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Related Experiment Videos

Extreme regression models for characterizing high-cost patients.

Dario Gregori1, Michele Petrinco, Giulia Barbati

  • 1Department of Public Health and Microbiology, University of Turin, Turin, Italy. dario.gregori@unito.it

Journal of Evaluation in Clinical Practice
|February 26, 2009
PubMed
Summary

Extreme regression (ER) models effectively handle skewed healthcare cost distributions, provided all costs are positive. This approach unifies prediction and cutoff estimation for continuous variables without pre-specifying thresholds.

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Area of Science:

  • Health Economics
  • Statistical Modeling
  • Biostatistics

Background:

  • Healthcare costs are typically skewed, with a few individuals incurring high expenditures.
  • Predicting high healthcare costs is crucial for effective healthcare planning and resource allocation.

Purpose of the Study:

  • To evaluate the performance of extreme regression (ER) models in handling skewed cost data.
  • To assess ER models' behavior under conditions of heterogeneity and asymmetry.

Main Methods:

  • A simulation study using the LogNormal distribution to test ER model performance.
  • Application of ER models to real-world datasets from diabetes, lung cancer, and myocardial infarction patients.

Main Results:

  • ER models demonstrated good performance with skewed cost distributions.
  • The condition of strictly positive costs is necessary for ER model efficacy.

Conclusions:

  • ER models offer a unified framework for estimating cut-offs and generating prediction rules simultaneously.
  • ER models allow analysis at any desired quantile of the cost distribution, eliminating the need for pre-specified cut-offs.