JEL ratio test for independence of time to failure and cause of failure in competing risks

TL;DR

This paper introduces a new jackknife empirical likelihood ratio test for assessing the independence between failure time and cause in competing risks data, with theoretical derivation, simulation validation, and real data application.

Contribution

The paper develops a novel JEL ratio test for independence in competing risks, deriving its asymptotic distribution and demonstrating its effectiveness through simulations and real data examples.

Findings

The JEL test follows a chi-square distribution with one degree of freedom.

Simulation results show the test performs well in finite samples.

Application to real data illustrates the test's practical utility.

Abstract

In the present article, we propose jackknife empirical likelihood (JEL) ratio test for testing the independence of time to failure and cause of failure in competing risks data. We use U-statistic theory to derive the JEL ratio test. The asymptotic distribution of the test statistic is shown to be chi-square distribution with one degree of freedom. A Monte Carlo simulation study is carried out to assess the finite sample behaviour of the proposed test. The performance of proposed JEL test is compared with the test given in Dewan et al. (2004). Finally we illustrate our test procedure using various real data sets.