Dimension Reduction of Microarray Data in the Presence of a Censored Survival Response: A Simulation Study

Nguyen Tuan S and Rojo Javier
Additional contact information
Nguyen Tuan S: Rice University
Rojo Javier: Rice University

Statistical Applications in Genetics and Molecular Biology, 2009, vol. 8, issue 1, 40

Abstract: An important aspect of microarray studies involves the prediction of patient survival based on their gene expression levels. To cope with the high dimensionality of the microarray gene expression data, it is customary to first reduce the dimension of the gene expression data via dimension reduction methods, and then use the Cox proportional hazards model to predict patient survival. In this paper, we propose a variant of Partial Least Squares, denoted as Rank-based Modified Partial Least Squares (RMPLS), that is insensitive to outlying values of both the response and the gene expressions. We assess the performance of RMPLS and several dimension reduction methods using a simulation model for gene expression data with a censored response. In particular, Principal Component Analysis (PCA), modified Partial Least Squares (MPLS), RMPLS, Sliced Inverse Regression (SIR), Correlation Principal Component Regression (CPCR), Supervised Principal Component Regression (SPCR) and Univariate Selection (UNIV) are compared in terms of mean squared error of the estimated survival function and the estimated coefficients of the covariates, and in terms of the bias of the estimated survival function. It turns out that RMPLS outperforms all other methods in terms of the mean squared error and the bias of the survival function in the presence of outliers in the response. In addition, RMPLS is comparable to MPLS in the absence of outliers. In this setting, both RMPLS and MPLS outperform all other methods considered in this study in terms of mean squared error and bias of the estimated survival function.

Keywords: censored response; Cox proportional hazards model; outliers; mean squared error; bias (search for similar items in EconPapers)
Date: 2009
References: View references in EconPapers View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1395 (text/html)
For access to full text, subscription to the journal or payment for the individual article is required.

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:bpj:sagmbi:v:8:y:2009:i:1:n:4

Ordering information: This journal article can be ordered from
https://www.degruyter.com/journal/key/sagmb/html

DOI: 10.2202/1544-6115.1395

Access Statistics for this article

Statistical Applications in Genetics and Molecular Biology is currently edited by Michael P. H. Stumpf

More articles in Statistical Applications in Genetics and Molecular Biology from De Gruyter
Bibliographic data for series maintained by Peter Golla ().