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

• Published in: BMC immunology 2012-07, Vol.13 (1), p.37-37, Article 37
• Main Authors: Eilers, Paul H C, Röder, Esther, Savelkoul, Huub F J, van Wijk, Roy Gerth
• Format: Article
• Language: English
• Subjects: Analysis; Animals; Data analysis; Economic models; Expected values; Humans; Immunologic Tests - methods; Immunological data; immunotherapy; Information management; Medical research; Medicine, Experimental; Methodology; Models, Statistical; Non-detects; Observer Variation; Outliers; Quantile regression; Regression Analysis; Research Design; Robustness; Soluble biological markers; Statistical; Statistical analysis; Studies
• ISSN: 1471-2172; 1471-2172
• DOI: 10.1186/1471-2172-13-37

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