Survival prediction from clinico-genomic models--a comparative study.
Survival prediction from clinico-genomic models--a comparative study.
复制标题
临床基因组模型的生存预测 - 一项比较研究。
DOI：
10.1186/1471-2105-10-413
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发表时间：
2009-12-13
影响因子：
3
通讯作者：
Borgan O
中科院分区：
文献类型：
--
作者：
Bøvelstad HM;Nygård S;Borgan O
从高维基因组数据进行生存预测是当今医学研究中的一个活跃领域。大多数提出的预测方法仅利用基因组数据，而不考虑通常可用且已知具有预测价值的已建立的临床协变量。最近的研究表明，结合临床和基因组信息可能会改善预测，但缺乏系统的研究。此外，对于广泛使用的考克斯回归模型，如何处理这种组合模型并不明显。我们提出了一种在基于考克斯回归模型的临床基因组预测模型中将经典临床协变量与基因组数据相结合的方法。联合收割机。通过同时使用两种类型的协变量，但仅对高维基因组变量应用降维来获得预测模型。我们描述了如何可以做到这一点的七个著名的预测方法：变量选择，无监督和监督的主成分回归和偏最小二乘回归，岭回归和套索。我们进一步对仅使用临床协变量、仅使用基因组数据或两者组合的预测模型的性能进行了系统比较。使用包含临床信息和微阵列基因表达数据的三个生存数据集进行比较。用于临床基因组预测方法的Matlab代码可在http://www.med.uio.no/imb/stat/bmms/software/clinico-genomic/获得。基于我们的三个数据集，比较表明，建立的临床协变量通常会导致比单独从基因组数据获得的预测更好。在基因组模型优于临床模型的情况下，岭回归用于降维。我们还发现临床基因组模型往往优于仅基于基因组数据的模型。此外，临床-基因组模型和岭回归的使用对于所有三个数据集给出了比仅基于临床协变量的模型更好的预测。
Survival prediction from high-dimensional genomic data is an active field in today's medical research. Most of the proposed prediction methods make use of genomic data alone without considering established clinical covariates that often are available and known to have predictive value. Recent studies suggest that combining clinical and genomic information may improve predictions, but there is a lack of systematic studies on the topic. Also, for the widely used Cox regression model, it is not obvious how to handle such combined models. We propose a way to combine classical clinical covariates with genomic data in a clinico-genomic prediction model based on the Cox regression model. The prediction model is obtained by a simultaneous use of both types of covariates, but applying dimension reduction only to the high-dimensional genomic variables. We describe how this can be done for seven well-known prediction methods: variable selection, unsupervised and supervised principal components regression and partial least squares regression, ridge regression, and the lasso. We further perform a systematic comparison of the performance of prediction models using clinical covariates only, genomic data only, or a combination of the two. The comparison is done using three survival data sets containing both clinical information and microarray gene expression data. Matlab code for the clinico-genomic prediction methods is available at http://www.med.uio.no/imb/stat/bmms/software/clinico-genomic/. Based on our three data sets, the comparison shows that established clinical covariates will often lead to better predictions than what can be obtained from genomic data alone. In the cases where the genomic models are better than the clinical, ridge regression is used for dimension reduction. We also find that the clinico-genomic models tend to outperform the models based on only genomic data. Further, clinico-genomic models and the use of ridge regression gives for all three data sets better predictions than models based on the clinical covariates alone.
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影响因子：
3.8
作者：
Campone, Mario;Campion, Loic;Jezequel, Pascal
通讯作者：
Jezequel, Pascal
影响因子：
9.8
作者：
Bair E;Tibshirani R
通讯作者：
Tibshirani R
影响因子：
12.3
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Teschendorff AE;Naderi A;Barbosa-Morais NL;Pinder SE;Ellis IO;Aparicio S;Brenton JD;Caldas C
通讯作者：
Caldas C
影响因子：
8.4
作者：
Dunkler, Daniela;Michiels, Stefan;Schemper, Michael
通讯作者：
Schemper, Michael
影响因子：
1.3
作者：
Nygard, Stale;Borgan, Ornulf;Storvold, Hege Leite
通讯作者：
Storvold, Hege Leite