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Group testing regression analysis with covariates and specimens subject to missingness.

Aurore Delaigle1, Ruoxu Tan1,2

  • 1School of Mathematics and Statistics, University of Melbourne, 3010, Victoria, Parkville, Australia.

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This study introduces new methods for estimating disease prevalence using group testing, even when data is missing. The parametric estimators handle missing covariate data, improving accuracy in public health surveys.

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Public Health

Background:

  • Group testing reduces the number of diagnostic tests by pooling specimens.
  • Estimating conditional prevalence from group testing data is crucial for disease surveillance.
  • Existing methods often fail with missing data, limiting their applicability.

Purpose of the Study:

  • To develop parametric estimators for conditional prevalence in group testing with missing data.
  • To address non-monotone missingness in both specimens and covariates.
  • To establish conditions for valid estimation under missing data models.

Main Methods:

  • Development of parametric estimators for conditional prevalence.
  • Establishment of identifiability conditions for a logistic missing-not-at-random model.
  • Introduction of a missing-at-random model for covariate data.

Main Results:

  • The proposed estimators are valid for group testing data with missing covariates.
  • Identifiability conditions were established for specific missing data models.
  • Numerical challenges were noted when multiple covariates are missing.

Conclusions:

  • The developed methods offer a robust approach to prevalence estimation in group testing with missing data.
  • The study highlights the importance of accounting for missing data mechanisms.
  • Further research is needed to overcome practical numerical challenges with extensive missing covariates.