Literature DB >> 9385098

Frailty models of manufacturing effects.

J T Wassell1, G W Kulczycki, E S Moyer.   

Abstract

The median service lifetime of respiratory safety devices produced by different manufacturers is determined using frailty models to account for unobserved differences in manufacturing processes and raw materials. The gamma and positive stable frailty distributions are used to obtain survival distribution estimates when the baseline hazard is assumed to be Weibull. Frailty distributions are compared using laboratory test data of the failure times for 104 respirator cartridges produced by 10 different manufacturers tested with three different challenge agents. Likelihood ratio tests indicate that both frailty models provide a significant improvement over a Weibull model assuming independence. Results are compared to fixed effects approaches for analysis of this data.

Mesh:

Year:  1995        PMID: 9385098     DOI: 10.1007/bf00985767

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


  3 in total

1.  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

2.  Bounds on net survival probabilities for dependent competing risks.

Authors:  J P Klein; M L Moeschberger
Journal:  Biometrics       Date:  1988-06       Impact factor: 2.571

3.  A bivariate survival model with modified gamma frailty for assessing the impact of interventions.

Authors:  J T Wassell; M L Moeschberger
Journal:  Stat Med       Date:  1993-02       Impact factor: 2.373

  3 in total
  3 in total

1.  A Weibull-based score test for heterogeneity.

Authors:  A C Kimber
Journal:  Lifetime Data Anal       Date:  1996       Impact factor: 1.588

2.  Modelling conditional distributions in bivariate survival.

Authors:  R Henderson
Journal:  Lifetime Data Anal       Date:  1996       Impact factor: 1.588

3.  A positive stable frailty model for clustered failure time data with covariate-dependent frailty.

Authors:  Dandan Liu; John D Kalbfleisch; Douglas E Schaubel
Journal:  Biometrics       Date:  2011-03       Impact factor: 2.571

  3 in total

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