Joint Modeling of Survival and Longitudinal Ordered Data Using a Semiparametric Approach. (1st July 2016)

• Record Type: Journal Article
• Authors: Preedalikit, Kemmawadee; Liu, Ivy; Hirose, Yuichi; Sibanda, Nokuthaba; Fernández, Daniel
• Is Part Of: Australian & New Zealand journal of statistics. Volume 58:Number 2(2016:Jun.)
• Page Start: 153
• Page End: 172
• Publication Date: 2016-07-01
• Subjects: competing risks -- ordinal responses -- profile likelihood -- proportional odds -- quality of life -- stereotype model; Statistics -- Periodicals; 519.5
• DOI: 10.1111/anzs.12153
• Languages: English
• ISSNs: 1369-1473

Abstract

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. Abstract : This paper considers a semiparametric joint model for item response and survival components, linked through latent variables.