Direct, age- and gender-specific reference intervals: applying a modified M-estimator of the Yeo-Johnson transformation to clinical real-world data

Abstract

Reference intervals for the general clinical practice are expected to cover non-pathological values, but also reflect the underlying biological variation present in age- and gender-specific patient populations. Reference intervals can be inferred from routine patient data measured in high capacity using parametric approaches. Stratified reference distributions are obtained which may be transformed to normality via e.g. a Yeo-Johnson transformation. The estimation of the optimal transformation parameter for Yeo-Johnson through maximum likelihood can be highly influenced by the presence of outlying observations, resulting in biased reference interval estimates.

Keywords

• clinical diagnostics
• expectation ranges
• machine learning
• medical statistics
• robust parametric methods