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首页> 外文期刊>Computational Biology and Bioinformatics, IEEE/ACM Transactions on >A Hybrid Approach to Survival Model Building Using Integration of Clinical and Molecular Information in Censored Data
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A Hybrid Approach to Survival Model Building Using Integration of Clinical and Molecular Information in Censored Data

机译：在审查数据中整合临床和分子信息的生存模型建立的混合方法

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摘要

In medical society, the prognostic models, which use clinicopathologic features and predict prognosis after a certain treatment, have been externally validated and used in practice. In recent years, most research has focused on high dimensional genomic data and small sample sizes. Since clinically similar but molecularly heterogeneous tumors may produce different clinical outcomes, the combination of clinical and genomic information, which may be complementary, is crucial to improve the quality of prognostic predictions. However, there is a lack of an integrating scheme for clinic-genomic models due to the {rm P}gg{rm N} problem, in particular, for a parsimonious model. We propose a methodology to build a reduced yet accurate integrative model using a hybrid approach based on the Cox regression model, which uses several dimension reduction techniques, {rm L}_{2} penalized maximum likelihood estimation (PMLE), and resampling methods to tackle the problem. The predictive accuracy of the modeling approach is assessed by several metrics via an independent and thorough scheme to compare competing methods. In breast cancer data studies on a metastasis and death event, we show that the proposed methodology can improve prediction accuracy and build a final model with a hybrid signature that is parsimonious when integrating both types of variables.
机译：在医学社会中，利用临床病理特征并预测某种治疗后的预后的预后模型已在外部得到验证并在实践中使用。近年来，大多数研究都集中在高维基因组数据和小样本量上。由于临床上相似但分子异质性的肿瘤可能产生不同的临床结果，因此临床和基因组信息的组合（可能是互补的）对于提高预后预测的质量至关重要。然而，由于{rm P} gg {rm N}问题，特别是对于简约模型，缺乏针对临床基因组模型的整合方案。我们提出一种方法，使用基于Cox回归模型的混合方法来构建精简而准确的集成模型，该方法使用了几种降维技术，{rm L} _ {2}惩罚最大似然估计（PMLE）和重采样方法来解决问题。建模方法的预测准确性是通过一个独立，彻底的方案通过几个指标来评估的，以比较竞争方法。在有关转移和死亡事件的乳腺癌数据研究中，我们表明，所提出的方法可以提高预测准确性，并建立具有混合签名的最终模型，当整合两种类型的变量时，该签名是简约的。

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