Probability of atrial fibrillation after ablation: Using a parametric nonlinear temporal decomposition mixed effects model. (January 2018)

• Record Type: Journal Article
• Authors: Rajeswaran, Jeevanantham; Blackstone, Eugene H; Ehrlinger, John; Li, Liang; Ishwaran, Hemant; Parides, Michael K
• Is Part Of: Statistical methods in medical research. Volume 27:Number 1(2018)
• Page Start: 126
• Page End: 141
• Publication Date: 2018-01
• Subjects: Binary longitudinal response -- nonlinear model -- mixed effects model -- temporal decomposition -- multiphase model -- time varying coefficient; Medicine -- Research -- Statistical methods -- Periodicals; Research -- Periodicals; Review Literature -- Periodicals; Statistics -- methods -- Periodicals; Médecine -- Recherche -- Méthodes statistiques -- Périodiques; 610.727
• DOI: 10.1177/0962280215623583
• Languages: English
• ISSNs: 0962-2802

Abstract

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.