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A Discriminant Function Approach to Adjust for Processing and Measurement Error When a Biomarker is Assayed in Pooled

Robert H Lyles1, Dane Van Domelen2, Emily M Mitchell3

  • 1Department of Biostatistics and Bioinformatics, The Rollins School of Public Health of Emory University, 1518 Clifton Rd. N.E., Mailstop 1518-002-3AA, Atlanta, GA 30322, USA. rlyles@sph.emory.edu.

International Journal of Environmental Research and Public Health
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PubMed
Summary

Pooling biological samples reduces costs but introduces assay errors. This study introduces a statistical method to accurately estimate odds ratios (OR) from pooled data, accounting for processing and measurement errors to prevent biased results.

Keywords:
epidemiologyerrors-in-variablesodds ratiopooling

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Genetics

Background:

  • Biological specimen pooling is cost-effective for assays.
  • Pooling can introduce processing and measurement errors, leading to biased estimates.
  • Accurate estimation requires accounting for these errors.

Purpose of the Study:

  • To adapt hybrid designs (individual and pooled samples) for estimating covariate-adjusted odds ratios (OR).
  • To address bias in odds ratio estimation caused by processing and measurement errors in pooled biomarker data.
  • To evaluate a discriminant function-based analysis for this purpose.

Main Methods:

  • Explored discriminant function-based analysis assuming normal residual, processing, and measurement errors.
  • Applied maximum likelihood estimation for straightforward computation of adjusted log OR.
  • Utilized real data from the Collaborative Perinatal Project and simulations for validation.

Main Results:

  • The proposed method provides a computationally convenient way to estimate covariate-adjusted odds ratios.
  • The discriminant function approach effectively accounts for processing and measurement errors.
  • Simulations demonstrated the estimators' ability to alleviate bias in pooled data analysis.

Conclusions:

  • The discriminant function-based approach is a viable method for estimating odds ratios from pooled biomarker data.
  • Accounting for processing and measurement errors is crucial for accurate inference in pooled studies.
  • This method offers an efficient and unbiased estimator, particularly valuable in large-scale epidemiological research.