Comparing nonnested Cox models
Comparing nonnested Cox models
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DOI：
10.1093/biomet/89.3.635
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发表时间：
2002-09-01
期刊：
影响因子：
2.7
通讯作者：
Fine, JP
中科院分区：
文献类型：
--
作者：
Fine, JP
在一般情况下，我们得到了偏似然比的极限分布。拟合出的乘性风险模型可能是非嵌套的和错误指定的。真正的模型并不假定包含考虑中的任何一个模型。零假设是模型在应用于秩似然的Kullback-Leibler度量中是等距离的。对于更接近真实情况的模型，统计数据是一致的。它的分布取决于未知的数据生成机制。提出了一种非嵌套比较的序贯检验方法，无论模型是什么，该方法都是有效的。这涉及到一个新的统计量，它是关于拟合模型的等价性的，它与部分似然性是分开的。该方法在模型评估中具有重要的应用价值。仿真和实例证明了该方法在选择协变量的函数形式和相对风险方面的有效性。
We derive the limiting distribution of the partial likelihood ratio under general conditions. The multiplicative hazards models being fitted may be nonnested and misspecified. The true model is not assumed to contain either model under consideration. The null hypothesis is that the models are equidistant in Kullback-Leibler metric applied to the rank likelihood. The statistic is consistent for the model which is closer to the truth. Its distribution depends on the unknown data-generating mechanism. A sequential testing procedure is proposed for nonnested comparisons which is valid regardless of the true model. This involves a novel statistic for the equality of the fitted models which is separate from the partial likelihood. The methodology has important applications in model assessment. Simulations and a real example demonstrate its utility in selecting the functional forms of covariates and relative risks.