Scenario 4: carry-over and activity interaction
The last investigated scenario contains both carry-over and Activity interaction. We observe that both the sample mean and G-estimation (AR1) strongly underestimate the treatment effect, both for complete data and data with missing values. Both the Bayesian Network with time adjustment and unadjusted Bayesian Network provide treatment effect difference estimates of about 1.5, hence underestimating the effect difference, and also yield larger standard errors compared to the other methods as seen already in scenario 3. The Bayesian Network with time adjustment yields slightly better results than the unadjusted Bayesian Network, but does not provide a major improvement. The linear model and G-estimation (independence) provide less biased treatment effect estimates, but still also underestimate the treatment effect difference. Finally, the COAPM again provides good results in this scenario. Across all sample sizes with complete data, this model yields unbiased estimates of the treatment effect difference. When data points are missing, this model slightly overestimates the treatment effect.