Literature DB >> 9384653

A test for independence of competing risks with discrete failure times.

M Crowder1.   

Abstract

The identifiability problem in Competing Risks is well known. In particular, it implies that independent action or otherwise of the risks cannot be inferred from data alone. However, Crowder (1996) showed that, in the case of purely discrete failure times, an inference can be made. An algebraic criterion was derived which bears essentially on the independence in question. The condition was presented in a theoretical setting but it was pointed out that the quantities involved can be estimated from data and that, therefore, there is the potential to develop practical tests for the hypothesis of independence. It is the purpose of this paper to construct such a test.

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Year:  1997        PMID: 9384653     DOI: 10.1023/a:1009696830515

Source DB:  PubMed          Journal:  Lifetime Data Anal        ISSN: 1380-7870            Impact factor:   1.588


  5 in total

1.  A review and critique of some models used in competing risk analysis.

Authors:  M Gail
Journal:  Biometrics       Date:  1975-03       Impact factor: 2.571

2.  A nonidentifiability aspect of the problem of competing risks.

Authors:  A Tsiatis
Journal:  Proc Natl Acad Sci U S A       Date:  1975-01       Impact factor: 11.205

3.  Bounds for a joint distribution function with fixed sub-distribution functions: Application to competing risks.

Authors:  A V Peterson
Journal:  Proc Natl Acad Sci U S A       Date:  1976-01       Impact factor: 11.205

4.  On assessing independence of competing risks when failure times are discrete.

Authors:  M Crowder
Journal:  Lifetime Data Anal       Date:  1996       Impact factor: 1.588

5.  The analysis of failure times in the presence of competing risks.

Authors:  R L Prentice; J D Kalbfleisch; A V Peterson; N Flournoy; V T Farewell; N E Breslow
Journal:  Biometrics       Date:  1978-12       Impact factor: 2.571

  5 in total
  1 in total

1.  Inference on latent factor models for informative censoring.

Authors:  Francesco Ungolo; Edwin R van den Heuvel
Journal:  Stat Methods Med Res       Date:  2022-01-25       Impact factor: 3.021

  1 in total

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