| Literature DB >> 26740575 |
Jeevanantham Rajeswaran1, Eugene H Blackstone1, John Ehrlinger1, Liang Li2, Hemant Ishwaran3, Michael K Parides4.
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.Entities:
Keywords: Binary longitudinal response; mixed effects model; multiphase model; nonlinear model; temporal decomposition; time varying coefficient
Mesh:
Year: 2016 PMID: 26740575 PMCID: PMC5633490 DOI: 10.1177/0962280215623583
Source DB: PubMed Journal: Stat Methods Med Res ISSN: 0962-2802 Impact factor: 3.021