TY - JOUR
T1 - Probability of atrial fibrillation after ablation
T2 - Using a parametric nonlinear temporal decomposition mixed effects model
AU - Rajeswaran, Jeevanantham
AU - Blackstone, Eugene H.
AU - Ehrlinger, John
AU - Li, Liang
AU - Ishwaran, Hemant
AU - Parides, Michael K.
N1 - Publisher Copyright:
© The Author(s) 2016.
PY - 2018/1/1
Y1 - 2018/1/1
N2 - 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.
AB - 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.
KW - Binary longitudinal response
KW - Mixed effects model
KW - Multiphase model
KW - Nonlinear model
KW - Temporal decomposition
KW - Time varying coefficient
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U2 - 10.1177/0962280215623583
DO - 10.1177/0962280215623583
M3 - Article
C2 - 26740575
AN - SCOPUS:85041425924
SN - 0962-2802
VL - 27
SP - 126
EP - 141
JO - Statistical Methods in Medical Research
JF - Statistical Methods in Medical Research
IS - 1
ER -