Comparison of Bayesian Networks, G-estimation and linear models to estimate causal treatment effects in aggregated N-of-1 trials with carry-over effects

Abstract

The aggregation of a series of N-of-1 trials presents an innovative and efficient study design, as an alternative to traditional randomized clinical trials. Challenges for the statistical analysis arise when there are carry-over effects or confounding of the treatment effect of interest.In this study, we evaluate and compare methods for the analysis of aggregated N-of-1 trials in different scenarios with carry-over and confounding effects. For this, we simulate data of a series of N-of-1 trials for chronic nonspecific low back pain based on assumed causal relationships parameterized by directed acyclic graphs. In addition to existing statistical methods such as regression models, Bayesian Networks and G-estimation, we introduce a linear model adjusted for time-varying treatment carry-over effects.The results show that all evaluated existing models have a good performance when there is no carry-over and confounding. When there are carry-over effects, our proposed method yields unbiased estimates while all methods show some bias in the estimation. When there is known confounding, all approaches that specify the confounding correctly yield unbiased estimates. Finally, the efficiency of all methods decreases slightly when there are missing values, and the bias in the estimates can also increase.This study presents a systematic evaluation of existing and novel approaches for the statistical analysis of a series of N-of-1 trials. We derive practical recommendations which methods may be best in which scenarios.