Skip Navigation

Biometrika 1986 73(3):671-678; doi:10.1093/biomet/73.3.671
© 1986 by Biometrika Trust
This Article
Right arrow Full Text (PDF)
Right arrow Alert me when this article is cited
Right arrow Alert me if a correction is posted
Services
Right arrow Email this article to a friend
Right arrow Similar articles in this journal
Right arrow Alert me to new issues of the journal
Right arrow Add to My Personal Archive
Right arrow Download to citation manager
Right arrowRequest Permissions
Google Scholar
Right arrow Articles by HOUGAARD, P.
Right arrow Search for Related Content
Social Bookmarking
 Add to CiteULike   Add to Connotea   Add to Del.icio.us  
What's this?

A class of multivanate failure time distributions

PHILIP HOUGAARD

Biostatistical Department, Novo Research Institute DK-2880 Bagsvaerd, Denmark

A class of continuous multivariate lifetime distributions is proposed. The dependence between individuals in a group is modelled by a group specific quantity, which can be interpreted as an unobserved covariate common to the individuals in the group and assumed to follow a positive stable distribution. It is possible to include covariates in the model and discuss whether the dependence is still present after specific covariates are taken into account. If the conditional hazards are proportional, then the hazards in the marginal distributions are also proportional, but with different constants of proportionality. Also the hazard for the minimum in a group is proportional to the marginal hazards. If the conditional distributions given the group quantity are Weibull then the marginal distributions are also Weibull. This class can be used to test the hypothesis of independence of litter mates in the proportional hazards model.

Key Words: Heterogeneity • Matched pairs • Positive stable distribution • Survival data • Weibull distribution


Add to CiteULike CiteULike   Add to Connotea Connotea   Add to Del.icio.us Del.icio.us    What's this?


This article has been cited by other articles:


Home page
BiometrikaHome page
D. V. Glidden
Pairwise dependence diagnostics for clustered failure-time data
Biometrika, June 1, 2007; 94(2): 371 - 385.
[Abstract] [Full Text] [PDF]


Home page
Clin TrialsHome page
Y. Zhao, P. M. Grambsch, and J. D. Neaton
A decision rule for sequential monitoring of clinical trials with a primary and supportive outcome
Clinical Trials, April 1, 2007; 4(2): 140 - 153.
[Abstract] [PDF]


Home page
Stat Methods Med ResHome page
G. Escarela, R. H Mena, and A. Castillo-Morales
A flexible class of parametric transition regression models based on copulas: application to poliomyelitis incidence
Statistical Methods in Medical Research, December 1, 2006; 15(6): 593 - 609.
[Abstract] [PDF]


Home page
J. Dent. Res.Home page
S.K. Chuang, T. Cai, C.W. Douglass, L.J. Wei, and T.B. Dodson
Frailty Approach for the Analysis of Clustered Failure Time Observations in Dental Research
J. Dent. Res., January 1, 2005; 84(1): 54 - 58.
[Abstract] [Full Text] [PDF]


Home page
Stat Methods Med ResHome page
J. Ritz and D. Spiegelman
Equivalence of conditional and marginal regression models for clustered and longitudinal data
Statistical Methods in Medical Research, August 1, 2004; 13(4): 309 - 323.
[Abstract] [PDF]


Home page
Stat Methods Med ResHome page
G. Escarela and J. F. Carriere
Fitting competing risks with an assumed copula
Statistical Methods in Medical Research, August 1, 2003; 12(4): 333 - 349.
[Abstract] [PDF]


Home page
Sociological Methods ResearchHome page
K. HUMPHREYS
The Latent Markov Chain with Multivariate Random Effects: An Evaluation of Instruments Measuring Labor Market Status in the British Household Panel Study
Sociological Methods Research, February 1, 1998; 26(3): 269 - 299.
[Abstract]


Home page
Stat Methods Med ResHome page
O. Aalen
Effects of frailty in survival analysis
Statistical Methods in Medical Research, October 1, 1994; 3(3): 227 - 243.
[Abstract] [PDF]


Home page
Stat Methods Med ResHome page
A. Pickles and R. Crouchlev
Generalizations and applications of frailty models for survival and event data
Statistical Methods in Medical Research, October 1, 1994; 3(3): 263 - 278.
[Abstract] [PDF]



Disclaimer:
Please note that abstracts for content published before 1996 were created through digital scanning and may therefore not exactly replicate the text of the original print issues. All efforts have been made to ensure accuracy, but the Publisher will not be held responsible for any remaining inaccuracies. If you require any further clarification, please contact our Customer Services Department.