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Misclassified group-tested current status data.

L C Petito1, N P Jewell1

  • 1Division of Biostatistics, School of Public Health, 101 Haviland Hall, University of California, Berkeley, California 94720, U.S.A.jewell@berkeley.edu.

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Group testing enhances disease prevalence estimation, especially for low prevalence and misclassification, by analyzing pooled samples. This method offers more precise estimates for time-to-event data, like age at infection.

Keywords:
Current status dataExpectation-maximization algorithmGroup testingPool-adjacent-violators algorithm

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical modeling

Background:

  • Group testing reduces costs for estimating disease prevalence using pooled samples.
  • Current status data involves a binary response related to a time-to-event variable and an observed screening time.
  • Nonparametric estimation of survival functions is possible with current status data.

Purpose of the Study:

  • To develop and evaluate a nonparametric estimator for time-to-event distributions using group-tested current status data.
  • To compare the performance of the group-based estimator against the individual testing estimator, particularly concerning precision in the presence of misclassification.

Main Methods:

  • Utilized group testing on current status data, where a group tests positive if any individual's event time is less than the observed screening time.
  • Employed the pool-adjacent-violators algorithm for nonparametric estimation.
  • Investigated the precision of the group-based estimator versus the individual test nonparametric maximum likelihood estimator.

Main Results:

  • The group-based estimator can be more precise than individual testing when misclassification is present and prevalence is low.
  • Demonstrated the applicability of the method to estimate age-at-incidence curves.

Conclusions:

  • Group testing offers a cost-effective and potentially more precise method for estimating time-to-event distributions from current status data, especially in scenarios with low prevalence and misclassification.
  • The developed estimator has practical applications in public health, such as analyzing disease incidence over time.