An N-of-1 trial is a multiple crossover trial conducted in a single individual to provide evidence to directly inform personalized treatment decisions. Advancements in wearable devices greatly improved the feasibility of adopting these trials to identify optimal individual treatment plans, particularly when treatments differ among individuals and responses are highly heterogeneous. Our work was motivated by the I-STOP-AFib Study, which examined the impact of different triggers on atrial fibrillation (AF) occurrence. We described a causal framework for 'N-of-1' trial using potential treatment selection paths and potential outcome paths. Two estimands of individual causal effect were defined:(a) the effect of continuous exposure, and (b) the effect of an individual observed behavior. We addressed three challenges: (a) imperfect compliance to the randomized treatment assignment; (b) binary treatments and binary outcomes which led to the 'non-collapsibility' issue of estimating odds ratios; and (c) serial inference in the longitudinal observations. We adopted the Bayesian IV approach where the study randomization was the IV as it impacted the choice of exposure of a subject but not directly the outcome. Estimations were through a system of two parametric Bayesian models to estimate the individual causal effect. Our model got around the non-collapsibility and non-consistency by modeling the confounding mechanism through latent structural models and by inferring with Bayesian posterior of functionals. Autocorrelation present in the repeated measurements was also accounted for. The simulation study showed our method largely reduced bias and greatly improved the coverage of the estimated causal effect, compared to existing methods (ITT, PP, and AT). We applied the method to I-STOP-AFib Study to estimate the individual effect of alcohol on AF occurrence.

翻译：N-of-1试验是在单个个体中进行的多交叉试验，旨在为个性化治疗决策提供直接证据。可穿戴设备的发展显著提高了采用此类试验识别最优个体治疗方案的可操作性，尤其是在治疗效应存在个体差异且反应高度异质性的情况下。本研究受I-STOP-AFib研究启发，该研究旨在探究不同触发因素对心房颤动（AF）发生的影响。我们构建了基于潜在治疗选择路径与潜在结局路径的N-of-1试验因果框架，定义了个体因果效应的两个估计量：（a）持续暴露效应，以及（b）个体观测行为效应。研究面临三大挑战：（a）随机化治疗方案的不完全依从性；（b）二元治疗与二元结局导致的比值比估计的"非可压缩性"问题；（c）纵向观测中的序列推断。我们采用贝叶斯工具变量方法，将研究随机化作为工具变量（IV），因其影响受试者的暴露选择而非直接作用于结局。通过两个参数化贝叶斯模型构成的系统进行个体因果效应估计，利用潜在结构模型建模混杂机制并基于贝叶斯后验函数推断，有效规避了非可压缩性与非一致性。同时考虑了重复测量中的自相关特性。模拟研究表明，与现有方法（ITT、PP、AT）相比，本方法显著降低偏倚并极大改善因果效应估计的覆盖概率。我们将该方法应用于I-STOP-AFib研究，以评估酒精对AF发生的个体效应。