Literature DB >> 15456107

Modelling converging hazards in survival analysis.

Peter Barker1, Robin Henderson.   

Abstract

The Cox proportional hazards model has become the standard model for survival analysis. It is often seen as the null model in that "... explicit excuses are now needed to use different models" (Keiding, Proceedings of the XIXth International Biometric Conference, Cape Town, 1998). However, converging hazards also occur frequently in survival analysis. The Burr model, which may be derived as the marginal from a gamma frailty model, is one commonly used tool to model converging hazards. We outline this approach and introduce a mixed model which extends the Burr model and allows for both proportional and converging hazards. Although a semi-parametric model in its own right, we demonstrate how the mixed model can be derived via a gamma frailty interpretation, suggesting an E-M fitting procedure. We illustrate the modelling techniques using data on survival of hospice patients.

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Year:  2004        PMID: 15456107     DOI: 10.1023/b:lida.0000036392.68675.7f

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


  6 in total

1.  A local likelihood proportional hazards model for interval censored data.

Authors:  Rebecca A Betensky; Jane C Lindsey; Louise M Ryan; M P Wand
Journal:  Stat Med       Date:  2002-01-30       Impact factor: 2.373

2.  The method of expected number of deaths, 1786-1886-1986.

Authors:  N Keiding
Journal:  Int Stat Rev       Date:  1987-04       Impact factor: 2.217

3.  Semiparametric estimation of random effects using the Cox model based on the EM algorithm.

Authors:  J P Klein
Journal:  Biometrics       Date:  1992-09       Impact factor: 2.571

4.  Estimation of variance in Cox's regression model with shared gamma frailties.

Authors:  P K Andersen; J P Klein; K M Knudsen; R Tabanera y Palacios
Journal:  Biometrics       Date:  1997-12       Impact factor: 2.571

5.  The impact of heterogeneity in individual frailty on the dynamics of mortality.

Authors:  J W Vaupel; K G Manton; E Stallard
Journal:  Demography       Date:  1979-08

6.  Extent and determinants of error in doctors' prognoses in terminally ill patients: prospective cohort study.

Authors:  N A Christakis; E B Lamont
Journal:  BMJ       Date:  2000-02-19
  6 in total
  1 in total

1.  Mixtures of Polya trees for flexible spatial frailty survival modelling.

Authors:  Luping Zhao; Timothy E Hanson; Bradley P Carlin
Journal:  Biometrika       Date:  2009-06-01       Impact factor: 2.445

  1 in total

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