Literature DB >> 9384635

Modelling conditional distributions in bivariate survival.

R Henderson1.   

Abstract

Conditional distributions for bivariate survival can be obtained via a model for the joint distribution, or, as has sometimes been suggested, by modelling the conditioned variable directly, with the conditioning variable included as a covariate. A quantitative comparison of estimated covariate effects and predictive distributions under the two approaches is given. The results are illustrated in a novel frailty application.

Mesh:

Year:  1996        PMID: 9384635     DOI: 10.1007/bf00128976

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


  11 in total

1.  Assessing gamma frailty models for clustered failure time data.

Authors:  J H Shih; T A Louis
Journal:  Lifetime Data Anal       Date:  1995       Impact factor: 1.588

Review 2.  Frailty models for survival data.

Authors:  P Hougaard
Journal:  Lifetime Data Anal       Date:  1995       Impact factor: 1.588

3.  Frailty models of manufacturing effects.

Authors:  J T Wassell; G W Kulczycki; E S Moyer
Journal:  Lifetime Data Anal       Date:  1995       Impact factor: 1.588

4.  Heterogeneity in survival analysis.

Authors:  O O Aalen
Journal:  Stat Med       Date:  1988-11       Impact factor: 2.373

Review 5.  Problems and prediction in survival-data analysis.

Authors:  R Henderson
Journal:  Stat Med       Date:  1995-01-30       Impact factor: 2.373

6.  REML estimation for survival models with frailty.

Authors:  C A McGilchrist
Journal:  Biometrics       Date:  1993-03       Impact factor: 2.571

7.  Generalizations and applications of frailty models for survival and event data.

Authors:  A Pickles; R Crouchley
Journal:  Stat Methods Med Res       Date:  1994       Impact factor: 3.021

8.  Effects of frailty in survival analysis.

Authors:  O O Aalen
Journal:  Stat Methods Med Res       Date:  1994       Impact factor: 3.021

9.  Heterogeneity models of disease susceptibility, with application to diabetic nephropathy.

Authors:  P Hougaard; P Myglegaard; K Borch-Johnsen
Journal:  Biometrics       Date:  1994-12       Impact factor: 2.571

10.  Analysis of dependent survival data applied to lifetimes of amalgam fillings.

Authors:  O O Aalen; E Bjertness; T Soønju
Journal:  Stat Med       Date:  1995-08-30       Impact factor: 2.373

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