Nonparametric Reference Regions for in Laboratory Medicine Using Tolerance and Prediction Regions

Music City Center 

Reference regions are invaluable in the interpretation of results of biochemical and physiological tests of patients. When there are multiple biochemical analytes measured from each subject, a multivariate reference region (MRR) is needed. MRRs are more desirable than multiple univariate reference regions because the latter has less specificity against false positives and disregards the cross-correlations between variables. In the laboratory medicine literature, there are MRRs available under multivariate normality. However, almost all laboratory test results follow a non-normal distribution. While this is true, very few procedures to compute MRRs outside a multivariate normal setting are available. For this reason, we develop MRRs in a nonparametric setting. We consider two criteria in constructing MRRs: the prediction region and the tolerance region criteria. Moreover, to make the MRRs amenable for component-wise outlier detection, which ellipsoidal regions are not capable of, we use rectangular regions. The accuracies of the proposed procedures are evaluated through coverage probabilities and expected volumes. A solution to include covariates in the model is also proposed.

reference intervals

nonparametric

tolerance regions

prediction regions

laboratory medicine 

Section on Medical Devices and Diagnostics