Investigating the prediction ability of survival models based on both clinical and omics data: two case studies
Investigating the prediction ability of survival models based on both clinical and omics data: two case studies
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DOI：
10.1002/sim.6246
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发表时间：
2014-12-01
影响因子：
2
通讯作者：
Boulesteix, Anne-Laure
中科院分区：
文献类型：
--
作者：
De Bin, Riccardo;Sauerbrei, Willi;Boulesteix, Anne-Laure
在生物医学文献中，已经基于临床数据或最近基于高通量分子数据（组学数据）开发了许多临床结果的预测模型。然而，基于这两种类型的数据的预测模型并不常见，尽管最近的一些研究表明，临床和分子信息的适当组合可能会产生具有更好预测能力的模型。这可能是由于组合具有不同特征和维度的联合收割机数据（表征不佳的高维组学数据，充分研究的低维临床数据）并不简单。在本文中，我们分析了两个公开的数据集相关的乳腺癌和神经母细胞瘤，分别，以显示一些可能的方法，联合收割机临床和组学数据到一个预测模型的时间到事件的结果。采用不同的策略和统计方法。根据不同的标准，包括判别能力的模型，计算验证数据集的结果进行了比较和讨论。版权所有（c）2014约翰威利父子有限公司
In biomedical literature, numerous prediction models for clinical outcomes have been developed based either on clinical data or, more recently, on high-throughput molecular data (omics data). Prediction models based on both types of data, however, are less common, although some recent studies suggest that a suitable combination of clinical and molecular information may lead to models with better predictive abilities. This is probably due to the fact that it is not straightforward to combine data with different characteristics and dimensions (poorly characterized high-dimensional omics data, well-investigated low-dimensional clinical data). In this paper, we analyze two publicly available datasets related to breast cancer and neuroblastoma, respectively, in order to show some possible ways to combine clinical and omics data into a prediction model of time-to-event outcome. Different strategies and statistical methods are exploited. The results are compared and discussed according to different criteria, including the discriminative ability of the models, computed on a validation dataset. Copyright (c) 2014 John Wiley & Sons, Ltd.