Single and multiple time-point prediction models in kidney transplant outcome

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Title Single and multiple time-point prediction models in kidney transplant outcome
Publication Type thesis
School or College School of Medicine
Department Biomedical Informatics
Author Lin, Shih-jui
Date 2005-08
Description This study predicted graft and recipient survival in kidney transplantation based on the United States Renal Data System (USRDS) by regression models and artificial neural networks (ANNs). Four categories of models were examined: single time-point models (logistic regression and single-output ANNs) versus multiple time-point models (Cox models and multiple-output ANNs). The results showed that kidney transplant survival could be predicted by regression models and ANNs, with good prediction discrimination and model calibration. In AUC (area under the ROC curve) measurements, logistic regression ranged from 0.71 to 0.81; single-output ANNs from 0.72 to 0.82; Cox models from 0.65 to 0.78; multiple-output ANNs from 0.59 to 0.82. In Hosmer-Lemeshow test, p values for logistic regression and single-output ANNs were above 0.05 indicating good calibration, while p values for Cox models and multiple-output ANNs were in general below 0.05, indicating poor calibration. This study established for the first time that: 1) Single time-point models may be more appropriate than multiple time-point models if the predictors have significantly different effects on short-term versus long-term survival. 2) Logistic regression is able to achieve comparable performance as ANNs if there are no strong interactions or nonlinear relationships among the predictors and the outcomes. 3) Multiple-output ANNs may perform poorly when a large proportion of observations are censored. 4) Appropriate baseline survivor function should be specified for Cox models in order to achieve good model calibration when the clinical decision support is designed to provide exact predicted survival rates instead of a relative percentile in survival.
Type Text
Publisher University of Utah
Subject Logistic Regression; Time-point Models
Subject MESH Kidney Diseases; Kidney Transplantation
Dissertation Institution University of Utah
Dissertation Name MS
Language eng
Relation is Version of Digital reproduction of "Single and multiple time-point prediction models in kidney transplant outcome Spencer S. Eccles Health Sciences Library. Print version of "Single and multiple time-point prediction models in kidney transplant outcome". available at J. Willard Marriott Library Special Collection, RD14.5 2005 .L55.
Rights Management © Shih-jui Lin.
Format application/pdf
Format Medium application/pdf
Format Extent 665,333 bytes
Identifier undthes,4490
Source Original: University of Utah Spencer S. Eccles Health Sciences Library (no longer available).
Master File Extent 665,373 bytes
ARK ark:/87278/s6v40x35
Setname ir_etd
ID 191862
Reference URL https://collections.lib.utah.edu/ark:/87278/s6v40x35
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