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Predictor Selection

  • Eric Vittinghoff
  • David V. Glidden
  • Stephen C. Shiboski
  • Charles E. McCulloch
Chapter
Part of the Statistics for Biology and Health book series (SBH)

Abstract

Walter et al. (2001) developed a model to identify older adults at high risk of death in the first year after hospitalization, using data collected for 2,922 patients discharged from two hospitals in Ohio. Potential predictors included demographics, activities of daily living (ADLs), the APACHE-II illness-severity score, and information about the index hospitalization. A “backward” selection procedure with a restrictive inclusion criterion was used to choose a multipredictor model, using data from one of the two hospitals. The model was then validated using data from the other hospital. The goal was to select a model that best predicted future events, with a view toward identifying patients in need of more intensive monitoring and intervention.

Keywords

Bayesian Information Criterion Treatment Effect Estimate Primary Predictor Candidate Predictor Maternal Weight Gain 
These keywords were added by machine and not by the authors. This process is experimental and the keywords may be updated as the learning algorithm improves.

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Copyright information

© Springer Science+Business Media, LLC 2012

Authors and Affiliations

  • Eric Vittinghoff
    • 1
  • David V. Glidden
    • 1
  • Stephen C. Shiboski
    • 1
  • Charles E. McCulloch
    • 2
  1. 1.Department of Epidemiology and BiostatisticsUniversity of California, San FranciscoSan FranciscoUSA
  2. 2.Department of Epidemiology and BiostatisticsUniversity of California, San FranciscoSan FranciscoUSA

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