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Additive risk survival model with microarray data.

Ma S, Huang J - BMC Bioinformatics (2007)

Bottom Line: We model the survival time using the additive risk model, which provides a useful alternative to the proportional hazards model and is adopted when the absolute effects, instead of the relative effects, of multiple predictors on the hazard function are of interest.We analyze the MCL and DLBCL data using the proposed approach.The selected probes are relatively stable and the proposed approach has overall satisfactory prediction power.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Epidemiology and Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu

ABSTRACT

Background: Microarray techniques survey gene expressions on a global scale. Extensive biomedical studies have been designed to discover subsets of genes that are associated with survival risks for diseases such as lymphoma and construct predictive models using those selected genes. In this article, we investigate simultaneous estimation and gene selection with right censored survival data and high dimensional gene expression measurements.

Results: We model the survival time using the additive risk model, which provides a useful alternative to the proportional hazards model and is adopted when the absolute effects, instead of the relative effects, of multiple predictors on the hazard function are of interest. A Lasso (least absolute shrinkage and selection operator) type estimate is proposed for simultaneous estimation and gene selection. Tuning parameter is selected using the V-fold cross validation. We propose Leave-One-Out cross validation based methods for evaluating the relative stability of individual genes and overall prediction significance.

Conclusion: We analyze the MCL and DLBCL data using the proposed approach. A small number of probes represented on the microarrays are identified, most of which have sound biological implications in lymphoma development. The selected probes are relatively stable and the proposed approach has overall satisfactory prediction power.

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MCL data: CV score as a function of tuning parameter u.
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Figure 1: MCL data: CV score as a function of tuning parameter u.

Mentions: In applying the proposed Lasso approach, we select the tuning parameter u via the five-fold cross validation. We show in Figure 1 the CV score as a function of the tuning parameter u. There is a well-defined minimum at u = 2.3. Using this cross validated tuning parameter, only 6 out of 500 probes have nonzero estimated coefficients in the predictive model. We show in Table 1 the list of those six probes, their official symbols, estimates and corresponding OI (occurrence index). Two of these six probes, UNIQID 23826 and 34790, are from the same gene TK1.


Additive risk survival model with microarray data.

Ma S, Huang J - BMC Bioinformatics (2007)

MCL data: CV score as a function of tuning parameter u.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC1904459&req=5

Figure 1: MCL data: CV score as a function of tuning parameter u.
Mentions: In applying the proposed Lasso approach, we select the tuning parameter u via the five-fold cross validation. We show in Figure 1 the CV score as a function of the tuning parameter u. There is a well-defined minimum at u = 2.3. Using this cross validated tuning parameter, only 6 out of 500 probes have nonzero estimated coefficients in the predictive model. We show in Table 1 the list of those six probes, their official symbols, estimates and corresponding OI (occurrence index). Two of these six probes, UNIQID 23826 and 34790, are from the same gene TK1.

Bottom Line: We model the survival time using the additive risk model, which provides a useful alternative to the proportional hazards model and is adopted when the absolute effects, instead of the relative effects, of multiple predictors on the hazard function are of interest.We analyze the MCL and DLBCL data using the proposed approach.The selected probes are relatively stable and the proposed approach has overall satisfactory prediction power.

View Article: PubMed Central - HTML - PubMed

Affiliation: Department of Epidemiology and Public Health, Yale University, New Haven, CT 06520, USA. shuangge.ma@yale.edu

ABSTRACT

Background: Microarray techniques survey gene expressions on a global scale. Extensive biomedical studies have been designed to discover subsets of genes that are associated with survival risks for diseases such as lymphoma and construct predictive models using those selected genes. In this article, we investigate simultaneous estimation and gene selection with right censored survival data and high dimensional gene expression measurements.

Results: We model the survival time using the additive risk model, which provides a useful alternative to the proportional hazards model and is adopted when the absolute effects, instead of the relative effects, of multiple predictors on the hazard function are of interest. A Lasso (least absolute shrinkage and selection operator) type estimate is proposed for simultaneous estimation and gene selection. Tuning parameter is selected using the V-fold cross validation. We propose Leave-One-Out cross validation based methods for evaluating the relative stability of individual genes and overall prediction significance.

Conclusion: We analyze the MCL and DLBCL data using the proposed approach. A small number of probes represented on the microarrays are identified, most of which have sound biological implications in lymphoma development. The selected probes are relatively stable and the proposed approach has overall satisfactory prediction power.

Show MeSH
Related in: MedlinePlus