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

BMC Medical Research Methodology

Research

Bert Arnrich¹  Thomas Gärtner¹  Juliana Schneider¹  Stefan Konigorski² 

[1] Digital Health Center, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany;University of Potsdam, Digital Engineering Faculty, Potsdam, Germany;Digital Health Center, Hasso Plattner Institute for Digital Engineering, Potsdam, Germany;University of Potsdam, Digital Engineering Faculty, Potsdam, Germany;Hasso Plattner Institute for Digital Health at Mount Sinai, Icahn School of Medicine at Mount Sinai, New York, USA;

关键词: N-of-1 trials;    Randomized clinical trials;    Bayesian Networks;    G-estimation;    Linear model;    Simulation study;    Chronic Nonspecific Low Back Pain;   

DOI  :  10.1186/s12874-023-02012-5

 received in 2022-07-13, accepted in 2023-08-07,  发布年份 2023

来源: Springer

【 摘 要 】

BackgroundThe 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.MethodsIn 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).ResultsThe 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.ConclusionsThis 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.

【 授权许可】

CC BY   
© BioMed Central Ltd., part of Springer Nature 2023

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