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A TREE-STRUCTURED SURVIVAL MODEL WITH INCOMPLETE ANDTIME-DEPENDENT COVARIATES: ILLUSTRATIONS USING TYPE 1DIABETES DATA

Yu, Shui (2007) A TREE-STRUCTURED SURVIVAL MODEL WITH INCOMPLETE ANDTIME-DEPENDENT COVARIATES: ILLUSTRATIONS USING TYPE 1DIABETES DATA. Doctoral Dissertation, University of Pittsburgh. (Unpublished)

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Abstract

A tree-structured recursive partitioning algorithm is adapted for censored survival analysis with incomplete and time-dependent covariates. The only assumptions required for this method are those that guarantee identifiability of the conditional distribution of the survival time given the covariates, providing broad applicability. The method also provides personalized prognosis. A conditional incremental imputation procedure, which does not depend on any model assumptions, is implemented to impute missing covariate values. These novel algorithms are applied to assess the role of islet antibodies (ICAs) as predictive markers for Type 1 diabetes mellitus (T1DM) progression in a longitudinal study of 300 first-degree relatives (FDRs) that were consecutively enrolled between 1977 through 2001 from the Children's Hospital of Pittsburgh Registry. Results provide evidence that ICAs predict a more rapid progression to insulin-requiring diabetes in GAD65 positive relatives. A cross-validation study confirms the findings. Islet-cell antibodies (ICAs) are important markers of Type 1 diabetes. The issue regarding whether or not the measurement of ICAs should be completely replaced by biochemical markers detecting islet autoantibodies (AAs) for the prediction of T1DM has been the subject of endless debates. Our conclusion that ICAs should remain part of the assessment of T1DM risk is of great public health significance.


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Details

Item Type: University of Pittsburgh ETD
Status: Unpublished
Creators/Authors:
CreatorsEmailPitt UsernameORCID
Yu, Shuiys6202@gmail.com
Date: 15 February 2007
Date Type: Completion
Defense Date: 15 November 2006
Approval Date: 15 February 2007
Submission Date: 29 November 2006
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: survival analyisis; tree model; type 1 diabetes
Other ID: http://etd.library.pitt.edu/ETD/available/etd-11292006-112014/, etd-11292006-112014
Date Deposited: 10 Nov 2011 20:06
Last Modified: 15 Nov 2016 13:52
URI: http://d-scholarship.pitt.edu/id/eprint/9849

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