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Classification Trees for Survival Data with Competing Risks

Callaghan, Fiona (2008) Classification Trees for Survival Data with Competing Risks. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Classification trees are the most popular tool for categorizing individuals into groups and subgroups based on particular outcomes of interest. To date, trees have not been developed for the competing risk situation where survival times are recorded and more than one outcome is possible. In this work we propose three classification trees to analyze survival data with multiple competing risk outcomes, using both univariate and multivariate techniques, respectively. After we describe the method used in growing and pruning the classification trees for competing risks, we demonstrate the performance with simulations in a variety of competing risk model configurations, and compare the competing risk trees to currently available tree-based methods. We also illustrate their use by analyzing survival data concerning patients who had end-stage liver disease and were on the waiting list to receive a liver transplant.Public Health Significance: Competing risks are common in longitudinal studies. The classification tree for competing risks will provide more accurate estimates of risk in distinct subpopulations than the current tree techniques can provide.


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Item Type: University of Pittsburgh ETD
Status: Unpublished
CreatorsEmailPitt UsernameORCID
Callaghan,; fiona.m.callaghan@gmail.comFMC2
ETD Committee:
TitleMemberEmail AddressPitt UsernameORCID
Committee CoChairChang, Chung-Chou
Committee CoChairAnderson, Stewartsja@nsabp.pitt.eduSJA
Committee MemberWahed, AbdusWahedA@edc.pitt.eduWAHED
Committee MemberWeissfeld, Lisalweis@pitt.eduLWEIS
Committee MemberRoberts, Markrobertsm@upmc.eduMROBERTS
Date: 25 June 2008
Date Type: Completion
Defense Date: 15 April 2008
Approval Date: 25 June 2008
Submission Date: 4 April 2008
Access Restriction: No restriction; Release the ETD for access worldwide immediately.
Institution: University of Pittsburgh
Schools and Programs: School of Public Health > Biostatistics
Degree: PhD - Doctor of Philosophy
Thesis Type: Doctoral Dissertation
Refereed: Yes
Uncontrolled Keywords: Between-node heterogeneity; Classification trees; Competing risks; Cumulative incidence function; Event-specific martingale residuals; Multiple endpoints; Multivariate methods; Regression trees; Within-node homogeneity
Other ID:, etd-04042008-125255
Date Deposited: 10 Nov 2011 19:34
Last Modified: 19 Dec 2016 14:35


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