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Semiparametric isotonic regression modelling and estimation for group testing data.

Ao Yuan1, Jin Piao2, Jing Ning3

  • 1Department of Biostatistics, Bioinformatics & Biomathematics, Georgetown University, Washington, DC USA.

The Canadian Journal of Statistics = Revue Canadienne De Statistique
|October 25, 2021
PubMed
Summary
This summary is machine-generated.

Group testing efficiently screens samples but presents statistical challenges. This study introduces a semiparametric isotonic regression model and an expectation-maximization algorithm to address these challenges in estimating disease status.

Keywords:
Expectation-maximization algorithmgroup testing dataisotonic regressionpool-adjacent violators algorithm

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Area of Science:

  • Biostatistics
  • Epidemiology
  • Statistical Modeling

Background:

  • Group testing offers cost-effective sample screening for outcomes like infectious diseases.
  • Traditional methods face statistical and computational challenges with semiparametric models in group testing.

Purpose of the Study:

  • To develop efficient estimators for semiparametric isotonic regression models in group testing.
  • To simultaneously estimate conditional probability curves and covariate effects.

Main Methods:

  • Utilized semiparametric isotonic regression with a parametric covariate combination and unspecified monotonic link function.
  • Developed an expectation-maximization algorithm incorporating the pool-adjacent violators algorithm for computation.
  • Established theoretical large sample properties of the estimators.

Main Results:

  • The proposed expectation-maximization algorithm effectively addresses computational challenges in group testing.
  • Simulation studies demonstrated the finite sample performance of the developed estimators.
  • The method was successfully applied to National Health and Nutrition Examination Survey data.

Conclusions:

  • The novel semiparametric approach provides statistically and computationally efficient estimation for group testing.
  • This method enhances the analysis of large-scale health survey data using group testing strategies.