On the identification of predictive biomarkers: Detecting treatment-by-gene interaction in high-dimensional data
For personalised medicine the identification of predictive biomarkers is of great interest. These could guide the choice of therapy and could therefore optimise the benefits of patients of such treatments. The technology of gene expression microarrays allows one to scan thousands of potentially predictive biomarkers simultaneously. In clinical trials it has nowadays become common to use microarrays to collect gene expression data of the patients before treatment. The identification of predictive biomarkers can be statistically addressed by inference of gene-wise generalised linear models (GLM) including an interaction term gene expression times treatment. Inference for such GLMs is then often based on likelihood-ratio (LR) or Wald test statistics to test the influence of interaction of gene expression and treatment on the clinical treatment response. For multiple testing scenarios coming along with these gene-wise GLMs the control of the false discovery rate (FDR) would be appropriate; some false positives can be tolerated within a list of potential candidate genes which deserve further investigation. In a simulation study the utility of various FDR controlling multiple testing procedures for the identification of predictive genes is examined. Since the usual experiment on microarray data deals with small numbers of observations due to financial or probe limitations special interest lies on the behaviour of small sample sizes. Results reveal that a permutation of regressor residuals (PRR) test is superior to standard LR and Wald tests in terms of FDR control.
If you experience problems downloading a file, check if you have the proper application to view it first. In case of further problems read the IDEAS help page. Note that these files are not on the IDEAS site. Please be patient as the files may be large.
As the access to this document is restricted, you may want to look for a different version under "Related research" (further below) or search for a different version of it.
Volume (Year): 56 (2012)
Issue (Month): 5 ()
|Contact details of provider:|| Web page: http://www.elsevier.com/locate/csda|
References listed on IDEAS
Please report citation or reference errors to , or , if you are the registered author of the cited work, log in to your RePEc Author Service profile, click on "citations" and make appropriate adjustments.:
- Efron B. & Tibshirani R. & Storey J.D. & Tusher V., 2001. "Empirical Bayes Analysis of a Microarray Experiment," Journal of the American Statistical Association, American Statistical Association, vol. 96, pages 1151-1160, December.
When requesting a correction, please mention this item's handle: RePEc:eee:csdana:v:56:y:2012:i:5:p:1275-1286. See general information about how to correct material in RePEc.
For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: (Shamier, Wendy)
If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.
If references are entirely missing, you can add them using this form.
If the full references list an item that is present in RePEc, but the system did not link to it, you can help with this form.
If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your profile, as there may be some citations waiting for confirmation.
Please note that corrections may take a couple of weeks to filter through the various RePEc services.