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

Background 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 is carry-over or complex dependencies of the treatment effect of interest. Methods In this study, we evaluate and compare methods for the analysis of aggregated N-of-1 trials in different scenarios with carry-over and complex dependencies of treatment effects on covariates. 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 carry-over adjusted parametric model (COAPM). Results The results show that all evaluated existing models have a good performance when there is no carry-over and no treatment dependence. When there is carry-over, COAPM yields unbiased and more efficient estimates while all other methods show some bias in the estimation. When there is known treatment dependence, all approaches that are capable to model it 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. Conclusions 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.

Analyzing population-level trials as N-of-1 trials: An application to gait

“…In the case of prominent carryover effects, the single-crossover design does not isolate the carryover effects from the intervention effects for the individual. As one approach, the carryover effects could be modeled in the analysis to still allow efficient and unbiased estimation of the effects [43] . In future work, additional crossovers between the fatigue and dual-task conditions can be added to the study design, in order to introduce randomization within one person.…”

“…As future work, the effect sizes of interventions can be estimated and further investigated based on the posteriors obtained from the Bayesian analyses [45] . Aggregated N-of-1 trials analyses can be performed to investigate the underlying causes of the personalized responses to intervention [43] . In our study, the different responses could potentially be associated with the participants’ pre-existing health conditions, anthropometric features or stable lifestyle habits (e.g., as measured by the IPAQ questionnaire), or a combination of all these factors.…”