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Augmented backward elimination: a pragmatic and purposeful way to develop statistical models.

Daniela Dunkler1, Max Plischke2, Karen Leffondré3

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

Plos One
|November 22, 2014
PubMed
Summary

Selecting the best statistical model variables is challenging. Augmented backward elimination improves upon traditional methods by reducing bias and offering greater flexibility in statistical modeling.

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Area of Science:

  • Statistics
  • Biostatistics
  • Data Science

Background:

  • Statistical models use empirical data to describe relationships between outcomes and explanatory variables.
  • Variable selection in statistical modeling is complex, especially with many potential predictors and limited subject-matter knowledge.

Purpose of the Study:

  • To critically evaluate the change-in-estimate criterion within the purposeful variable selection procedure.
  • To propose and evaluate an enhanced variable selection method, augmented backward elimination.

Main Methods:

  • Revisiting Hosmer and Lemeshow's purposeful variable selection, focusing on significance and change-in-estimate criteria.
  • Developing and implementing augmented backward elimination using a standardized change-in-estimate criterion.
  • Evaluating the proposed method via a simulation study for linear, logistic, and Cox regression models.

Main Results:

  • A significance-based threshold for the change-in-estimate criterion can render the criterion ineffective.
  • Augmented backward elimination selects larger models than standard backward elimination.
  • Augmented backward elimination yields less biased regression coefficients compared to standard backward elimination.

Conclusions:

  • Augmented backward elimination offers a reproducible and flexible approach to variable selection in statistical modeling.
  • The method provides improved accuracy in estimating regression coefficients.