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Estimation of interaction effects using pooled biospecimens in a case-control study.

Michelle R Danaher1,2, Paul S Albert1, Aninyda Roy2

  • 1Eunice Kennedy Shriver National Institute of Child Health and Human Development, Bethesda, Maryland, U.S.A.

Statistics in Medicine
|November 11, 2015
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Summary
This summary is machine-generated.

Pooling biospecimens reduces costs and sample needs. The expectation maximization (EM) algorithm effectively estimates logistic regression interaction effects for pooled continuous exposures, offering a promising approach for biomarker studies.

Keywords:
cytokinesexpectation maximizationlogistic regressionpooling designsskewed biomarkers

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Area of Science:

  • Biostatistics
  • Biomarker Discovery
  • Epidemiology

Background:

  • Pooling biospecimens offers cost and efficiency benefits in biomarker analysis.
  • Existing set-based logistic models effectively estimate main effects but not interaction effects for pooled continuous exposures.
  • A case-control study on miscarriage and cytokine levels motivates the need for analyzing pooled continuous exposures.

Purpose of the Study:

  • To propose and evaluate the expectation maximization (EM) algorithm for estimating parameters, including interaction effects, in logistic regression models with pooled continuous exposures.
  • To compare the efficiency of the EM algorithm with other methods, such as individual biospecimen measurement and random sampling, under various scenarios.

Main Methods:

  • Development and application of the expectation maximization (EM) algorithm for logistic regression with pooled continuous exposures.
  • Simulation studies comparing the efficiency of the EM algorithm against individual measurements and random sampling of biospecimens.
  • Analysis of biospecimens pooled by disease status (case-control) for continuous cytokine exposures.

Main Results:

  • The EM algorithm successfully estimates all logistic regression parameters, including interaction effects, for pooled continuous exposures.
  • Pooling pairs of biospecimens stratified by disease status is more efficient than randomly sampling half of the biospecimens.
  • The EM algorithm provides a viable method for estimating interaction effects when biospecimens are already pooled.

Conclusions:

  • The expectation maximization (EM) algorithm is a valuable tool for estimating interaction effects in logistic regression models involving pooled continuous exposures.
  • The EM algorithm enables comprehensive analysis of pooled biospecimens, even when interactions are of interest.
  • This approach enhances the utility of biospecimen pooling strategies in biomarker research and epidemiological studies.