Abstract:
Background: Kidney failure is an irreversible disease in which one or both kidneys
are unable to adequately filter waste products from the blood. Bi-variate time to event
endpoints may be correlated as they come from the same subject. However, classical
survival analysis assumes that survival times of different subjects are independent.
Thus, this study aimed to model time to right and left kidney failure of the patient at
Adama Hospital Medical College.
Methods: The data for this study was the chronic kidney disease patients under follow
up at Adama Hospital Medical College fromfrom 1st January 2015 to 30th January
2020 . The copulas are used to join the bi-variate time to event endpoints to the
one dimensional marginal distribution functions. The dependence between the time to
right and left kidney failure of the patient was quantified using the copula parameter,
while the effect of covariates were modeled using the parametric marginal survival
model. Akaike information criterion and Bayesian information criterion were used for
the models comparison.
Results: Of all 431 patients, 170 (39.4%) failed at least one kidney during the follow up period. The Log-logistic marginal distribution with Clayton copula model revealed
that sex of patients, hypertension, family history of kidney disease, obesity and age of
patients were the most significant factor that associated with time to kidney failure.
The dependence parameter was 1.4 (p-value < 0.0001).
Conclusions: The Log-logistic marginal distribution with Clayton copula model fit
the kidney failure dataset well. Being male, older adult, obese, hypertensive and having
family history of kidney disease were the most risk factors that leads to kidney failure.
There is the dependence between the time to right and left kidney failure of the patient.
