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Joint Modeling of Survival and Longitudinal Ordered Data Using a Semiparametric Approach
Author(s) -
Preedalikit Kemmawadee,
Liu Ivy,
Hirose Yuichi,
Sibanda Nokuthaba,
Fernández Daniel
Publication year - 2016
Publication title -
australian and new zealand journal of statistics
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.434
H-Index - 41
eISSN - 1467-842X
pISSN - 1369-1473
DOI - 10.1111/anzs.12153
Subject(s) - proportional hazards model , semiparametric regression , survival analysis , econometrics , semiparametric model , mathematics , survival function , hazard , statistics , component (thermodynamics) , hazard ratio , baseline (sea) , ordered logit , nonparametric statistics , confidence interval , chemistry , physics , organic chemistry , oceanography , thermodynamics , geology
Summary 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.