Quantile regression for the statistical analysis of immunological data with many 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.