Document Detail


Single and multiple time-point prediction models in kidney transplant outcomes.
MedLine Citation:
PMID:  18442951     Owner:  NLM     Status:  MEDLINE    
Abstract/OtherAbstract:
This study predicted graft and recipient survival in kidney transplantation based on the USRDS dataset by regression models and artificial neural networks (ANNs). We examined single time-point models (logistic regression and single-output ANNs) versus multiple time-point models (Cox models and multiple-output ANNs). These models in general achieved good prediction discrimination (AUC up to 0.82) and model calibration. This study found that: (1) Single time-point and multiple time-point models can achieve comparable AUC, except for multiple-output ANNs, which may perform poorly when a large proportion of observations are censored, (2) Logistic regression is able to achieve comparable performance as ANNs if there are no strong interactions or non-linear relationships among the predictors and the outcomes, (3) Time-varying effects must be modeled explicitly in Cox models when predictors have significantly different effects on short-term versus long-term survival, and (4) Appropriate baseline survivor function should be specified for Cox models to achieve good model calibration, especially when clinical decision support is designed to provide exact predicted survival rates.
Authors:
Ray S Lin; Susan D Horn; John F Hurdle; Alexander S Goldfarb-Rumyantzev
Publication Detail:
Type:  Journal Article     Date:  2008-03-22
Journal Detail:
Title:  Journal of biomedical informatics     Volume:  41     ISSN:  1532-0480     ISO Abbreviation:  J Biomed Inform     Publication Date:  2008 Dec 
Date Detail:
Created Date:  2008-11-17     Completed Date:  2009-02-12     Revised Date:  -    
Medline Journal Info:
Nlm Unique ID:  100970413     Medline TA:  J Biomed Inform     Country:  United States    
Other Details:
Languages:  eng     Pagination:  944-52     Citation Subset:  IM    
Affiliation:
Biomedical Informatics, Stanford University, MSOB X-215, 251 Campus Drive, Stanford, CA 94305-5479, USA. raylin@stanford.edu
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MeSH Terms
Descriptor/Qualifier:
Humans
Kidney Transplantation*
Logistic Models
Models, Theoretical*
Proportional Hazards Models

From MEDLINE®/PubMed®, a database of the U.S. National Library of Medicine


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