Instrumental Variable Approach to Estimating Individual Causal Effects in N-of-1 Trials: Application to ISTOP Study
arXiv (Cornell University)(2023)
Abstract
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.
MoreTranslated text
Key words
individual causal effects
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