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 language | English (US) |
|---|---|
| Pages (from-to) | 126-141 |
| Number of pages | 16 |
| Journal | Statistical Methods in Medical Research |
| Volume | 27 |
| Issue number | 1 |
| DOIs | |
| State | Published - Jan 1 2018 |
| Externally published | Yes |
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