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Related Experiment Videos

Reducing mean squared error in the analysis of pair-matched case-control studies.

L A Kalish1

  • 1Department of Biostatistics, Dana-Farber Cancer Institute, Harvard School of Public Health, Boston, Massachusetts 02115.

Biometrics
|June 1, 1990
PubMed
Summary

This study introduces an optimized estimator for pair-matched case-control studies by balancing bias and precision. It explores a weighted average of existing estimators to improve the common odds ratio estimation.

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Standard estimators for common odds ratio in pair-matched case-control studies have limitations: the stratified estimate is consistent but ignores concordant pairs, while the pooled estimate uses all data but is inconsistent.
  • Liang and Zeger (1988) proposed a compromise estimator to balance bias and precision.

Purpose of the Study:

  • To explore optimizing the trade-off between bias and precision in estimating the common odds ratio for pair-matched case-control studies.
  • To derive the optimal weight for a family of weighted averages of stratified and pooled estimates that minimizes mean squared error.

Main Methods:

  • Consideration of a family of weighted averages combining the stratified and pooled estimators.
  • Derivation of the optimal weight minimizing an asymptotic approximation of the mean squared error.

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  • Evaluation of small-sample properties using simulations.

    • Main Results:

    • A method is proposed to derive an optimal weight for a composite estimator.
    • The derived optimal weight aims to minimize the mean squared error, balancing bias and precision.
    • Simulation studies are used to assess the performance of the proposed estimator in small samples.

    Conclusions:

    • The proposed method offers an approximately optimal estimator by optimizing the trade-off between bias and precision.
    • The practical implementation requires estimating the optimal weight from the data.
    • The study provides insights into improving the estimation of common odds ratios in matched case-control designs.