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Validation studies using an alloyed gold standard

S Wacholder1, B Armstrong, P Hartge

  • 1National Cancer Institute, Epidemiology and Biostatistics Program, Bethesda, MD.

American Journal of Epidemiology
|June 1, 1993
PubMed
Summary
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When correcting relative risk estimates for measurement error, a common assumption is a perfect gold standard. However, this study shows that if the gold standard also has errors, the corrected estimate may be inaccurate, especially with correlated errors.

Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Validation studies are crucial for correcting relative risk estimates in epidemiology.
  • A common assumption is that the "gold standard" measure is error-free.

Purpose of the Study:

  • To investigate the impact of measurement error in the gold standard on corrected relative risk estimates.
  • To analyze how error correlation between measures affects bias correction.

Main Methods:

  • The study theoretically examines the effect of a fallible gold standard on bias correction methods.
  • It analyzes the influence of error magnitudes and correlations on corrected estimates.

Main Results:

  • Violating the perfect gold standard assumption can lead to overcorrection.

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  • The degree of overcorrection depends on the error magnitudes and their correlation.

    • Conclusions:

    • Epidemiologic studies must account for potential errors in gold standard measures.
    • Current bias correction methods may require adjustment when gold standards are not perfect.