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Positing, fitting, and selecting regression models for pooled biomarker data.

Emily M Mitchell1, Robert H Lyles2, Enrique F Schisterman1

  • 1Division of Intramural Population Health Research, Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, 20892, MD, U.S.A.

Statistics in Medicine
|April 8, 2015
PubMed
Summary
This summary is machine-generated.

Pooling biospecimens reduces costs and preserves samples. New regression models and Akaike

Keywords:
AICMCEMbiomarkersgammapooled specimensskewness

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Area of Science:

  • Epidemiology
  • Biostatistics

Background:

  • Biospecimen pooling reduces laboratory costs and preserves precious samples.
  • Analysis of pooled data requires specialized statistical methods due to right-skewed biomarker distributions and detection limits.

Purpose of the Study:

  • To develop and compare parametric regression models for analyzing skewed, pooled biospecimen data.
  • To introduce a novel gamma distribution parameterization for pooled data analysis.
  • To adapt Akaike's Information Criterion (AIC) for model selection with pooled data.

Main Methods:

  • Development of parametric regression models for skewed, pooled data.
  • Introduction of a gamma distribution parameterization leveraging its summation property.
  • Application of a Monte Carlo approximation of AIC for model selection in pooled data analysis.

Main Results:

  • The novel gamma distribution parameterization effectively handles pooled, skewed data.
  • AIC approximation provides a reliable method for selecting the best parametric model.
  • Simulation studies and real-world data analysis confirm the utility of the proposed methods.

Conclusions:

  • Parametric regression models combined with AIC offer valid inference and improved precision for pooled biospecimen data.
  • The proposed methods are valuable for epidemiological studies utilizing pooled biospecimens.