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Gradient and Likelihood Ratio Tests in Cure Rate Models

机译：固化速率模型中的梯度和似然比测试

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摘要

In some survival studies part of the population may be no longer subject to the event of interest. The called cure rate models take this fact into account. They have been extensively studied for several authors who have proposed extensions and applications in real lifetime data. Classic large sample tests are usually considered in these applications, especially the likelihood ratio. Recently? a new test called extit{gradient test} has been proposed. The gradient statistic shares the same asymptotic properties with the classic likelihood ratio and does not involve knowledge of the information matrix, which can be an advantage in survival models. Some simulation studies have been carried out to explore the behavior of the gradient test in finite samples and compare it with the classic tests in different models. However little is known about the properties of these large sample tests in finite sample for cure rate models. In this work we? performed a simulation study based on the promotion time model with Weibull distribution, to assess the performance of likelihood ratio and gradient tests in finite samples. An application is presented to illustrate the results.
机译：在一些生存期间，部分人口可能不再受到兴趣的事件。被称为固化速率模型考虑到这一事实。他们已被广泛地研究了在实际终身数据中提出的扩展和应用程序的若干作者研究。经典的大型样品测试通常考虑在这些应用中，尤其是似然比。最近？已经提出了一个名为 texit {梯度测试}的新测试。梯度统计与经典似然比共享相同的渐近性质，并且不涉及信息矩阵的知识，这可以是生存模型中的优势。已经进行了一些仿真研究，以探讨有限样本中梯度测试的行为，并将其与不同模型中的经典测试进行比较。然而，关于固化速率模型的有限样品中这些大型样品测试的性质很少。在这项工作中我们？基于Weibull分布的促销时间模型进行了模拟研究，以评估有限样本中似然比和梯度试验的性能。提出了应用程序以说明结果。

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