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Valid overall odds ratio estimators using different stratified sampling schemes.

Hani M Samawi1, Jing Kersey1

  • 1Department of Biostatistics, Epidemiology and Environmental Health Sciences, Jiann-Ping Hsu College of Public Health, Georgia Southern University, Statesboro, Georgia, USA.

Journal of Biopharmaceutical Statistics
|December 26, 2024
PubMed
Summary

Stratified ranked set sampling (SRSS) offers improved odds ratio estimation over simple stratified sampling (SSRS). This study validates new estimators using NHANES data, confirming SRSS advantages for stratified populations.

Keywords:
Cochran Mantel–Haenszeloverall odds ratioranked set sampling, stratified ranked set samplingsimple stratified random sampling

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Survey Methodology

Background:

  • Accurate estimation of odds ratios is crucial for analyzing associations between exposures and outcomes in stratified populations.
  • Existing methods using simple stratified sampling (SSRS) may not fully leverage available information for improved precision.

Purpose of the Study:

  • To develop and evaluate valid estimators for the overall odds ratio in stratified populations using both SSRS and stratified ranked set sampling (SRSS).
  • To compare the performance of SRSS-based estimators against SSRS-based estimators.

Main Methods:

  • Analytical derivations of expected values and variances for naive weighted and Cochran-Mantel-Haenszel estimators under SSRS and SRSS.
  • Intensive simulation experiments to assess estimator performance.
  • Empirical validation using the 2009-2010 National Health and Nutrition Examination Survey (NHANES) data.

Main Results:

  • SRSS-based estimators demonstrated notable advantages over SSRS-based estimators in simulation studies.
  • The proposed estimators showed practical utility when applied to real-world NHANES data.
  • Both naive weighted and Cochran-Mantel-Haenszel approaches were examined for their performance characteristics.

Conclusions:

  • SRSS provides a more efficient approach for estimating overall odds ratios in stratified populations compared to SSRS.
  • The validated estimators offer robust tools for researchers analyzing odds ratios in diverse settings.
  • Empirical validation confirms the practical applicability of SRSS-based methods in public health research.