RefineR: A Novel Algorithm for Reference Interval Estimation from Real-World Data.

ABSTRACT: Reference intervals are essential for the interpretation of laboratory test results in medicine. We propose a novel indirect approach to estimate reference intervals from real-world data as an alternative to direct methods, which require samples from healthy individuals. The presented refineR algorithm separates the non-pathological distribution from the pathological distribution of observed test results using an inverse approach and identifies the model that best explains the non-pathological distribution. To evaluate its performance, we simulated test results from six common laboratory analytes with a varying location and fraction of pathological test results. Estimated reference intervals were compared to the ground truth, an alternative indirect method (kosmic), and the direct method (

SUBMITTER: Ammer T 

PROVIDER: S-EPMC8346497 | biostudies-literature | 2021 Aug

REPOSITORIES: biostudies-literature