Discriminant analyses of peanut allergy severity scores

O. Collignon, J.-M. Monnez, P. Vallois, F. Codreanu, J.-M. Renaudin, G. Kanny, M. Brulliard, B. E. Bihain, S. Jacquenet and D. Moneret-Vautrin

Journal of Applied Statistics, 2011, vol. 38, issue 9, 1783-1799

Abstract: Peanut allergy is one of the most prevalent food allergies. The possibility of a lethal accidental exposure and the persistence of the disease make it a public health problem. Evaluating the intensity of symptoms is accomplished with a double blind placebo-controlled food challenge (DBPCFC), which scores the severity of reactions and measures the dose of peanut that elicits the first reaction. Since DBPCFC can result in life-threatening responses, we propose an alternate procedure with the long-term goal of replacing invasive allergy tests. Discriminant analyses of DBPCFC score, the eliciting dose and the first accidental exposure score were performed in 76 allergic patients using 6 immunoassays and 28 skin prick tests. A multiple factorial analysis was performed to assign equal weights to both groups of variables, and predictive models were built by cross-validation with linear discriminant analysis, k -nearest neighbours, classification and regression trees, penalized support vector machine, stepwise logistic regression and AdaBoost methods. We developed an algorithm for simultaneously clustering eliciting dose values and selecting discriminant variables. Our main conclusion is that antibody measurements offer information on the allergy severity, especially those directed against rAra-h1 and rAra-h3 . Further independent validation of these results and the use of new predictors will help extend this study to clinical practices.

Date: 2011
References: View complete reference list from CitEc
Citations:

Downloads: (external link)
http://hdl.handle.net/10.1080/02664763.2010.529878 (text/html)
Access to full text is restricted to subscribers.

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:taf:japsta:v:38:y:2011:i:9:p:1783-1799

Ordering information: This journal article can be ordered from
http://www.tandfonline.com/pricing/journal/CJAS20

DOI: 10.1080/02664763.2010.529878

Access Statistics for this article

Journal of Applied Statistics is currently edited by Robert Aykroyd

More articles in Journal of Applied Statistics from Taylor & Francis Journals
Bibliographic data for series maintained by Chris Longhurst ().