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

被引:48
|
作者
Lin, Ray S. [1 ]
Horn, Susan D. [2 ,3 ]
Hurdle, John F. [3 ]
Goldfarb-Rumyantzev, Alexander S. [4 ]
机构
[1] Stanford Univ, Stanford, CA 94305 USA
[2] Inst Clin Outcomes Res, Salt Lake City, UT USA
[3] Univ Utah, Salt Lake City, UT USA
[4] Harvard Univ, Beth Israel Deaconess Med Ctr, Sch Med, Div Nephrol, Boston, MA 02215 USA
关键词
Survival analysis; Kidney transplantation; Graft survival; Recipient survival; Logistic regression; Cox proportional hazards models; Artificial neural networks;
D O I
10.1016/j.jbi.2008.03.005
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
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. (c) 2008 Elsevier Inc. All rights reserved.
引用
收藏
页码:944 / 952
页数:9
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