Joint modeling of progression-free and overall survival and computation of correlation measures
Joint modeling of progression-free and overall survival and computation of correlation measures
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DOI：
10.1002/sim.8295
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发表时间：
2019-09-30
影响因子：
2
通讯作者：
Rufibach, Kaspar
中科院分区：
文献类型：
--
作者：
Meller, Matthias;Beyersmann, Jan;Rufibach, Kaspar
在本文中，我们推导出的联合分布的无进展生存和总生存的转移概率的函数在一个多状态模型。不需要对copulae或潜在事件时间进行假设，并且允许模型是非马尔可夫的。根据联合分布，可以计算感兴趣的统计数据。作为一个例子，我们提供了封闭的公式和统计推断皮尔逊的相关系数之间的无进展生存和总生存的参数框架。这个例子的灵感来自于最近的方法来量化无进展生存期（肿瘤学3期试验中常见的主要结局）与总生存期之间的依赖性。我们通过提供统计推断的方法来补充这些方法，同时在一个更简约的建模框架内工作。我们的方法是完全通用的，可以应用于其他依赖的措施。我们还讨论了非参数推理的扩展。我们的分析结果说明了使用一个大型的随机临床试验在乳腺癌。
In this paper, we derive the joint distribution of progression-free and overall survival as a function of transition probabilities in a multistate model. No assumptions on copulae or latent event times are needed and the model is allowed to be non-Markov. From the joint distribution, statistics of interest can then be computed. As an example, we provide closed formulas and statistical inference for Pearson's correlation coefficient between progression-free and overall survival in a parametric framework. The example is inspired by recent approaches to quantify the dependence between progression-free survival, a common primary outcome in Phase 3 trials in oncology and overall survival. We complement these approaches by providing methods of statistical inference while at the same time working within a much more parsimonious modeling framework. Our approach is completely general and can be applied to other measures of dependence. We also discuss extensions to nonparametric inference. Our analytical results are illustrated using a large randomized clinical trial in breast cancer.