title: A multiphase non-linear mixed effects model: An application to spirometry after lung transplantation.
abstract: In medical sciences, we often encounter longitudinal temporal relationships that are non-linear in nature. The influence of risk factors may also change across longitudinal follow-up. A system of multiphase non-linear mixed effects model is presented to model temporal patterns of longitudinal continuous measurements, with temporal decomposition to identify the phases and risk factors within each phase. Application of this model is illustrated using spirometry data after lung transplantation using readily available statistical software. This application illustrates the usefulness of our flexible model when dealing with complex non-linear patterns and time-varying coefficients.
authors.affiliation: Department of Quantitative Health Sciences, Heart and Vascular Institute, Cleveland Clinic, Cleveland, USA.
authors.forename: Jeevanantham
authors.initials: J
authors.lastname: Rajeswaran
authors.affiliation: Department of Quantitative Health Sciences, Heart and Vascular Institute, Cleveland Clinic, Cleveland, USA.
authors.forename: Eugene H
authors.initials: EH
authors.lastname: Blackstone
mesh_terms: D006801:Humans; D008137:Longitudinal Studies; D016040:Lung Transplantation; D015233:Models, Statistical; D012307:Risk Factors; D012984:Software; D013147:Spirometry / Q000379:methods*
keywords: additive regression; mixed-effects model; non-linear model; spirometry data; temporal decomposition; time-varying coefficient