A Semiparametric Joint Model for Longitudinal and Time-to- Event Univariate Data in Presence of Cure Fraction

• Main Authors: Abu Bakar, Mohd Rizam, A. Salah, Khalid, Ibrahim, Noor Akma, Haron, Kassim
• Format: Article
• Language: English; English
• Published: EuroJournals Publishing Inc. 2007
• Online Access: http://psasir.upm.edu.my/id/eprint/7671/1/A%20Semiparametric%20Joint%20Model%20for%20Longitudinal%20and%20Time.pdf; http://psasir.upm.edu.my/id/eprint/7671/; http://www.eurojournals.com/ejsr.htm

Many medical investigations generate both repeatedly-measured (longitudinal)biomarker and survival data. One of complex issue arises when investigating the association between longitudinal and time-to-event data when there are cured patients in the population, which leads to a plateau in the survival function S(t) after sufficient follow-up. Thus, usual Cox proportional hazard model Cox (1972) is not applicable since the proportional hazard assumption is violated. An alternative is to consider survival models incorporating a cure fraction. In this paper we present a new class of joint model for univariate longitudinal and survival data in presence of cure fraction. For the longitudinal model, a stochastic Integrated Ornstein-Uhlenbeck process will present, and for the survival model a semiparametric survival function will be considered which accommodate both zero and non-zero cure fractions of the dynamic disease progression. Moreover, we consider a Bayesian approach which is motivated by the complexity of the model. Posterior and prior specification needs to accommodate parameter constraints due to the nonnegativity of the survival function. A simulation study is presented to evaluate the performance of this joint model.