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Regression analysis of group-tested current status data.

Shuwei Li1, Tao Hu2, Lianming Wang3

  • 1School of Economics and Statistics, Guangzhou University, Daxuecheng Road 230, Guangzhou, Guangdong 510006, China.

Biometrika
|December 18, 2024
PubMed
Summary
This summary is machine-generated.

Group testing efficiently screens infectious diseases by pooling specimens. New statistical methods accurately analyze this group-tested current status data, improving upon individual testing analysis.

Keywords:
Cox proportional hazards modelDiagnostic testingGroup testingMaximum likelihoodMisclassificationSieve estimator

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Public Health

Background:

  • Group testing reduces costs and time for large-scale infectious disease screening by pooling specimens.
  • Current status data, where individuals are assessed once for a time-to-event endpoint, is common in such studies.
  • Analyzing group-tested current status data presents unique statistical challenges.

Purpose of the Study:

  • To develop and evaluate statistical methods for analyzing group-tested current status data.
  • To estimate proportional hazard regression models using pool test outcomes.
  • To provide a computationally efficient and statistically rigorous approach for group testing data.

Main Methods:

  • A sieve maximum likelihood estimation approach was developed, approximating the cumulative baseline hazard function.
  • A computationally efficient expectation-maximization algorithm using data augmentation was derived.
  • Modern empirical process theory was applied to establish asymptotic properties of the estimator.

Main Results:

  • The proposed sieve estimation method accurately analyzes group-tested current status data.
  • Simulation studies demonstrated the method's nominal performance and advantages over individual testing analysis.
  • The approach was successfully applied to a real-world chlamydia dataset.

Conclusions:

  • The developed statistical methods offer an effective way to analyze complex group-tested current status data.
  • This approach enhances the efficiency and accuracy of infectious disease screening analysis.
  • The findings have significant implications for public health surveillance and research.