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

P.H.C. Eilers, E. Roder, H.F.J. Savelkoul, R.G. van Wijk

Research output: Contribution to journalArticleAcademicpeer-review

8 Citations (Scopus)

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.
Original languageEnglish
Article number37
JournalBMC Immunology
Volume13
DOIs
Publication statusPublished - 2012

Keywords

  • immunotherapy

Fingerprint Dive into the research topics of 'Quantile regression for the statistical analysis of immunological data with many non-detects'. Together they form a unique fingerprint.

Cite this