Background: The influence of donor and recipient factors on outcomes following kidney transplantation is\ncommonly analysed using Cox regression models, but this approach is not useful for predicting long-term\nsurvival beyond observed data. We demonstrate the application of a flexible parametric approach to fit a\nmodel that can be extrapolated for the purpose of predicting mean patient survival. The primary motivation\nfor this analysis is to develop a predictive model to estimate post-transplant survival based on individual\npatient characteristics to inform the design of alternative approaches to allocating deceased donor kidneys\nto those on the transplant waiting list in the United Kingdom.\nMethods: We analysed data from over 12,000 recipients of deceased donor kidney or combined kidney and\npancreas transplants between 2003 and 2012. We fitted a flexible parametric model incorporating restricted cubic\nsplines to characterise the baseline hazard function and explored a range of covariates including recipient, donor and\ntransplant-related factors.\nResults: Multivariable analysis showed the risk of death increased with recipient and donor age, diabetic nephropathy\nas the recipient�s primary renal diagnosis and donor hypertension. The risk of death was lower in female\nrecipients, patients with polycystic kidney disease and recipients of pre-emptive transplants. The final model\nwas used to extrapolate survival curves in order to calculate mean survival times for patients with specific\ncharacteristics.\nConclusion: The use of flexible parametric modelling techniques allowed us to address some of the\nlimitations of both the Cox regression approach and of standard parametric models when the goal is\nto predict long-term survival.
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