REGRESSION MODELING STRATEGIES FOR IMPROVED PROGNOSTIC PREDICTION
REGRESSION MODELING STRATEGIES FOR IMPROVED PROGNOSTIC PREDICTION
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DOI：
10.1002/sim.4780030207
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发表时间：
1984-01-01
影响因子：
2
通讯作者：
ROSATI, RA
中科院分区：
文献类型：
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作者：
HARRELL, FE;LEE, KL;ROSATI, RA
回归模型，如COX比例风险模型，在建模和估计各种疾病患者的预后方面得到了越来越多的使用。许多应用涉及使用相对较小的患者样本来建模的大量变量。在分析预后时，过度拟合和识别重要协变量的问题会加剧，因为模型的准确性更多地是事件数量的函数，而不是样本大小的函数。我们使用预测判别的一般指数来衡量在不同大小的训练样本上开发的模型在怀疑患有冠状动脉疾病的患者的独立测试样本中预测生存的能力。我们比较了三种模型拟合方法：(1)标准逐步变量选择，(2)不完全主成分回归，(3)从变量簇中提取临床指标后的COX模型回归。我们发现，使用主成分的回归在测试样本中提供了更好的预测，而使用指数的回归提供了几乎与主成分模型一样好的易于解释的模型。标准变量选择有许多不足之处。
Regression models such as the Cox proportional hazards model have had increasing use in modelling and estimating the prognosis of patients with a variety of diseases. Many applications involve a large number of variables to be modelled using a relatively small patient sample. Problems of overfitting and of identifying important covariates are exacerbated in analysing prognosis because the accuracy of a model is more a function of the number of events than of the sample size.We used a general index of predictive discrimination to measure the ability of a model developed on training samples of varying sizes to predict survival in an independent test sample of patients suspected of having coronary artery disease. We compared three methods of model fitting: (1) standard ‘step‐up’ variable selection, (2) incomplete principal components regression, and (3) Cox model regression after developing clinical indices from variable clusters. We found regression using principal components to offer superior predictions in the test sample, whereas regression using indices offers easily interpretable models nearly as good as the principal components models. Standard variable selection has a number of deficiencies.