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Establishing a Competing Risk Regression Nomogram Model for Survival Data
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Using flexible regression models for calculating hospital's production functions.

Francisco Reyes-Santías1, Octavio Cordova-Arevalo2, Elena Rivo-Lopez3

  • 1Departamento de Organización de Empresas y Marketing, Universidad de Vigo. Facultad de Ciencias Empresarias e Turismo, As Lagoas, Campus Universitario s/n, 32004, Ourense, Spain. francisco.reyes@uvigo.es.

BMC Health Services Research
|July 12, 2020
PubMed
Summary

Generalized Additive Models (GAM) offer a more flexible approach than traditional parametric models for health economics. GAMs provide more accurate predictions, especially for specialized medical services, making them a promising tool for the field.

Keywords:
Cobb-DouglasGAMHospitalProduction functionRegression

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Area of Science:

  • Health Economics
  • Econometrics
  • Statistical Modeling

Background:

  • Parametric models in health economics lack flexibility.
  • Generalized Additive Models (GAM) offer a nonparametric alternative.
  • GAMs remain underexplored in health economics applications.

Purpose of the Study:

  • To develop a flexible hospital production function using GAMs with interactions.
  • To compare the predictive performance of GAMs against the classic Cobb-Douglas model.
  • To assess the utility of GAMs in modeling productive factors in healthcare.

Main Methods:

  • Utilized an Additive Model (AM) incorporating beds-facultative interaction.
  • Included categorical (Hospital) and continuous (Year) covariates.
  • Employed penalized thin plate splines for smoothed functions and REML for parameter estimation.

Main Results:

  • The Cobb-Douglas model adequately fits general clinical and surgical services.
  • The GAM demonstrated superior fit for more specialized medical services.
  • GAMs provide more accurate prediction values by capturing non-linear relationships.

Conclusions:

  • GAMs are more flexible than parametric models, offering better fit for non-linear relationships.
  • GAMs enable more accurate prediction values in health economics.
  • Additive Models (AM) show promise for research and application in health economics.