Literature DB >> 12048865

Hierarchical-likelihood approach for mixed linear models with censored data.

Il Do Ha1, Youngjo Lee, Jae-Kee Song.   

Abstract

Mixed linear models describe the dependence via random effects in multivariate normal survival data. Recently they have received considerable attention in the biomedical literature. They model the conditional survival times, whereas the alternative frailty model uses the conditional hazard rate. We develop an inferential method for the mixed linear model via Lee and Nelder's (1996) hierarchical-likelihood (h-likelihood). Simulation and a practical example are presented to illustrate the new method.

Mesh:

Year:  2002        PMID: 12048865     DOI: 10.1023/a:1014839723865

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


  8 in total

1.  A linear mixed-effects model for multivariate censored data.

Authors:  W Pan; T A Louis
Journal:  Biometrics       Date:  2000-03       Impact factor: 2.571

2.  Linear regression for bivariate censored data via multiple imputation.

Authors:  W Pan; C Kooperberg
Journal:  Stat Med       Date:  1999-11-30       Impact factor: 2.373

3.  Best linear unbiased estimation and prediction under a selection model.

Authors:  C R Henderson
Journal:  Biometrics       Date:  1975-06       Impact factor: 2.571

4.  Modeling random effects for censored data by a multivariate normal regression model.

Authors:  J P Klein; C Pelz; M J Zhang
Journal:  Biometrics       Date:  1999-06       Impact factor: 2.571

5.  Mixed effects models with censored data with application to HIV RNA levels.

Authors:  J P Hughes
Journal:  Biometrics       Date:  1999-06       Impact factor: 2.571

6.  Random effects models with non-parametric priors.

Authors:  S M Butler; T A Louis
Journal:  Stat Med       Date:  1992 Oct-Nov       Impact factor: 2.373

7.  Mantel-Haenszel analyses of litter-matched time-to-response data, with modifications for recovery of interlitter information.

Authors:  N Mantel; N R Bohidar; J L Ciminera
Journal:  Cancer Res       Date:  1977-11       Impact factor: 12.701

8.  REML estimation for survival models with frailty.

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

  8 in total
  2 in total

1.  Multilevel mixed linear models for survival data.

Authors:  Il Do Ha; Youngjo Lee
Journal:  Lifetime Data Anal       Date:  2005-03       Impact factor: 1.588

2.  Frailty modelling for survival data from multi-centre clinical trials.

Authors:  Il Do Ha; Richard Sylvester; Catherine Legrand; Gilbert Mackenzie
Journal:  Stat Med       Date:  2011-05-12       Impact factor: 2.373

  2 in total

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