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Predictive accuracy and explained variation.

Michael Schemper1

  • 1Section of Clinical Biometrics, Department of Medical Computer Sciences, Vienna University, Spitalgasse 23, A-1090 Vienna, Austria. michael.scheper@akh-wien.ac.at

Statistics in Medicine
|July 11, 2003
PubMed
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This study introduces unified measures for predictive accuracy and explained variation in regression models. These metrics quantify how well covariates predict outcomes, even when significance is high.

Area of Science:

  • Statistics
  • Biostatistics
  • Epidemiology

Background:

  • Predictive accuracy quantifies how well covariates determine individual outcomes in regression models.
  • Explained variation measures the relative improvement in prediction accuracy when using covariates compared to unconditional prediction.

Purpose of the Study:

  • To present a unified concept of predictive accuracy and explained variation.
  • To provide measures applicable to various outcome types (continuous, binary, polytomous, survival).
  • To demonstrate applications using examples from different regression models.

Main Methods:

  • Development of a unified concept based on absolute prediction error.
  • Formulation of measures in both model-based and observed-vs-expected contrasts.

Related Experiment Videos

  • Application examples across continuous, binary, polytomous, and survival regression models.

    • Main Results:

    • A unified framework for assessing predictive accuracy and explained variation is presented.
    • Measures are applicable across diverse regression model types and outcome variables.
    • Demonstrated that predictive accuracy can be low even with significant covariates.

    Conclusions:

    • The proposed unified measures offer a consistent approach to evaluating regression model performance.
    • Emphasizes that statistical significance and effect size of covariates do not always translate to high predictive accuracy.
    • Highlights the importance of assessing absolute and relative predictive accuracy in regression analysis.