1 Applied Statistics, ISRT, University of Dhaka, Dhaka 1000, Bangladesh.
2 Department of Statistics, University of Warwick, Coventry, UK.
Stat Methods Med Res. 2019 Mar;28(3):937-952. doi: 10.1177/0962280217739522. Epub 2017 Nov 9.
We develop variable selection approaches for accelerated failure time models, consisting of a group of algorithms based on a synthesis of two widely used techniques in the area of variable selection for survival analysis-the Buckley-James method and the Dantzig selector. Two algorithms are based on proposed modified Buckley-James estimating methods that are designed for high-dimensional censored data. Another two algorithms are based on a two-stage weighted Dantzig selector method where weights are obtained from the two proposed synthesis-based algorithms. The methods are easy to understand and they perform estimation and variable selection simultaneously. Furthermore, they can deal with collinearity among the covariates. We conducted several simulation studies and one empirical analysis with a microarray dataset; these studies demonstrated satisfactory variable selection performance. In addition, the microarray data analysis shows the methods performing similarly to three other correlation-based greedy variable selection techniques in the literature-sure independence screening, tilted correlation screening (TCS), and partial correlation (PC) simple. This empirical study also found that the sure independence screening technique considerably improves the performance of most of the proposed methods.
我们开发了一种适用于加速失效时间模型的变量选择方法，该方法由一组算法组成，这些算法综合了生存分析中两种广泛使用的变量选择技术——Buckley-James 方法和 Dantzig 选择器。其中两种算法基于针对高维删失数据设计的改进的 Buckley-James 估计方法。另外两种算法基于两阶段加权 Dantzig 选择器方法，其中权重来自于两种基于合成的算法。这些方法易于理解，并且可以同时进行估计和变量选择。此外，它们还可以处理协变量之间的共线性。我们进行了多项模拟研究和一项基于微阵列数据集的实证分析，这些研究表明了这些方法具有令人满意的变量选择性能。此外，微阵列数据分析表明，这些方法的表现与文献中的其他三种基于相关性的贪婪变量选择技术（Sure Independence Screening、Tilted Correlation Screening（TCS）和 Partial Correlation（PC）Simple）相似。这项实证研究还发现，Sure Independence Screening 技术显著提高了大多数所提出方法的性能。
