Proportional Hazards Mixture Cure Models for End Stage Kidney Disease
Presentation Type
Poster
Student
Yes
Abstract
Survival probability predictions of all-cause mortality for persons with end-stage kidney disease can benefit healthcare workers, policymakers, and patients in making informed decisions. However, traditional survival techniques rely on the assumption that all individuals will eventually experience the event of interest rather than allowing for a subset of cured individuals. This paper makes use of a cure mixture model, a method that is able to assume there is a subset of people who are considered cured of the disease. This then allows for a more accurate interpretation of survivability by analyzing both the short-term and long-term effects of a set of variables. This work analyzes United States Renal Data System data composed of individuals living with end-stage kidney disease in the United States. We compare the outcomes of using either basic proportional hazards survival methods or mixture cure model methods. Within each method, covariates such as age, race, and transplant status are considered. Using the covariates, the mixture cure model provided both the comparative relative risks of individuals and the cure proportions. Our results show the disparities by race, age, and transplant status in the long-term and short-term survival of patients indicating the relative risks and the inferences that follow when considering a mixture cure model.
Acknowledgments: The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the U.S. government.
Funding Disclosure: This research was, in part, funded by the National Institutes of Health (NIH) Agreement No. 1OT2OD032581-01. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the NIH.
Start Date
2-6-2024 1:00 PM
End Date
2-6-2024 2:00 PM
Proportional Hazards Mixture Cure Models for End Stage Kidney Disease
Volstorff A
Survival probability predictions of all-cause mortality for persons with end-stage kidney disease can benefit healthcare workers, policymakers, and patients in making informed decisions. However, traditional survival techniques rely on the assumption that all individuals will eventually experience the event of interest rather than allowing for a subset of cured individuals. This paper makes use of a cure mixture model, a method that is able to assume there is a subset of people who are considered cured of the disease. This then allows for a more accurate interpretation of survivability by analyzing both the short-term and long-term effects of a set of variables. This work analyzes United States Renal Data System data composed of individuals living with end-stage kidney disease in the United States. We compare the outcomes of using either basic proportional hazards survival methods or mixture cure model methods. Within each method, covariates such as age, race, and transplant status are considered. Using the covariates, the mixture cure model provided both the comparative relative risks of individuals and the cure proportions. Our results show the disparities by race, age, and transplant status in the long-term and short-term survival of patients indicating the relative risks and the inferences that follow when considering a mixture cure model.
Acknowledgments: The data reported here have been supplied by the United States Renal Data System (USRDS). The interpretation and reporting of these data are the responsibility of the author(s) and in no way should be seen as an official policy or interpretation of the U.S. government.
Funding Disclosure: This research was, in part, funded by the National Institutes of Health (NIH) Agreement No. 1OT2OD032581-01. The views and conclusions contained in this document are those of the authors and should not be interpreted as representing the official policies, either expressed or implied, of the NIH.