Two-Part Joint Model for the Analysis of Survival and Longitudinal Binary Data with Excess Zeros

• Published in: Biometrics Vol. 64; no. 2; pp. 611 - 619
• Main Authors: Rizopoulos, Dimitris, Verbeke, Geert, Lesaffre, Emmanuel, Vanrenterghem, Yves
• Format: Journal Article
• Language: English
• Published: Malden, USA Blackwell Publishing Inc 01.06.2008; Blackwell Publishing; Blackwell Publishing Ltd
• Subjects: Biometric Practice; Biometrics; Biometry - methods; Computer Simulation; Copula functions; Copulas; Data Interpretation, Statistical; Joint modeling; Kidney diseases; Logistic regression; Longitudinal Studies; Missing data; Modeling; Models, Statistical; Parametric models; Proportional Hazards Models; Proteinuria; Research Design; Sensitivity analysis; Shared parameter model; Survival Analysis; Survival Rate; Tissue grafting; Transplants & implants; Urinalysis
• ISSN: 0006-341X; 1541-0420; 1541-0420
• DOI: 10.1111/j.1541-0420.2007.00894.x

Many longitudinal studies generate both the time to some event of interest and repeated measures data. This article is motivated by a study on patients with a renal allograft, in which interest lies in the association between longitudinal proteinuria (a dichotomous variable) measurements and the time to renal graft failure. An interesting feature of the sample at hand is that nearly half of the patients were never tested positive for proteinuria (>=1g/day) during follow-up, which introduces a degenerate part in the random-effects density for the longitudinal process. In this article we propose a two-part shared parameter model framework that effectively takes this feature into account, and we investigate sensitivity to the various dependence structures used to describe the association between the longitudinal measurements of proteinuria and the time to renal graft failure.