Penalized weighted proportional hazards model for robust variable selection and outlier detection.
Penalized weighted proportional hazards model for robust variable selection and outlier detection.
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DOI：
10.1002/sim.9424
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发表时间：
2022-07-30
影响因子：
2
通讯作者：
Halabi, Susan
中科院分区：
文献类型：
--
作者：
Luo, Bin;Gao, Xiaoli;Halabi, Susan
关键词：
由于这些患者可能具有不同的生物学或分子特征，因此可能对治疗产生不同的反应，因此识别异常反应或无反应是精密医学研究兴趣增加的领域。我们的动机源于一个临床试验的真实例子，我们对特殊前列腺癌应答者的特征感兴趣。我们研究了筛选生存结果的稀疏比例风险模型中的异常值检测和鲁棒回归问题。其主要思想是通过给危害函数分配一个单独的权重来模拟每个观测的不规则性。通过对模型参数和权重向量的对数变换施加lasso类型的惩罚，我们提出的方法能够同时进行变量选择和异常值检测。该优化问题可转化为典型的惩罚极大部分似然问题，易于实现。我们进一步扩展了所提出的方法，以处理由删减结果引起的潜在异常值掩蔽问题。通过广泛的仿真研究和在低维和高维环境下的真实数据分析，证明了所提出的估计器的性能。
Identifying exceptional responders or non-responders is an area of increased research interest in precision medicine as these patients may have different biological or molecular features and therefore may respond differently to therapies. Our motivation stems from a real example from a clinical trial where we are interested in characterizing exceptional prostate cancer responders. We investigate the outlier detection and robust regression problem in the sparse proportional hazards model for censored survival outcomes. The main idea is to model the irregularity of each observation by assigning an individual weight to the hazard function. By applying a LASSO-type penalty on both the model parameters and the log transformation of the weight vector, our proposed method is able to perform variable selection and outlier detection simultaneously. The optimization problem can be transformed to a typical penalized maximum partial likelihood problem and thus it is easy to implement. We further extend the proposed method to deal with the potential outlier masking problem caused by censored outcomes. The performance of the proposed estimator is demonstrated with extensive simulation studies and real data analyses in low-dimensional and high-dimensional settings.
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影响因子：
1.5
作者：
Halabi S;Dutta S;Wu Y;Liu A
通讯作者：
Liu A
DOI：
10.1158/1078-0432.ccr-13-3473
发表时间：
2015-04-01
期刊：
Clinical cancer research : an official journal of the American Association for Cancer Research
影响因子：
--
作者：
Herbst RS;Gandara DR;Hirsch FR;Redman MW;LeBlanc M;Mack PC;Schwartz LH;Vokes E;Ramalingam SS;Bradley JD;Sparks D;Zhou Y;Miwa C;Miller VA;Yelensky R;Li Y;Allen JD;Sigal EV;Wholley D;Sigman CC;Blumenthal GM;Malik S;Kelloff GJ;Abrams JS;Blanke CD;Papadimitrakopoulou VA
通讯作者：
Papadimitrakopoulou VA
DOI：
10.1214/10-aoas388
发表时间：
2011-01-01
期刊：
The annals of applied statistics
影响因子：
--
作者：
Breheny P;Huang J
通讯作者：
Huang J
影响因子：
78.8
作者：
Moscow, Jeffrey A.;Fojo, Tito;Schilsky, Richard L.
通讯作者：
Schilsky, Richard L.
影响因子：
1.7
作者：
Farcomeni, Alessio;Viviani, Sara
通讯作者：
Viviani, Sara