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Hierarchical Likelihood Inference on Clustered Competing Risks Data

Christian, Nicholas J. (2011) Hierarchical Likelihood Inference on Clustered Competing Risks Data. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

Frailties models, an extension of the proportional hazards model, are used to model clustered survival data. In some situations there may be competing risks within a cluster. When this happens the basic frailty model is no longer appropriate. Depending on the purpose of the analysis, either the cause-specific hazard frailty model or the subhazard frailty model needs to be used. In this work, hierarchical likelihood (h-likelihood) methods are extended to provide a new method for fitting both types of competing risks frailty models. Methods for model selection as well as testing for covariate and clustering effects are discussed. Simulations show that in cases with little information, the h-likelihood method can perform better than the penalized partial likelihood method for estimating the subhazard frailty model. Additional simulations demonstrate that h-likelihood performs well when estimating the cause-specific hazard frailty model assuming both a univariate and bivariate frailty distribution. A real example from a breast cancer clinical trial is used to demonstrate using h-likelihood to fit both types of competing risks frailty models.Public health significance: When researchers have clustered survival data and the observations within those clusters can experience multiple types of events the popular proportional hazards model is no longer appropriate and can lead to biased estimates. For the results of a clinical study to be meaningful the estimated effects of treatments and other covariates needs to be accurate. H-likelihood methods are an alternative to existing procedures and can provide less bias and more accurate information which will ultimately lead to better patient care.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Christian, Nicholas J.njxian@gmail.com
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee ChairJeong, Jong-Hyeonjeong@nsabp.pitt.eduJJEONG
Committee MemberWahed, Abduswahed@pitt.eduWAHED
Committee MemberChang, Chung-Chou Hochangj@pitt.eduCHANGJ
Committee MemberAnderson, Stewartsja@pitt.eduSJA
Date: 23 September 2011
Date Type: Completion
Defense Date: 14 July 2011
Approval Date: 23 September 2011
Submission Date: 25 July 2011
Access Restriction: 5 year -- Restrict access to University of Pittsburgh for a period of 5 years.
Institution: University of Pittsburgh
Schools and Programs: Graduate School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: frailty models; hierarchical likelihood; competing risks
Other ID: http://etd.library.pitt.edu/ETD/available/etd-07252011-122200/, etd-07252011-122200
Date Deposited: 10 Nov 2011 19:53
Last Modified: 15 Nov 2016 13:46
URI: http://d-scholarship.pitt.edu/id/eprint/8599

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