{"database":"biostudies-literature","file_versions":[],"scores":null,"additional":{"submitter":["Rajeswaran J"],"funding":["NHLBI NIH HHS"],"pagination":["126-141"],"full_dataset_link":["https://www.ebi.ac.uk/biostudies/studies/S-EPMC5633490"],"repository":["biostudies-literature"],"omics_type":["Unknown"],"volume":["27(1)"],"pubmed_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."],"journal":["Statistical methods in medical research"],"pubmed_title":["Probability of atrial fibrillation after ablation: Using a parametric nonlinear temporal decomposition mixed effects model."],"pmcid":["PMC5633490"],"funding_grant_id":["R01 HL103552","U01 HL088942"],"pubmed_authors":["Ishwaran H","Rajeswaran J","Parides MK","Ehrlinger J","Li L","Blackstone EH"],"additional_accession":[]},"is_claimable":false,"name":"Probability of atrial fibrillation after ablation: Using a parametric nonlinear temporal decomposition mixed effects model.","description":"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.","dates":{"release":"2018-01-01T00:00:00Z","publication":"2018 Jan","modification":"2025-04-27T03:54:50.566Z","creation":"2019-03-26T22:26:04Z"},"accession":"S-EPMC5633490","cross_references":{"pubmed":["26740575"],"doi":["10.1177/0962280215623583"]}}