A joint model for repeated events of different types and multiple longitudinal outcomes with application to a follow‐up study of patients after kidney transplant. Issue 2 (15th October 2014)

• Record Type: Journal Article
• Authors: Musoro, Jammbe Z.; Geskus, Ronald B.; Zwinderman, Aeilko H.
• Is Part Of: Biometrical journal. Volume 57:Issue 2(2015:Mar.)
• Page Start: 185
• Page End: 200
• Publication Date: 2014-10-15
• Subjects: Biometry -- Periodicals; Medical statistics -- Periodicals; 570.15195
• DOI: 10.1002/bimj.201300167
• Languages: English
• ISSNs: 0323-3847

Abstract

<abstract abstract-type="main"> <title> <x xml:space="preserve">Abstract</x> </title> <p>This paper presents an extension of the joint modeling strategy for the case of multiple longitudinal outcomes and repeated infections of different types over time, motivated by postkidney transplantation data. Our model comprises two parts linked by shared latent terms. On the one hand is a multivariate mixed linear model with random effects, where a low‐rank thin‐plate spline function is incorporated to collect the nonlinear behavior of the different profiles over time. On the other hand is an infection‐specific Cox model, where the dependence between different types of infections and the related times of infection is through a random effect associated with each infection type to catch the <italic>within</italic> dependence and a shared frailty parameter to capture the dependence <italic>between</italic> infection types. We implemented the parameterization used in joint models which uses the fitted longitudinal measurements as time‐dependent covariates in a relative risk model. Our proposed model was implemented in OpenBUGS using the MCMC approach.</p> </abstract>