A non-parametric test for testing independence between time to failure and cause of failure of discrete competing risks data

TL;DR

This paper introduces a non-parametric U-statistics based test to assess independence between failure time and cause in discrete competing risks data, addressing a gap in existing methods.

Contribution

It proposes a novel non-parametric test for independence in discrete competing risks data, with derived asymptotic distribution and demonstrated effectiveness through simulations and real data applications.

Findings

Test has good finite sample performance in simulations.

Method successfully applied to real datasets on oral cancer and drug pregnancies.

Asymptotic distribution derived for the proposed test statistic.

Abstract

Competing risks data with discrete lifetime comes up in practice. However, only limited literature exists for such data. In this paper, we propose a non-parametric test based on U-statistics for testing independence of time to failure and cause of failure of competing risks data when the lifetime is a discrete random variable. Asymptotic distribution of the proposed test statistic is derived. An extensive Monte Carlo simulation study is conducted to assess the finite sample performance of the proposed test. The flexibility of the testing procedure is illustrated using real data sets on oral cancer patients and drug exposed pregnancies.