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Computing the polytomous discrimination index.

Douglas C Dover1, Sunjidatul Islam1, Cynthia M Westerhout1

  • 1Canadian VIGOUR Centre, University of Alberta, Edmonton, Alberta, Canada.

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|April 18, 2021
PubMed
Summary
This summary is machine-generated.

This study introduces an efficient method for calculating the polytomous discrimination index (PDI) in large datasets. The enhanced PDI computation significantly speeds up the assessment of regression models with multiple outcome categories.

Keywords:
R functionSAS macrodiscriminationpolytomous discrimination indexpolytomous regression

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Area of Science:

  • Statistics
  • Biostatistics
  • Machine Learning

Background:

  • Polytomous regression models are crucial in clinical research for analyzing outcomes with multiple categories.
  • Assessing the discriminatory ability of these models is essential for reliable predictions.
  • Existing methods for calculating the polytomous discrimination index (PDI) are computationally intensive and not suitable for large datasets.

Purpose of the Study:

  • To develop a computationally efficient method for calculating the polytomous discrimination index (PDI).
  • To enable the application of PDI analysis on "big data" in clinical research.
  • To provide practical tools (SAS macro and R function) for rapid PDI evaluation.

Main Methods:

  • The PDI formula was mathematically manipulated to rely solely on predicted probability distributions.
  • This manipulation significantly reduces computation time, making it suitable for large datasets.
  • New SAS macro and R functions were developed and validated on simulated and real-world health datasets.

Main Results:

  • The revised PDI calculation method demonstrates substantial improvements in computation time.
  • The developed SAS macro and R functions efficiently evaluate PDI and its components.
  • Comparisons with M-index and HUM show PDI and HUM are superior in detecting non-random or incorrectly ranked predictions.

Conclusions:

  • The enhanced PDI calculation is a significant advancement for assessing polytomous regression models, especially with large datasets.
  • The provided SAS and R functions offer practical tools for researchers to implement improved discrimination analysis.
  • The study highlights the advantages of PDI and HUM over pairwise comparison indices for specific predictive performance evaluations.