Semi-Explainable Deep Cox Model for Analyzing Survival Time in End Stage Kidney Diseases

Music City Center 

Employing deep cox model (DCM) allows for capturing the non-linear behavior in the survival time of a person with end-stage kidney disease (ESKD). However, this approach does not provide any explanation or determine the importance of different features. In this work, we combine DCM with Stochastic Variable Selection (SVS) to address this gap in the modeling. Furthermore, we study the effect of the sample size on the accuracy of the DCM compared to the traditional Cox Model using the Harrell C index. Our results indicates that gains only occur when the sample size is very large. The results of this analysis were consistent with variables selected using likelihood-based methods.

The research reported in this abstract was supported by South Dakota State University, AIM-AHEAD Coordinating Center, award number OTA-21-017, and was, in part, funded by the National Institutes of Health Agreement No. 1OT2OD032581. The work is solely the responsibility of the authors and does not necessarily represent the official view of AIM-AHEAD or the National Institutes of Health.

Deep Cox

Survival Analysis

End Stage Kidney Diseases 

Biometrics Section