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Related Experiment Video

Updated: Jul 10, 2026

A Machine Learning Approach to Design an Efficient Selective Screening of Mild Cognitive Impairment
12:18

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Published on: January 11, 2020

Conditional logistic regression with sandwich estimators: application to a meta-analysis

M P Fay1, B I Graubard, L S Freedman

  • 1Biometry Branch, National Cancer Institute, Bethesda, Maryland 20892-7354, USA. m7f@helix.nih.gov

Biometrics
|April 17, 1998
PubMed
Summary
This summary is machine-generated.

This study introduces improved sandwich estimators for conditional logistic regression, enhancing statistical inference for mammary tumorigenesis research. These methods provide reliable results even with limited data per cluster.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Cancer Research

Background:

  • Dietary fat and caloric intake influence mammary tumorigenesis.
  • Conditional logistic regression is a standard statistical method.
  • Classical methods have limitations with small cluster sizes.

Purpose of the Study:

  • To explore sandwich estimators of variance with conditional logistic regression.
  • To address limitations of standard Wald tests with sandwich estimators when cluster numbers are small.
  • To improve statistical inference in mammary cancer research.

Main Methods:

  • Utilized sandwich estimators of variance with conditional logistic regression.
  • Modified the standard Wald test using standardized residuals to reduce bias.
  • Employed the t-distribution with Satterthwaite's approximation for variance estimation.
  • Conducted simulations to evaluate estimator performance.

Main Results:

  • Modified sandwich estimators perform comparably to classical estimators for fixed effects.
  • Modified sandwich estimators significantly outperform classical estimators for random effects.
  • Achieved simulated nominal coverage even with few clusters per parameter.
  • Demonstrated improved statistical inference for small sample sizes.

Conclusions:

  • Modified sandwich estimators offer robust inference in conditional logistic regression, particularly for random effects.
  • These methods are valuable for analyzing mammary tumorigenesis data with limited clusters.
  • The approach enhances statistical power and reliability in complex epidemiological studies.