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Related Experiment Videos

Calibration in multi-centre cohort studies

M Plummer1, D Clayton, R Kaaks

  • 1MRC Biostatistics Unit, University Forvie Site, Cambridge, UK.

International Journal of Epidemiology
|April 1, 1994
PubMed
Summary
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Measurement error in multi-centre studies requires calibration for accurate diet-disease relationship analysis. Calibration substudy sample sizes depend on expected disease cases, not cohort size, for efficient meta-analysis.

Area of Science:

  • Epidemiology
  • Biostatistics
  • Nutritional Science

Background:

  • Measurement error in multi-centre studies introduces differential bias, hindering direct comparison of diet-disease relationships across cohorts.
  • Calibration is essential to reconcile data from different cohorts before meta-analysis.
  • Calibration substudy design must account for both subject-level and cohort-level information.

Purpose of the Study:

  • To address challenges posed by measurement error in multi-centre diet and disease studies.
  • To optimize the design of calibration substudies for accurate meta-analysis.
  • To differentiate calibration requirements for subject-level versus cohort-level data.

Main Methods:

  • Calibration involves remeasuring dietary intake in cohort subsamples using a standardized reference method.

Related Experiment Videos

  • The reference method aims to provide unbiased estimates of habitual dietary intake.
  • Calibration substudy efficiency is evaluated relative to a perfectly calibrated study.

    • Main Results:

    • Optimal sample size for calibration is a multiple of the expected number of disease cases within each cohort.
    • Stratification within cohorts and a constant sample size to case ratio within strata are recommended for confounding control.
    • Efficiency is maximized when sample size is proportional to the expected number of cases.

    Conclusions:

    • Calibration sample sizes are determined by the number of disease cases, not overall cohort size, making them potentially manageable.
    • Focusing calibration efforts on subgroups likely to yield more cases (e.g., older age groups) is more efficient.
    • Effective calibration strategies can improve the reliability of meta-analyses in multi-centre studies.