Joint modelling for organ transplantation outcomes for patients with diabetes and the end-stage renal disease

Statistical Methods in Medical Research
Jianghu James DongJiguo Cao

Abstract

This article is motivated by jointly modelling longitudinal and time-to-event clinical data of patients with diabetes and end-stage renal disease. All patients are on the waiting list for the pancreas transplant after kidney transplant, and some of them have a pancreas transplant before kidney transplant failure or death. Scant literature has studied the dynamical joint relationship of the estimated glomerular filtration rates trajectory, the effect of pancreas transplant, and time-to-event outcomes, although it remains an important clinical question. In an attempt to describe the association in the multiple outcomes, we propose a new joint model with a longitudinal submodel and an accelerated failure time submodel, which are linked by some latent variables. The accelerated failure time submodel is used to determine the relationship of the time-to-event outcome with all predictors. In addition, the piecewise linear function in the survival submodel is used to calculate the dynamic hazard ratio curve of a time-dependent side event, because the effect of the side event on the time-to-event outcome is non-proportional. The model parameters are estimated with a Monte Carlo EM algorithm. The finite sample performance of the proposed...Continue Reading

References

Jun 15, 2004·Transplantation Proceedings·E OrsenigoV Di Carlo
May 31, 2008·Biometrics·Peter X-K SongYing Yuan
May 6, 2009·Annals of Internal Medicine·Andrew S LeveyUNKNOWN CKD-EPI (Chronic Kidney Disease Epidemiology Collaboration)
Jun 6, 2009·Clinical Transplantation·Francois KleinclaussAngelika C Gruessner

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Citations

May 11, 2021·Statistical Methods in Medical Research·Jianghu James DongTroy Plumb

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Software Mentioned

MCEM

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