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Estimation of a common odds ratio under binary cluster sampling

C Ahn1, T Odom-Maryon

  • 1Department of biostatistics, City of Hope Medical Center, Duarte, CA 91010, USA.

Statistics in Medicine
|July 30, 1995
PubMed
Summary
This summary is machine-generated.

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This study compares two methods for estimating odds ratios in clustered data. For uncorrelated data, the Mantel-Haenszel estimator is recommended. For correlated data, adjusted Mantel-Haenszel or Rao and Scott estimators are advised.

Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Estimating common odds ratios is crucial in meta-analysis of 2x2 tables.
  • Clustered data introduces correlation, complicating standard estimation methods.

Purpose of the Study:

  • To compare the performance of the Mantel-Haenszel estimator and Rao and Scott's estimator for common odds ratio estimation in clustered data.
  • To evaluate biases, variances, and coverage proportions under different correlation scenarios.

Main Methods:

  • A simulation study was conducted using the beta-binomial distribution to model data correlation within clusters.
  • The Mantel-Haenszel (gamma MH) and Rao and Scott (gamma RSP) estimators were compared.
  • Simulations focused on scenarios with equal subjects per cluster and equal observations per row/stratum.

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Main Results:

  • When data are uncorrelated (rho = 0), the Mantel-Haenszel estimator with unadjusted variance and Rao and Scott's estimator with a pooled design effect performed well.
  • For correlated data (rho > 0), the Mantel-Haenszel estimator with adjusted variance is recommended.
  • Rao and Scott's estimator with a pooled design effect is also recommended for correlated data.

Conclusions:

  • The choice of estimator and variance adjustment depends on the degree of data correlation within clusters.
  • For uncorrelated data, simpler variance adjustments suffice.
  • For correlated data, adjusted variance for Mantel-Haenszel or the Rao and Scott approach are preferred for accurate odds ratio estimation.