Introduction
Within the last decade, personalized medicine has been on the rise. Treating patients on an individual level has been improved by the numerous possibilities to measure health outcomes with smart devices and application of novel data science approaches. In order to evaluate the effectiveness of health interventions on an individual level, N-of-1 trials have been established as the gold standard [Some studies have evaluated and compared different methods for the analysis. For example, Stunnenberg et al. [In this study, we focus on two particular challenges for the analysis of aggregated N-of-1 trials: (i) carry-over and (ii) complex dependencies and time-varying interactions of the treatment effect with covariates. First, as a patient cannot receive two treatments at the same time, the treatment is time-varying. This may introduce carry-over - i.e., that the effect of one treatment is still active when the other treatment is applied - complicating the analysis of the trials and the interpretation of the results. As one solution, wash-out periods can be introduced in the study design, where the patient does not receive any of the treatments. However, this is not always possible, so statistical methods have to be investigated regarding their robustness against known or unknown carry-over. Second, for the aggregated analysis of N-of-1 trials, treatment effects may often depend on covariates, their effect might be modified by them, and this might be further complicated if time-varying interactions between treatment and effect modifiers exist. Carry-over and such complex dependencies have to be considered to ensure unbiased estimates of the causal treatment effects, but best-practise recommendations are not available [Our paper is organized as follows. In 