EconPapers    
Economics at your fingertips  
 

A Modified Maximum Contrast Method for Unequal Sample Sizes in Pharmacogenomic Studies

Nagashima Kengo, Sato Yasunori and Hamada Chikuma

Statistical Applications in Genetics and Molecular Biology, 2011, vol. 10, issue 1, 1-25

Abstract: In pharmacogenomic studies, biomedical researchers commonly analyze the association between genotype and biological response by using the Kruskal-Wallis test or one-way analysis of variance (ANOVA) after logarithmic transformation of the obtained data. However, because these methods detect unexpected biological response patterns, the power for detecting the expected pattern is reduced. Previously, we proposed a combination of the maximum contrast method and the permuted modified maximum contrast method for unequal sample size in pharmacogenomic studies. However, we noted that the distribution of the permuted modified maximum contrast statistic depends on nuisance parameter σ2, which is the population variance. In this paper, we propose a modified maximum contrast method with a statistic that does not depend on the nuisance parameter. Furthermore, we compare the performance of these methods via simulation studies. The simulation results showed that the modified maximum contrast method gave the lowest false-positive rate; therefore, this method is powerful for detecting the true response patterns in some conditions. Further, it is faster and more accurate than the permuted modified maximum contrast method. On the basis of these results, we suggest a rule of thumb to select the appropriate method in a given situation.

Keywords: multiple contrast statistics; maximum contrast statistic; unequal sample size; pharmacokinetics-related gene; biological response pattern (search for similar items in EconPapers)
Date: 2011
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
https://doi.org/10.2202/1544-6115.1560 (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:10:y:2011:i:1:n:41

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

DOI: 10.2202/1544-6115.1560

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 ().

 
Page updated 2025-03-19
Handle: RePEc:bpj:sagmbi:v:10:y:2011:i:1:n:41