Probability of atrial fibrillation after ablation: Using a parametric nonlinear temporal decomposition mixed effects model

Jeevanantham Rajeswaran, Eugene H. Blackstone, John Ehrlinger, Liang Li, Hemant Ishwaran, Michael K. Parides

Research output: Contribution to journalArticle

6 Scopus citations

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.

Original languageEnglish (US)
Pages (from-to)126-141
Number of pages16
JournalStatistical Methods in Medical Research
Volume27
Issue number1
DOIs
StatePublished - Jan 1 2018
Externally publishedYes

Keywords

  • Binary longitudinal response
  • Mixed effects model
  • Multiphase model
  • Nonlinear model
  • Temporal decomposition
  • Time varying coefficient

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability
  • Health Information Management

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