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Rectangular multivariate normal prediction regions for setting reference regions in laboratory medicine.

Michael Daniel Lucagbo1,2, Thomas Mathew1, Derek S Young3

  • 1Department of Mathematics & Statistics, University of Maryland Baltimore County, Baltimore, Maryland, USA.

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|August 9, 2022
PubMed
Summary
This summary is machine-generated.

This study introduces a new method for creating rectangular multivariate reference regions (MRRs) for laboratory test results. These novel MRRs improve upon traditional ellipsoidal regions by better detecting extreme values and offering more accurate patient data interpretation.

Keywords:
Bonferroni correctionmixed reference intervalsmultivariate reference regionparametric bootstraprectangular prediction region

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Area of Science:

  • Biostatistics
  • Clinical Chemistry
  • Medical Decision Making

Background:

  • Reference intervals are crucial for interpreting patient laboratory results.
  • Multivariate reference regions (MRRs) are needed for multiple biochemical analytes but traditional ellipsoidal MRRs have limitations.
  • Ellipsoidal MRRs fail to detect componentwise extreme values, limiting their practical use.

Purpose of the Study:

  • To propose a novel procedure for constructing rectangular multivariate reference regions (MRRs) under multivariate normality.
  • To address the limitations of traditional ellipsoidal MRRs in detecting componentwise extreme values.
  • To develop methods for computing mixed and covariate-dependent MRRs.

Main Methods:

  • Construction of rectangular MRRs using a prediction region criterion.
  • Employment of a parametric bootstrap approach to compute prediction factors for unknown population correlations.
  • Exploration of covariate-dependent MRRs within a multivariate regression framework.

Main Results:

  • The proposed parametric bootstrap procedure demonstrates high accuracy with coverage probabilities close to the nominal level.
  • Rectangular MRRs exhibit substantially smaller expected volumes compared to Bonferroni simultaneous prediction intervals.
  • The study validates the accuracy and efficiency of the developed methods through numerical simulations.

Conclusions:

  • The proposed rectangular MRRs offer a more effective alternative to traditional ellipsoidal regions for interpreting multivariate laboratory data.
  • The developed methods provide accurate and efficient tools for constructing various types of MRRs, including mixed and covariate-dependent.
  • These advancements have significant implications for clinical practice, aiding in the assessment of kidney function and growth factor systems.