On a measure of information gain for regression models in survival analysis
On a measure of information gain for regression models in survival analysis
复制标题
生存分析中回归模型信息增益的衡量
DOI：
10.1080/02664763.2014.926596
复制
发表时间：
2014
影响因子：
1.5
通讯作者：
J. Stare
中科院分区：
文献类型：
--
作者：
D. Maucort;Pascal Roy;J. Stare
关于生存分析中预测能力的度量的论文已经看到了它们的删失独立性，或者它们的估计在删失下是无偏的，这是最重要的属性。我们认为，这个属性被错误地理解了。讨论了所谓的信息增益测度，指出如果所有大于给定时间τ的值都被删失，则不可能得到无偏估计。这是因为在τ之前截尾与在τ之后截尾具有不同的效果。这种τ通常通过研究设计引入。独立性只能在模型在τ之后有效的假设下实现，这是不可能验证的。但是，如果人们愿意做出这样的假设，我们建议使用多重插补来获得一致的估计。我们进一步表明，删失对考克斯模型比参数模型的测量估计有不同的影响，我们分别讨论它们。我们还对该措施的使用提出了一些警告，特别是在比较本质上不同的模型时。
Papers dealing with measures of predictive power in survival analysis have seen their independence of censoring, or their estimates being unbiased under censoring, as the most important property. We argue that this property has been wrongly understood. Discussing the so-called measure of information gain, we point out that we cannot have unbiased estimates if all values, greater than a given time τ, are censored. This is due to the fact that censoring before τ has a different effect than censoring after τ. Such τ is often introduced by design of a study. Independence can only be achieved under the assumption of the model being valid after τ, which is impossible to verify. But if one is willing to make such an assumption, we suggest using multiple imputation to obtain a consistent estimate. We further show that censoring has different effects on the estimation of the measure for the Cox model than for parametric models, and we discuss them separately. We also give some warnings about the usage of the measure, especially when it comes to comparing essentially different models.