Flexible recalibration of binary clinical prediction models
Flexible recalibration of binary clinical prediction models
复制标题
DOI：
10.1002/sim.5544
复制
发表时间：
2013-01-30
影响因子：
2
通讯作者：
Dalton, Jarrod E.
中科院分区：
文献类型：
--
作者：
Dalton, Jarrod E.
二元预测模型中的校准，即模型预测与观测结果之间的一致性，是评估模型对于表征未来数据中的风险的有效性的一个重要方面。1958年，D.R.Cox首次提出了一种用于评估模型校准的流行技术，该技术涉及对Logistic模型进行拟合，该模型包含一个截距和一个斜率系数，用于估计结果概率的logit；如果这些参数分别与0和1相差不大，则很明显是良好的校准。然而，在实践中，错误校准的形式有时可能会更加复杂。在这篇文章中，我们扩展了Cox校准模型，以允许更一般的参数化，并从这个更灵活的模型中推导出两个竞争模型之间的误校的相对度量。我们提供了一个使用来自美国医疗保健研究和质量机构的数据的实施示例。版权所有(C)2012 John Wiley&Sons，Ltd.
Calibration in binary prediction models, that is, the agreement between model predictions and observed outcomes, is an important aspect of assessing the models' utility for characterizing risk in future data. A popular technique for assessing model calibration first proposed by D. R. Cox in 1958 involves fitting a logistic model incorporating an intercept and a slope coefficient for the logit of the estimated probability of the outcome; good calibration is evident if these parameters do not appreciably differ from 0 and 1, respectively. However, in practice, the form of miscalibration may sometimes be more complicated. In this article, we expand the Cox calibration model to allow for more general parameterizations and derive a relative measure of miscalibration between two competing models from this more flexible model. We present an example implementation using data from the US Agency for Healthcare Research and Quality. Copyright (c) 2012 John Wiley & Sons, Ltd.