A generalized F mixture model for cure rate estimation
A generalized F mixture model for cure rate estimation
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DOI：
10.1002/(sici)1097-0258(19980430)17:8
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发表时间：
1998-04-30
影响因子：
2
通讯作者：
Denham, JW
中科院分区：
文献类型：
--
作者：
Peng, YW;Dear, KBG;Denham, JW
治愈率估计是淋巴瘤、乳腺癌等疾病临床试验中的重要问题，混合模型是主要的统计方法。在过去的十年中，混合模型在不同的分布，如指数，威布尔，对数正态和Gompertz，已被讨论和使用。然而，这些模型涉及比理想更强的分布假设，并且推断可能不足以偏离这些假设。本文利用广义F分布族提出了一种混合模型。虽然由于计算上的困难，这个族很少被使用，但它的优点是非常灵活，并且包括许多常用的分布作为特殊情况。广义F混合模型可以放松通常更强的分布假设，并允许分析师发现数据中的结构，否则可能会错过。通过将模型拟合到来自淋巴瘤患者长期随访的大规模临床试验的数据来说明这一点。计算问题的模型和模型选择方法进行了讨论。最大似然估计与其他分布下的混合模型得到的比较。(C)John Whey & Sons，Ltd.
Cure rate estimation is an important issue in clinical trials for diseases such as lymphoma and breast cancer and mixture models are the main statistical methods. In the last decade, mixture models under different distributions, such as exponential, Weibull, log-normal and Gompertz, have been discussed and used. However, these models involve stronger distributional assumptions than is desirable and inferences may not be robust to departures from these assumptions. In this paper, a mixture model is proposed using the generalized F distribution family. Although this family is seldom used because of computational difficulties, it has the advantage of being very flexible and including many commonly used distributions as special cases. The generalised F mixture model can relax the usual stronger distributional assumptions and allow the analyst to uncover structure in the data that might otherwise have been missed. This is illustrated by fitting the model to data from large-scale clinical trials with long follow-up of lymphoma patients. Computational problems with the model and model selection methods are discussed. Comparison of maximum likelihood estimates with those obtained from mixture models under other distributions are included. (C) 1998 John Whey & Sons, Ltd.