Liu, Yi
(2017)
Novel single and gene-based test procedures for large-scale bivariate time-to-event data, with application to a genetic study of AMD progression.
Doctoral Dissertation, University of Pittsburgh.
(Unpublished)
Abstract
Motivated by a genome-wide association study (GWAS) to discover risk variants for the progression of Age-related Macular Degeneration (AMD), I develop a computationally efficient copula-based score test, in which the association between bivariate progression times is explicitly modeled. Specifically, a two-step estimation approach with numerical derivatives to approximate the score function and information matrix is proposed. Both parametric and weakly parametric marginal distributions under the proportional hazards (PH) assumption are considered. Extensive simulation studies are conducted to evaluate the Type I error control and power performance of the proposed method. Further I extend this work to gene-based tests through the functional linear regression approach, which models the variants (within the same gene region) as a function of their physical positions. A robust variance estimator for bivariate time-to-event data under functional linear model is also proposed. Simulation studies are conducted to evaluate the Type I error control and power performance of the proposed method. Finally, we apply our method on a large randomized trial data set, Age-related Eye Disease Study (AREDS), to identify progression risk variants and gene regions for AMD progression. The top variants identified in the ARMS2 gene on chromosome 10 show differential progression profiles for different genetic groups, which are useful in characterizing and predicting the risk of progression for patients with moderate AMD.
Public health: significance: The application of the proposed methods jointly models the progression profiles in both eyes, which has not been done in any of the previous studies of AMD progression. The findings provide new insights about the genetic causes on AMD progression from single variants to genes, which will be critical for establishing novel and reliable predictive models of AMD progression to accurately identify high-risk patients at an early stage.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
|
| ETD Committee: |
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| Date: |
25 September 2017 |
| Date Type: |
Publication |
| Defense Date: |
28 July 2017 |
| Approval Date: |
25 September 2017 |
| Submission Date: |
25 July 2017 |
| Access Restriction: |
2 year -- Restrict access to University of Pittsburgh for a period of 2 years. |
| Number of Pages: |
86 |
| 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: |
Bivariate time-to-event;GWAS;Copula;score test |
| Date Deposited: |
25 Sep 2017 14:46 |
| Last Modified: |
01 Sep 2019 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/33121 |
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