Approximate tests for testing equality of two cumulative incidence functions of a competing risk

D. Dobler, Markus Pauly

Research output: Contribution to JournalArticleAcademicpeer-review

Abstract

In the context of a competing risks set-up, we discuss different inference procedures for testing equality of two cumulative incidence functions, where the data may be subject to independent right-censoring or left-truncation. To this end, we compare two-sample Kolmogorov–Smirnov- and Cramér–von Mises-type test statistics. Since, in general, their corresponding asymptotic limit distributions depend on unknown quantities, we utilize wild bootstrap resampling as well as approximation techniques to construct adequate test decisions. Here, the latter procedures are motivated from tests for heteroscedastic factorial designs but have not yet been proposed in the survival context. A simulation study shows the performance of all considered tests under various settings and finally a real data example about bloodstream infection during neutropenia is used to illustrate their application.

Original languageEnglish
Pages (from-to)1238-1258
Number of pages21
JournalStatistics
Volume51
Issue number6
DOIs
Publication statusPublished - 2017
Externally publishedYes

Keywords

  • Aalen–Johansen estimator
  • approximation techniques
  • competing risk
  • cumulative incidence function
  • wild bootstrap

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