# An estimation of the chronic rejection of kidney transplant using an eternal Weibull regression: A historical cohort study

Golestan, B., Hosseini-Moghaddam, S.M., Nafar, M., Rennolls, K. and Mohammad, K. (2009) *An estimation of the chronic rejection of kidney transplant using an eternal Weibull regression: A historical cohort study.* Archives of Iranian Medicine, 12 (4). pp. 341-346. ISSN 1029-2977 (Print), 1735-3947 (Online)

## Abstract

Background: We estimated the chronic rejection of kidney transplant using an eternal Weibull regression. Methods: In this historical cohort study, we enrolled all patients with chronic renal failure who were admitted to Shahid Labbafinejad medical center (Tehran, Iran) from 1984 to 2003. Using Matlab 7.0, we considered the eternal proportion θ , as a logistic-type function of the covariates and modified the survival function. We estimated the survival function in unmodified and modified forms using Weibull distribution. Results: The chance of chronic rejection was 1.95 times higher among those who received a kidney transplant before 1996. Considering all cases who received renal transplantation after 1984, males had a chance of rejection 20% less than females. Next to the eternity, Weibull model was fitted to patients who received renal transplantation after 1996. Treatment protocol was changed after 1996 expecting fewer chronic rejections; thereafter, the eternal proportion was estimated to be 0.81. This seems quite considerable as a percentage of non-failure cases. Conclusion: Providing a non-zero eternal proportion, the modified model would be superior over the unmodified mode.

Item Type: | Article |
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Uncontrolled Keywords: | cohort studies, graft rejection, kidney transplantation, regression analysis |

Subjects: | R Medicine > R Medicine (General) R Medicine > RB Pathology R Medicine > RC Internal medicine R Medicine > RZ Other systems of medicine |

School / Department / Research Groups: | Faculty of Architecture, Computing & Humanities School of Computing & Mathematical Sciences Faculty of Architecture, Computing & Humanities > School of Computing & Mathematical Sciences School of Computing & Mathematical Sciences > Department of Smart Systems Technologies Faculty of Architecture, Computing & Humanities > School of Computing & Mathematical Sciences > Department of Smart Systems Technologies |

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Last Modified: | 24 Jul 2015 15:56 |

URI: | http://gala.gre.ac.uk/id/eprint/7848 |

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