1. Joint Modeling of Survival and Longitudinal Ordered Data Using a Semiparametric Approach
- Author
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Nokuthaba Sibanda, Ivy Liu, Kemmawadee Preedalikit, Daniel Fernández, Yuichi Hirose, Universitat Politècnica de Catalunya. Departament d'Estadística i Investigació Operativa, and Universitat Politècnica de Catalunya. GRBIO - Grup de Recerca en Bioestadística i Bioinformàtica
- Subjects
Statistics and Probability ,Longitudinal study ,Survival ,Stereotype model ,Longitudinal method ,Competing risks ,01 natural sciences ,010104 statistics & probability ,03 medical and health sciences ,0302 clinical medicine ,Quality of life (healthcare) ,Pacients -- Satisfacció ,Joint model ,Anàlisi de supervivència (Biometria) ,Econometrics ,Survival analysis (Biometry) ,030212 general & internal medicine ,0101 mathematics ,Joint (geology) ,Proportional odds ,Mathematics ,Profile likelihood ,Matemàtiques i estadística::Estadística aplicada::Estadística biosanitària [Àrees temàtiques de la UPC] ,Patient satisfaction ,Analisi multivariable ,Semiparametric model ,Multivariate analysis ,Ordinal responses ,Mètode longitudinal ,Statistics, Probability and Uncertainty - Abstract
Medical research frequently focuses on the relationship between quality of life (QoL) and survival time of subjects. QoL may be one of the most important factors that could be used to predict survival, making it worth identifying factors that jointly affect survival and QoL. We propose a semiparametric joint model that consists of item response and survival components, where these two components are linked through latent variables. Several popular ordinal models are considered and compared in the item response component, while the Cox proportional hazards model is used in the survival component. We estimate the baseline hazard function and model parameters simultaneously, through a profile likelihood approach. We illustrate the method using an example from a clinical study.
- Published
- 2016
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