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

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 tim...

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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
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ISSN	0006-341X 1541-0420 1541-0420
DOI	10.1111/j.1541-0420.2007.00894.x

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Abstract	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.
AbstractList	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. Summary 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. 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 ([greater than or equal to] 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. [PUBLICATION ABSTRACT] Summary; 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 ( greater than or equal to 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. Summary 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. 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.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. 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. /// Beaucoup d'études longitudinales aboutissent à la fois à un délai jusqu'à un événement d'intérêt et à des données en mesures répétées. Ce papier est motivé par l'étude de patients ayant une allogreffe rénale, dans laquelle on s'intéresse à l'association entre les mesures longitudinales de protéinurie (une variable dichotomique) et le délai jusqu'au rejet de greffe rénale. Une caractéristique intéressante de l'échantillon est que presque la moitié des patients n'ont jamais été testés positifs pour la protéinurie (≥1gr/jour) durant le suivi, ce qui introduit une partie dégénérée dans la densité de l'effet aléatoire du processus longitudinal. Dans ce papier nous proposons un cadre de modèle à paramètre partagé en deux parties, et nous étudions la sensibilité à diverses structures de dépendance utilisées pour décrire l'association entre les mesures longitudinales de protéinurie et le délai jusqu'au rejet de greffe rénale.
Author	Rizopoulos, Dimitris Vanrenterghem, Yves Verbeke, Geert Lesaffre, Emmanuel
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Snippet	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... Summary 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... Summary 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... Summary; Many longitudinal studies generate both the time to some event of interest and repeated measures data. This article is motivated by a study on...
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SubjectTerms	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
Title	Two-Part Joint Model for the Analysis of Survival and Longitudinal Binary Data with Excess Zeros
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