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Model-based random forests for ordinal regression.

Muriel Buri1, Torsten Hothorn1

  • 1Institut für Epidemiologie, Biostatistik und Prävention, Universität Zürich, Zürich, Switzerland.

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|August 9, 2020
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Summary
This summary is machine-generated.

This study compares random forest methods for ordinal prognostic models, introducing two new transformation forests. One new method effectively handles non-proportional odds in prognostic variable impacts.

Keywords:
amyotrophic lateral sclerosisconditional distribution functionconditional odds functionordinal outcomerandom foresttransformation model

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Area of Science:

  • Statistics
  • Machine Learning
  • Biostatistics

Background:

  • Prognostic models for ordinal outcomes are crucial in healthcare.
  • Existing random forest variants may struggle with non-proportional odds.
  • Understanding the impact of prognostic variables is key for accurate predictions.

Purpose of the Study:

  • To compare existing random forest variants for ordinal outcomes.
  • To propose and evaluate novel model-based transformation forests.
  • To assess methods in the presence of non-proportional odds.

Main Methods:

  • Conditional odds function models were used to analyze random forest variants.
  • Ordinal Forests and Conditional Inference Forests were evaluated.
  • Two new transformation forests were developed and compared, differing in split criteria.

Main Results:

  • The proposed transformation forests were empirically evaluated via simulation.
  • One novel method demonstrated ability to detect non-proportional odds.
  • Performance was illustrated using a re-analysis of Amyotrophic Lateral Sclerosis (ALS) patient data.

Conclusions:

  • Novel transformation forests offer improved performance for ordinal prognostic models.
  • The ability to handle non-proportional odds enhances prognostic accuracy.
  • These methods have practical applications in clinical outcome prediction, such as in ALS research.