Hurd, Alex
(2019)
Use of survival trees and random forests for modeling time to end stage renal disease.
Master's Thesis, University of Pittsburgh.
(Unpublished)
Abstract
Polycystic kidney disease (PKD) is an inherited disorder distinguished by kidney cyst growth and a corresponding decline in renal function. With minimal treatment options available, often times PKD progresses to chronic kidney disease (CKD) and eventually, end-stage renal disease (ESRD), a complete loss of kidney function. The Consortium for Radiologic Imaging Studies of Polycystic Kidney Disease (CRISP) is a longitudinal cohort study that has been studying participants with PKD since 2001, including analysis of imaging and biomarker data. CRISP studies have characterized renal cyst growth, as well as important prognostic biomarkers for CKD. Identifying PKD patients at greatest risk for developing ESRD can help design future studies.
In this study, survival methods, including decision trees, were applied to baseline CRISP data to determine which characteristics were associated with ESRD-free survival. Results of Cox proportional hazard, survival tree, and random survival forests models showed that height-adjusted total kidney volume (htTKV), total kidney cyst volume (TCV), and glomular filtration rate (GFR) had the strongest association with ESRD-free survival.
Public health significance: Determination of which characteristics are associated with ESRD-free survival can inform preventative treatments and programs for PKD patients and give better insight to medical professionals.
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Details
| Item Type: |
University of Pittsburgh ETD
|
| Status: |
Unpublished |
| Creators/Authors: |
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| ETD Committee: |
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| Date: |
24 June 2019 |
| Date Type: |
Publication |
| Defense Date: |
12 April 2019 |
| Approval Date: |
24 June 2019 |
| Submission Date: |
4 April 2019 |
| Access Restriction: |
5 year -- Restrict access to University of Pittsburgh for a period of 5 years. |
| Number of Pages: |
75 |
| Institution: |
University of Pittsburgh |
| Schools and Programs: |
School of Public Health > Biostatistics |
| Degree: |
MS - Master of Science |
| Thesis Type: |
Master's Thesis |
| Refereed: |
Yes |
| Uncontrolled Keywords: |
Survival trees |
| Date Deposited: |
24 Jun 2019 17:43 |
| Last Modified: |
01 May 2024 05:15 |
| URI: |
http://d-scholarship.pitt.edu/id/eprint/36308 |
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