An evaluation of penalised survival methods for developing prognostic models with rare events
An evaluation of penalised survival methods for developing prognostic models with rare events
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DOI：
10.1002/sim.4371
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发表时间：
2012-05-20
影响因子：
2
通讯作者：
Omar, R. Z.
中科院分区：
文献类型：
--
作者：
Ambler, G.;Seaman, S.;Omar, R. Z.
生存结果的预后模型通常是通过将标准生存回归模型（如Cox比例风险模型）拟合到代表性数据集来建立的。然而，如果数据集包含的事件很少，这些模型可能是不可靠的，如果疾病或感兴趣的事件很少，可能就是这种情况。具体的问题包括过于极端的预测，以及对低风险和高风险患者区分不清。本文的目的是评估三种现有的惩罚方法，这些方法已被提出以提高预测准确性。特别是ridge， lasso和garotte，它们使用惩罚的最大似是性来缩小系数估计，在某些情况下完全忽略预测因子，使用来自两个临床数据集的模拟数据进行评估。用这些方法得到的预测结果与用标准最大似然拟合的Cox模型的预测结果进行了比较。模拟结果表明，当事件很少时，使用最大似然拟合的Cox模型可能表现不佳，并且通过采用惩罚建模方法可以显著改进。脊法通常表现最好，但如果需要进行可变选择，建议使用套索。版权所有：John Wiley & Sons， Ltd。
Prognostic models for survival outcomes are often developed by fitting standard survival regression models, such as the Cox proportional hazards model, to representative datasets. However, these models can be unreliable if the datasets contain few events, which may be the case if either the disease or the event of interest is rare. Specific problems include predictions that are too extreme, and poor discrimination between low-risk and high-risk patients. The objective of this paper is to evaluate three existing penalised methods that have been proposed to improve predictive accuracy. In particular, ridge, lasso and the garotte, which use penalised maximum likelihood to shrink coefficient estimates and in some cases omit predictors entirely, are assessed using simulated data derived from two clinical datasets. The predictions obtained using these methods are compared with those from Cox models fitted using standard maximum likelihood. The simulation results suggest that Cox models fitted using maximum likelihood can perform poorly when there are few events, and that significant improvements are possible by taking a penalised modelling approach. The ridge method generally performed the best, although lasso is recommended if variable selection is required. Copyright (C) 2011 John Wiley & Sons, Ltd.