A Weibull multi-state model for the dependence of progression-free survival and overall survival.
A Weibull multi-state model for the dependence of progression-free survival and overall survival.
复制标题
DOI：
10.1002/sim.6501
复制
发表时间：
2015-07-30
影响因子：
2
通讯作者：
Zhang, Qiang
中科院分区：
文献类型：
--
作者：
Li, Yimei;Zhang, Qiang
在肿瘤学临床试验中，总生存期（OS）、进展时间（TTP）和无进展生存期（PFS）是三个常用的终点。它们之间的经验相关性已经发表在不同的癌症上，但描述依赖结构的统计模型是有限的。最近Fleischer等人提出了一种统计模型，该模型在数学上易于处理，并显示出一些灵活性，可以基于指数分布的假设，以一种现实的方式描述依赖关系。本文旨在将他们的模型扩展到更灵活的威布尔分布。我们推导了不同生存结果之间的理论相关性，以及模型诱导的总生存分布。采用最大似然法估计模型参数，并通过绘制OS的估计生存曲线和观察生存曲线来评估拟合优度。我们将该方法应用于三个癌症临床试验。在非小细胞肺癌试验中，指数模型和威布尔模型都对数据提供了充分的拟合，两种模型下估计的相关性非常相似。在前列腺癌试验和喉癌试验中，威布尔模型比指数模型表现出优势，并产生了更大的估计相关性。仿真表明，所提出的威布尔模型对于来自一系列分布的数据具有鲁棒性。
In oncology clinical trials, overall survival (OS), time to progression (TTP) and progression free survival (PFS) are three commonly used endpoints. Empirical correlations among them have been published for different cancers, but statistical models describing the dependence structures are limited. Recently Fleischer et al. proposed a statistical model that is mathematically tractable and shows some flexibility to describe the dependencies in a realistic way, based on the assumption of exponential distributions. This paper aims to extend their model to the more flexible Weibull distribution. We derived theoretical correlations among different survival outcomes, as well as the distribution of overall survival induced by the model. Model parameters were estimated by the maximum likelihood method and the goodness of fit was assessed by plotting estimated vs. observed survival curves for OS. We applied the method to three cancer clinical trials. In the non-small-cell lung cancer trial, both the exponential and the Weibull models provided an adequate fit to the data and the estimated correlations were very similar under both models. In the prostate cancer trial and the laryngeal cancer trial, the Weibull model exhibited advantages over the exponential model and yielded larger estimated correlations. Simulations suggested that the proposed Weibull model is robust for data generated from a range of distributions.
登录
查看更多内容
影响因子：
45.3
作者：
Buyse, Marc;Burzykowski, Tomasz;Piedbois, Pascal
通讯作者：
Piedbois, Pascal
影响因子：
8.8
作者：
Sherrill, B.;Amonkar, M.;Cuzick, J.
通讯作者：
Cuzick, J.
影响因子：
2.2
作者：
Stone, Andrew;Wheeler, Catherine;Barge, Alan
通讯作者：
Barge, Alan
影响因子：
45.3
作者：
Gore, Elizabeth M.;Bae, Kyounghwa;Choy, Hak
通讯作者：
Choy, Hak
影响因子：
2
作者：
SHANNO, DF
通讯作者：
SHANNO, DF