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Open Access Highly Accessed Methodology article

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

Paul HC Eilers1, Esther Röder23, Huub FJ Savelkoul4 and Roy Gerth van Wijk2*

Author Affiliations

1 Department of Biostatistics, Erasmus MC-University Medical Center, PO Box 2040, 3000, CA, Rotterdam, The Netherlands

2 Section of Allergology, Department of Internal Medicine (GK 324), Erasmus MC-University Medical Center, PO Box 2040, 3000, CA, Rotterdam, The Netherland

3 Department of General Practice, Erasmus MC-University Medical Center, PO Box 2040, 3000, CA, Rotterdam, The Netherlands

4 Cell Biology and Immunology Group, Wageningen University, PO Box 338, 6700, AH, Wageningen, The Netherlands

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BMC Immunology 2012, 13:37  doi:10.1186/1471-2172-13-37

Published: 7 July 2012

Abstract

Background

Immunological 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 results

Quantile 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.

Conclusion

Quantile regression is a valuable addition to the statistical methods that can be used for the analysis of immunological datasets with non-detects.

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