Literature DB >> 15293627

Two-sample statistics for testing the equality of survival functions against improper semi-parametric accelerated failure time alternatives: an application to the analysis of a breast cancer clinical trial.

Philippe Broët1, Alexander Tsodikov, Yann De Rycke, Thierry Moreau.   

Abstract

This paper presents two-sample statistics suited for testing equality of survival functions against improper semi-parametric accelerated failure time alternatives. These tests are designed for comparing either the short- or the long-term effect of a prognostic factor, or both. These statistics are obtained as partial likelihood score statistics from a time-dependent Cox model. As a consequence, the proposed tests can be very easily implemented using widely available software. A breast cancer clinical trial is presented as an example to demonstrate the utility of the proposed tests.

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Year:  2004        PMID: 15293627      PMCID: PMC2992553          DOI: 10.1023/b:lida.0000030198.06968.98

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


  12 in total

1.  A semiparametric approach for the two-sample comparison of survival times with long-term survivors.

Authors:  P Broët; Y De Rycke; P Tubert-Bitter; J Lellouch; B Asselain; T Moreau
Journal:  Biometrics       Date:  2001-09       Impact factor: 2.571

2.  Bayesian semiparametric models for survival data with a cure fraction.

Authors:  J G Ibrahim; M H Chen; D Sinha
Journal:  Biometrics       Date:  2001-06       Impact factor: 2.571

3.  Semi-parametric models of long- and short-term survival: an application to the analysis of breast cancer survival in Utah by age and stage.

Authors:  A Tsodikov
Journal:  Stat Med       Date:  2002-03-30       Impact factor: 2.373

4.  A comparison of tests of the difference in the proportion of patients who are cured.

Authors:  R Sposto; H N Sather; S A Baker
Journal:  Biometrics       Date:  1992-03       Impact factor: 2.571

5.  Parametric versus non-parametric methods for estimating cure rates based on censored survival data.

Authors:  A B Cantor; J J Shuster
Journal:  Stat Med       Date:  1992-05       Impact factor: 2.373

6.  Nonparametric estimation and testing in a cure model.

Authors:  E M Laska; M J Meisner
Journal:  Biometrics       Date:  1992-12       Impact factor: 2.571

7.  Semi-parametric estimation in failure time mixture models.

Authors:  J M Taylor
Journal:  Biometrics       Date:  1995-09       Impact factor: 2.571

8.  A proportional hazards model taking account of long-term survivors.

Authors:  A Tsodikov
Journal:  Biometrics       Date:  1998-12       Impact factor: 2.571

9.  A linear rank test for use when the main interest is in differences in cure rates.

Authors:  R J Gray; A A Tsiatis
Journal:  Biometrics       Date:  1989-09       Impact factor: 2.571

10.  Covariance analysis of censored survival data.

Authors:  N Breslow
Journal:  Biometrics       Date:  1974-03       Impact factor: 2.571

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