The Concordance Index decomposition: a measure for a deeper understanding of survival prediction models

02/28/2022
by   Abdallah Alabdallah, et al.
0

The Concordance Index (C-index) is a commonly used metric in Survival Analysis to evaluate how good a prediction model is. This paper proposes a decomposition of the C-Index into a weighted harmonic mean of two quantities: one for ranking observed events versus other observed events, and the other for ranking observed events versus censored cases. This decomposition allows a more fine-grained analysis of the pros and cons of survival prediction methods. The utility of the decomposition is demonstrated using three benchmark survival analysis models (Cox Proportional Hazard, Random Survival Forest, and Deep Adversarial Time-to-Event Network) together with a new variational generative neural-network-based method (SurVED), which is also proposed in this paper. The demonstration is done on four publicly available datasets with varying censoring levels. The analysis with the C-index decomposition shows that all methods essentially perform equally well when the censoring level is high because of the dominance of the term measuring the ranking of events versus censored cases. In contrast, some methods deteriorate when the censoring level decreases because they do not rank the events versus other events well.

READ FULL TEXT

page 1

page 2

page 3

page 4

research
06/06/2018

Learning to rank for censored survival data

Survival analysis is a type of semi-supervised ranking task where the ta...
research
04/16/2023

Using Geographic Location-based Public Health Features in Survival Analysis

Time elapsed till an event of interest is often modeled using the surviv...
research
09/07/2023

CenTime: Event-Conditional Modelling of Censoring in Survival Analysis

Survival analysis is a valuable tool for estimating the time until speci...
research
01/17/2018

Deep Neural Networks for Survival Analysis Based on a Multi-Task Framework

Survival analysis/time-to-event models are extremely useful as they can ...
research
08/14/2023

GRU-D-Weibull: A Novel Real-Time Individualized Endpoint Prediction

Accurate prediction models for individual-level endpoints and time-to-en...
research
02/23/2023

A Statistical Learning Take on the Concordance Index for Survival Analysis

The introduction of machine learning (ML) techniques to the field of sur...
research
01/13/2021

X-CAL: Explicit Calibration for Survival Analysis

Survival analysis models the distribution of time until an event of inte...

Please sign up or login with your details

Forgot password? Click here to reset