networks and G-estimation show a good overall performance, but Bayesian Networks yield wider confidence intervals of effect estimates especially for small sample sizes.