24: An Application of Cox Mixture Models to End-Stage Kidney Disease

Music City Center 

Persons with end-stage kidney disease (ESKD) require undergoing dialysis or receiving a kidney transplant. Ethnic minority groups are disproportionately affected by ESKD in the United States. Due to the large range of ethnic and socio-economic groups in the United States, the assumption of proportional hazards (PH), which is required for Cox regression, could easily be violated. Hence, an investigation into the appropriate subpopulations which better satisfy the PH assumption is performed. Data from USRDS on patients with ESKD is analyzed. Cox mixture (CM) and deep Cox mixtures (DCM) models are utilized to identify and model latent subpopulations while modeling time to death. CM models were investigated to leverage the interpretability of typical Cox regression models with the increased performance of the mixture model. DCM is used for comparison. We found that CM and DCM models outperformed the Cox model in terms of Brier score and a time-dependent concordance index. The mixture models also show better performance for the smaller subpopulations, i.e., race/ethnicity, region of the United States, and rurality of the community the patient belongs.

Survival Analysis

Finite Mixture Model

Unsupervised Learning

Cox Regression

End-Stage Kidney Disease 

Section on Statistical Computing