Facial reconstruction

Search LJMU Research Online

Browse Repository | Browse E-Theses

A practical divergence measure for survival distributions that can be estimated from Kaplan-Meier curves

Cox, TF and Czanner, G (2016) A practical divergence measure for survival distributions that can be estimated from Kaplan-Meier curves. STATISTICS IN MEDICINE, 35 (14). pp. 2406-2421. ISSN 0277-6715

[img]
Preview
Text
DIVERGENCE_V4.pdf - Accepted Version

Download (366kB) | Preview

Abstract

This paper introduces a new simple divergence measure between two survival distributions. For two groups of patients, the divergence measure between their associated survival distributions is based on the integral of the absolute difference in probabilities that a patient from one group dies at time t and a patient from the other group survives beyond time t and vice versa. In the case of non‐crossing hazard functions, the divergence measure is closely linked to the Harrell concordance index, C, the Mann–Whitney test statistic and the area under a receiver operating characteristic curve. The measure can be used in a dynamic way where the divergence between two survival distributions from time zero up to time t is calculated enabling real‐time monitoring of treatment differences. The divergence can be found for theoretical survival distributions or can be estimated non‐parametrically from survival data using Kaplan–Meier estimates of the survivor functions. The estimator of the divergence is shown to be generally unbiased and approximately normally distributed. For the case of proportional hazards, the constituent parts of the divergence measure can be used to assess the proportional hazards assumption. The use of the divergence measure is illustrated on the survival of pancreatic cancer patients. Copyright © 2016 John Wiley & Sons, Ltd.

Item Type: Article
Additional Information: This is the peer reviewed version of the following article: Cox, T. F., and Czanner, G. ( 2016) A practical divergence measure for survival distributions that can be estimated from Kaplan–Meier curves. Statist. Med., 35: 2406– 2421, which has been published in final form at https://doi.org/10.1002/sim.6868. This article may be used for non-commercial purposes in accordance with Wiley Terms and Conditions for Use of Self-Archived Versions.
Uncontrolled Keywords: 0104 Statistics, 1117 Public Health and Health Services
Subjects: Q Science > QA Mathematics
R Medicine > R Medicine (General)
Divisions: Applied Mathematics
Publisher: WILEY
Related URLs:
Date Deposited: 05 Nov 2019 14:58
Last Modified: 05 Nov 2019 15:00
DOI or Identification number: 10.1002/sim.6868
URI: http://researchonline.ljmu.ac.uk/id/eprint/11690

Actions (login required)

View Item View Item