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Computerized Adaptive Testing System of Functional Assessment of Stroke
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Adaptive elastic net for group testing.

Karl B Gregory1, Dewei Wang1, Christopher S McMahan2

  • 1Department of Statistics, University of South Carolina, Columbia, South Carolina.

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|September 30, 2018
PubMed
Summary
This summary is machine-generated.

Group testing offers cost savings for disease screening by pooling biospecimens. This study introduces a novel regression method with an adaptive elastic net estimator for accurate disease surveillance and variable selection in group testing.

Keywords:
Group testingadaptive elastic netmodel selection

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Public Health

Background:

  • Group testing is a cost-effective strategy for disease screening, particularly when analyzing pooled biospecimens.
  • Disease surveillance often involves relating individual disease statuses to covariates using binary regression, complicated by imperfect testing and unobserved true statuses.
  • Existing regression methods for group testing data face challenges due to misclassification and complex testing structures.

Purpose of the Study:

  • To develop a novel, generalized regression methodology for group testing data that incorporates regularization.
  • To propose an adaptive elastic net estimator for robust model fitting and variable selection in group testing.
  • To provide an efficient algorithm for computing the estimator and guidance on parameter selection.

Main Methods:

  • The study proposes an adaptive elastic net estimator within a logistic regression framework.
  • An efficient algorithm is developed for computing the proposed estimator.
  • Asymptotic properties of the estimator are established, demonstrating "oracle" properties.

Main Results:

  • The novel regression methodology generalizes and extends existing techniques for group testing data.
  • The adaptive elastic net estimator demonstrates "oracle" properties, indicating strong performance in model fitting and variable selection.
  • Monte Carlo studies confirm the effectiveness of the proposed estimator.

Conclusions:

  • The developed methodology offers a powerful tool for analyzing complex group testing data in disease surveillance.
  • The adaptive elastic net estimator provides a statistically sound and efficient approach for regression with imperfect group testing data.
  • The method's utility is demonstrated through application to a real-world chlamydia dataset.