Survival analysis with high-dimensional covariates.
Survival analysis with high-dimensional covariates.
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DOI：
10.1177/0962280209105024
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发表时间：
2010-02
影响因子：
2.3
通讯作者：
Tibshirani R
中科院分区：
文献类型：
--
作者：
Witten DM;Tibshirani R
近年来，生物医学技术的突破导致了大量的数据，其中特征的数量(例如，可以进行表达测量的基因)超过了观察的数量(例如，患者)。有时，同样的观察也可以得出生存结果。在这种情况下，人们可能感兴趣的是(A)识别与生存相关的特征(在单变量意义上)，以及(B)开发特征与生存之间的关系的多变量模型，该模型可用于在新的观察中预测生存。由于这些数据的高维性，大多数经典的统计方法不能直接应用于生存分析。在这里，我们回顾了文献中解决这两个问题的一些方法。
In recent years, breakthroughs in biomedical technology have led to a wealth of data in which the number of features (for instance, genes on which expression measurements are available) exceeds the number of observations (e.g. patients). Sometimes survival outcomes are also available for those same observations. In this case, one might be interested in (a) identifying features that are associated with survival (in a univariate sense), and (b) developing a multivariate model for the relationship between the features and survival that can be used to predict survival in a new observation. Due to the high dimensionality of this data, most classical statistical methods for survival analysis cannot be applied directly. Here, we review a number of methods from the literature that address these two problems.