biostudies-literatureRajeswaran JNHLBI NIH HHS126-141https://www.ebi.ac.uk/biostudies/studies/S-EPMC5633490biostudies-literatureUnknown27(1)Atrial fibrillation is an arrhythmic disorder where the electrical signals of the heart become irregular. The probability of atrial fibrillation (binary response) is often time varying in a structured fashion, as is the influence of associated risk factors. A generalized nonlinear mixed effects model is presented to estimate the time-related probability of atrial fibrillation using a temporal decomposition approach to reveal the pattern of the probability of atrial fibrillation and their determinants. This methodology generalizes to patient-specific analysis of longitudinal binary data with possibly time-varying effects of covariates and with different patient-specific random effects influencing different temporal phases. The motivation and application of this model is illustrated using longitudinally measured atrial fibrillation data obtained through weekly trans-telephonic monitoring from an NIH sponsored clinical trial being conducted by the Cardiothoracic Surgery Clinical Trials Network.Statistical methods in medical researchProbability of atrial fibrillation after ablation: Using a parametric nonlinear temporal decomposition mixed effects model.PMC5633490R01 HL103552U01 HL088942Ishwaran HRajeswaran JParides MKEhrlinger JLi LBlackstone EHfalseProbability of atrial fibrillation after ablation: Using a parametric nonlinear temporal decomposition mixed effects model.Atrial fibrillation is an arrhythmic disorder where the electrical signals of the heart become irregular. The probability of atrial fibrillation (binary response) is often time varying in a structured fashion, as is the influence of associated risk factors. A generalized nonlinear mixed effects model is presented to estimate the time-related probability of atrial fibrillation using a temporal decomposition approach to reveal the pattern of the probability of atrial fibrillation and their determinants. This methodology generalizes to patient-specific analysis of longitudinal binary data with possibly time-varying effects of covariates and with different patient-specific random effects influencing different temporal phases. The motivation and application of this model is illustrated using longitudinally measured atrial fibrillation data obtained through weekly trans-telephonic monitoring from an NIH sponsored clinical trial being conducted by the Cardiothoracic Surgery Clinical Trials Network.2018-01-01T00:00:00Z2018 Jan2025-04-27T03:54:50.566Z2019-03-26T22:26:04ZS-EPMC56334902674057510.1177/0962280215623583