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Explained variation in shared frailty models.

Andreas Gleiss1, Michael Gnant2, Michael Schemper1

  • 1Center for Medical Statistics, Informatics, and Intelligent Systems, Medical University of Vienna, Vienna, Austria.

Statistics in Medicine
|December 29, 2017
PubMed
Summary
This summary is machine-generated.

This study extends explained variation to compare prognostic factor importance, including random factors like center effects in multicenter studies. The new method aids in ranking factor significance, even with low predictive accuracy.

Keywords:
explained variationfrailtymixed-effects proportional hazards modelmulticenter studysurvival

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Area of Science:

  • Biostatistics
  • Medical Informatics
  • Epidemiology

Background:

  • Explained variation quantifies the improvement in predictive accuracy from prognostic factors over unconditional prediction.
  • Existing methods for explained variation accommodate various factor types but struggle with random effects, limiting their application in complex study designs.

Purpose of the Study:

  • To extend the explained variation measure to incorporate random factors, such as center effects in multicenter studies.
  • To enable direct comparison of the importance of random factors (e.g., study centers) against other prognostic factors.

Main Methods:

  • Extension of the Schemper and Henderson (2000) explained variation measure for shared frailty Cox models.
  • Development of an SAS macro and an R function for practical implementation.
  • Monte Carlo simulation to explore empirical properties of variation explained by random factors.

Main Results:

  • The extended explained variation measure successfully accommodates random factors, allowing for their importance to be quantified and compared.
  • The Monte Carlo study provided insights into the behavior of explained variation attributed to random factors.
  • Application to an Austrian colon cancer multicenter study demonstrated the practical utility of the approach.

Conclusions:

  • The extended explained variation measure provides a robust method for ranking the importance of both fixed and random prognostic factors in complex studies.
  • This approach enhances the interpretability of multicenter studies by allowing direct comparison of center effects with other predictors.
  • The developed SAS macro and R function facilitate the application of this advanced statistical technique in research.