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Correction for covariate measurement error in generalized linear models--a bootstrap approach

J K Haukka1

  • 1National Public Health Institute, Helsinki, Finland.

Biometrics
|September 1, 1995
PubMed
Summary

This study introduces a two-phase bootstrap method to correct covariate measurement error in statistical models. The new approach improves parameter estimation accuracy for generalized linear models, offering a valuable tool for researchers dealing with imperfect data.

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Area of Science:

  • Biostatistics
  • Statistical Modeling
  • Epidemiology

Background:

  • Covariate measurement error is a common issue in statistical analyses, potentially biasing results.
  • Existing methods for correcting measurement error may have limitations.
  • Accurate covariate measurement is crucial for reliable model parameter estimation.

Purpose of the Study:

  • To propose and evaluate a novel two-phase bootstrap method for addressing covariate measurement error.
  • To compare the performance of the proposed method against a known correction technique.
  • To demonstrate the applicability of the method using real-world data.

Main Methods:

  • A two-phase bootstrap resampling strategy was employed.
  • Validation data were used to approximate the measurement error model.

Related Experiment Videos

  • Generalized linear models were fitted using corrected covariate expectations.
  • Simulations were conducted for logistic regression models.

    • Main Results:

    • The proposed two-phase bootstrap method demonstrated effectiveness in correcting covariate measurement error.
    • Performance was compared via simulation to the Rosner, Willet, and Spiegelman (1991) method.
    • The method showed potential for improving parameter estimates in the presence of measurement error.

    Conclusions:

    • The two-phase bootstrap method offers a viable approach for handling covariate measurement error.
    • This technique can enhance the accuracy of statistical inference in observational studies.
    • Further application and validation in diverse datasets are warranted.