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Updated: May 14, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Small-Sample Reference Interval Estimation: A Comparative Analysis of HARISS, Robust, Parametric, and Nonparametric

Kevin Le Boedec1

  • 1Internal Medecine Unit, Centre Hospitalier Vétérinaire Frégis, IVC Evidensia France, Paris, France.

Veterinary Clinical Pathology
|May 13, 2026
PubMed
Summary
This summary is machine-generated.

HARISS improves reference interval (RI) accuracy from small veterinary samples across diverse distributions. This new method significantly outperforms traditional robust (RM) and parametric/nonparametric (PNPM) approaches for accurate RI estimation.

Keywords:
ASVCPartificial intelligenceconvolutional neural networkdata distributionreference range

Related Experiment Videos

Last Updated: May 14, 2026

An R-Based Landscape Validation of a Competing Risk Model
05:37

An R-Based Landscape Validation of a Competing Risk Model

Published on: September 16, 2022

Area of Science:

  • Veterinary Medicine
  • Biostatistics
  • Reference Interval Determination

Background:

  • Current veterinary guidelines recommend robust (RM) or parametric/nonparametric methods (PNPM) for reference intervals (RIs) from small samples.
  • HARISS, a web application, refines RI estimation by visually classifying data distributions (Gaussian, lognormal, left-skewed) to select optimal statistical methods.
  • Previous simulations indicated HARISS's decision rules enhance RI accuracy for specific population distributions.

Purpose of the Study:

  • To compare the accuracy of HARISS, RM, and PNPM in estimating RIs from small samples drawn from diverse population distributions.
  • To evaluate the performance of HARISS against established methods under various simulated data scenarios.

Main Methods:

  • Samples of 40, 50, or 60 values (50 replicates each) were drawn from seven simulated populations (Gaussian, Student's t, lognormal, right-skewed, left-skewed, bimodal, irregular).
  • Reference intervals were calculated using HARISS, RM, and PNPM without outlier inclusion.
  • Accuracy was assessed using repeated-measures ANOVA.

Main Results:

  • HARISS significantly improved the accuracy of both lower and upper RI limit estimations (p < 0.001) across all tested population distributions and sample sizes.
  • HARISS demonstrated superior accuracy compared to RM for Gaussian and Student's t distributions.
  • HARISS significantly outperformed PNPM for skewed population distributions.

Conclusions:

  • HARISS provides the most accurate reference interval limit estimations from small samples, outperforming RM and PNPM across diverse simulated population distributions.
  • The findings support HARISS as a superior tool for establishing veterinary reference intervals, especially when dealing with small sample sizes and varied data distributions.