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 journalArticlepeer-review

10 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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