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Advances 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 \u201cN-of-1\u201d trial using potential treatment selection paths and potential outcome paths. Two estimands of individual causal effect were defined: (i) the effect of continuous exposure, and (ii) the effect of an individual\u2019s observed behavior. We addressed three challenges: (i) imperfect compliance to the randomized treatment assignment; (ii) binary treatments and binary outcomes, which led to the \u201cnon-collapsibility\u201d issue of estimating odds ratios; and (iii) serial correlation in the longitudinal observations. We adopted the Bayesian IV approach where the study randomization was the instrumental variable (IV) as it impacted the patient\u2019s choice of exposure but not directly the outcome. Estimations were obtained 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.<\/jats:p>","DOI":"10.1093\/biostatistics\/kxaf042","type":"journal-article","created":{"date-parts":[[2025,10,20]],"date-time":"2025-10-20T11:28:56Z","timestamp":1760959736000},"source":"Crossref","is-referenced-by-count":0,"title":["Instrumental variable approach to estimating individual causal effects in N-of-1 trials: application to ISTOP study"],"prefix":"10.1093","volume":"26","author":[{"ORCID":"https:\/\/orcid.org\/0000-0001-5333-9479","authenticated-orcid":false,"given":"Kexin","family":"Qu","sequence":"first","affiliation":[{"name":"Department of Biostatistics, School of Public Health, Brown University, \u00a0 121 South Main Street, Providence, RI 02903,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-0855-5313","authenticated-orcid":false,"given":"Christopher H","family":"Schmid","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health, Brown University , 121 South Main Street, Providence, RI 02903,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]},{"given":"Tao","family":"Liu","sequence":"additional","affiliation":[{"name":"Department of Biostatistics, School of Public Health, Brown University, \u00a0 121 South Main Street, Providence, RI 02903,","place":["United States"]}],"role":[{"role":"author","vocabulary":"crossref"}]}],"member":"286","published-online":{"date-parts":[[2025,12,13]]},"reference":[{"key":"2026031108164817500_kxaf042-B1","doi-asserted-by":"crossref","first-page":"669","DOI":"10.1080\/01621459.1993.10476321","article-title":"Bayesian analysis of binary and polychotomous response data","volume":"88","author":"Albert","year":"1993","journal-title":"J Am Stat 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