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Related Experiment Video

Updated: Mar 29, 2026

Inverse Probability of Treatment Weighting Propensity Score using the Military Health System Data Repository and National Death Index
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Fungible weights in logistic regression.

Jeff A Jones1, Niels G Waller2

  • 1Korn Ferry.

Psychological Methods
|December 15, 2015
PubMed
Summary
This summary is machine-generated.

This study introduces new methods for assessing parameter sensitivity in logistic regression models. The research provides R code to compute fungible weights, aiding researchers in evaluating model stability.

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Area of Science:

  • Statistics
  • Biostatistics
  • Quantitative Psychology

Background:

  • Parameter sensitivity analysis is crucial for robust statistical modeling.
  • Fungible weights offer a method to assess the stability of regression model parameters.
  • Existing methods for fungible weights are primarily developed for linear regression.

Purpose of the Study:

  • To develop and present novel methods for calculating fungible weights in logistic regression models.
  • To extend the concept of fungible weights from linear to logistic regression.
  • To provide accessible tools for researchers to evaluate parameter sensitivity in logistic regression.

Main Methods:

  • Review of Waller's (2008) equations for fungible weights in linear regression.
  • Description of two new methods for computing fungible weights specifically for logistic regression.
  • Application of these methods using data from the Youth Risk Behavior Surveillance Survey (Centers for Disease Control and Prevention, 2010).

Main Results:

  • Successful computation of fungible logistic regression weights.
  • Demonstration of how these weights can be utilized to assess parameter sensitivity in logistic models.
  • Development of R code (R Core Team, 2015) for generating the fungible weights.

Conclusions:

  • The developed methods provide a valuable tool for assessing parameter sensitivity in logistic regression.
  • The provided R code enhances the accessibility and application of fungible weight analysis for researchers.
  • This work contributes to more reliable and interpretable logistic regression modeling.