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(Unpublished)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Unpublished |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