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Bayesian Additive Regression Trees for Group Testing Data.

Madeleine E St Ville1, Christopher S McMahan2, Joe D Bible2

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Summary
This summary is machine-generated.

Group testing significantly reduces disease screening costs. This study introduces a flexible Bayesian method to accurately model disease risk using group testing data, even with imperfect tests.

Keywords:
decision treeslatent variable modelingmachine learningnon‐parametric regressionpooled testing

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Medical Diagnostics

Background:

  • Group testing offers cost savings for low-prevalence disease screening compared to individual testing.
  • Estimating individual disease risk from group test data is challenging due to unknown statuses and potential test errors.
  • Existing regression methods often assume known covariate effect forms, risking model misspecification.

Purpose of the Study:

  • To develop a flexible Bayesian framework for modeling individual disease probability using group testing data.
  • To address challenges posed by unknown individual statuses and imperfect assay classifications in group testing.
  • To estimate unknown covariate functions and assay accuracy probabilities within any group testing design.

Main Methods:

  • Proposed a Bayesian additive regression trees (BART) framework.
  • Applied BART to model individual-level disease probability with group testing data.
  • Accommodated potentially misclassified test results and unknown covariate effect functions.

Main Results:

  • The BART framework provides a flexible approach to group testing data analysis.
  • Successfully estimated unknown covariate effects and assay classification probabilities.
  • Demonstrated utility across various group testing protocols.

Conclusions:

  • The proposed Bayesian additive regression trees method offers a robust and flexible solution for analyzing group testing data.
  • This approach enhances the estimation of disease risk and covariate relationships, even with imperfect testing.
  • The methods are applicable to diverse group testing scenarios, improving diagnostic accuracy and cost-effectiveness.