Literature DB >> 10763563

Comparing sub-survival functions in a competing risks model.

K C Carriere1, S C Kochar.   

Abstract

In the competing risks literature, one usually compares whether two risks are equal or whether one is "more serious." In this paper, we propose tests for the equality of two competing risks against an ordered alternative specified by their sub-survival functions. These tests are naturally developed as extensions of those based on hazard rates and cumulative incidence functions. We note that the interpretation of the new test results is more direct compared to the situation when the hypotheses are framed in terms of their cumulative incidence functions. The proposed tests are of the Kolmogrov-Smirnov type, based on maximum differences between sub-survival functions. Our simulation studies indicate that they are excellent competitors of the existing tests, that are based mainly on differences between cumulative incidence functions. A numerical example will demonstrate the advantages of the proposed tests.

Mesh:

Year:  2000        PMID: 10763563     DOI: 10.1023/a:1009697802491

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


  2 in total

1.  Non-parametric inference for cumulative incidence functions in competing risks studies.

Authors:  D Y Lin
Journal:  Stat Med       Date:  1997-04-30       Impact factor: 2.373

2.  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

  2 in total
  3 in total

1.  Generalized supremum tests for the equality of cause specific hazard rates.

Authors:  Subhash C Kochar; K F Lam; Paul S F Yip
Journal:  Lifetime Data Anal       Date:  2002-09       Impact factor: 1.588

2.  Nonparametric analysis of bivariate gap time with competing risks.

Authors:  Chiung-Yu Huang; Chenguang Wang; Mei-Cheng Wang
Journal:  Biometrics       Date:  2016-03-18       Impact factor: 2.571

3.  Evaluating competing adverse and beneficial outcomes using a mixture model.

Authors:  Bryan Lau; Stephen R Cole; Richard D Moore; Stephen J Gange
Journal:  Stat Med       Date:  2008-09-20       Impact factor: 2.373

  3 in total

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