Time to Kidneys Failure Modeling in the Patients at Adama Hospital Medical College: Application of Copula Model

Abstract

Background: Kidney failure is a common public health problem around the world. The vast majority of kidney failure cases in Sub-Saharan African nations, including Ethiopia, go undetected and untreated, resulting in practically certain mortality cases. This study was aimed primarily to model the time to (right and left) kidneys failure in the patients at Adama Hospital Medical College using the copula model. Study design: A retrospective cohort study. Methods: The copula model was used to examine join time to the right and left kidneys failure in the patients by specifying the dependence between the failure times. We employed Weibull, Gompertz, and Log-logistic marginal baseline distributions with Clayton, Gumbel, and Joe Archimedean copula families. Results: This research comprised a total of 431 patients, out of which, 170 (39.4%) of the total patients failed at least one kidney during the follow-up period. Factors such as sex, age, family history of kidney disease, diabetes mellitus, hypertension, and obesity were found to be the most predictive variables for kidney failure in the patients. There was a 41 percent correlation between the patients’ time to the right and left kidneys failure. Conclusion: The patients’ kidney failure risk factors included being a male, older adult, obese, hypertensive, diabetic and also having a family history of kidney disease. The dependence between the patient’s time to the right and left kidneys failure was strong. The best statistical model for describing the kidney failure datasets was the log-logistic-Clayton Archimedean copula model.

Application of the Joint Frailty Copula Model for Analyzing Time to Relapse and Time to Death of Women with Cervical Cancer

“…This solidifies the idea that the time to bi-variate events are related. [25][26][27][28] The study also shows that there was a clustering (frailty) effect, which may have been influenced by the heterogeneity in the patients' countries of origin, given that those from the same region share similar risk factors for cervical cancer. 29,30 The study found that age significantly increased both the death and relapse rates for women with cervical cancer.…”

Parametric Distributions for Survival and Reliability Analyses, a Review and Historical Sketch

“…A vast array of applications is possible in survival/reliability analyses since the definition of "event" is arbitrary, not restricted to death. The events can be the appearance of tumor lesions and the onset/recurrence of diseases [5], progression of tumor [9], kidney failure [22], migraine [23], cognitive impairment [24], and others. The event could also be a good event, such as remission [5], return to work [25], marital pregnancy [26], and others.…”

Correlation analysis of diabetes based on Copula

“…It can fit the joint distribution of a variety of different distribution variables. At present, it is widely used in nonlinear relationships in finance, wind power, water conservancy, and other fields, but is still poorly used in the medical field ( 11 – 13 ). Espasandín-Domínguez et al.…”