Quantile regression for the statistical analysis of immunological data with many non-detects

BMC Immunology

Methodology Article

Huub FJ Savelkoul¹  Paul HC Eilers²  Roy Gerth van Wijk³  Esther Röder⁴ 

[1] Cell Biology and Immunology Group, Wageningen University, PO Box 338, 6700, Wageningen, AH, The Netherlands;Department of Biostatistics, Erasmus MC-University Medical Center, PO Box 2040, 3000, Rotterdam, CA, The Netherlands;Section of Allergology, Department of Internal Medicine (GK 324), Erasmus MC-University Medical Center, PO Box 2040, 3000, Rotterdam, CA, The Netherland;Section of Allergology, Department of Internal Medicine (GK 324), Erasmus MC-University Medical Center, PO Box 2040, 3000, Rotterdam, CA, The Netherland;Department of General Practice, Erasmus MC-University Medical Center, PO Box 2040, 3000, Rotterdam, CA, The Netherlands;

关键词: Non-detects;    Outliers;    Robustness;    Data analysis;    Statistical;    Quantile regression;    Soluble biological markers;    Immunological data;   

DOI  :  10.1186/1471-2172-13-37

 received in 2012-02-20, accepted in 2012-06-12,  发布年份 2012

来源: Springer

【 摘 要 】

BackgroundImmunological parameters are hard to measure. A well-known problem is the occurrence of values below the detection limit, the non-detects. Non-detects are a nuisance, because classical statistical analyses, like ANOVA and regression, cannot be applied. The more advanced statistical techniques currently available for the analysis of datasets with non-detects can only be used if a small percentage of the data are non-detects.Methods and resultsQuantile regression, a generalization of percentiles to regression models, models the median or higher percentiles and tolerates very high numbers of non-detects. We present a non-technical introduction and illustrate it with an implementation to real data from a clinical trial. We show that by using quantile regression, groups can be compared and that meaningful linear trends can be computed, even if more than half of the data consists of non-detects.ConclusionQuantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

【 授权许可】

Unknown   
© Eilers et al.; licensee BioMed Central Ltd. 2012. This article is published under license to BioMed Central Ltd. This is an Open Access article distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/2.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.