Global goodness-of-fit tests for group testing regression models

Peng Chen, Joshua M. Tebbs, Christopher R. Bilder

Research output: Contribution to journalArticlepeer-review

4 Scopus citations

Abstract

In a variety of biomedical applications, particularly those involving screening for infectious diseases, testing individuals (e.g. blood/urine samples, etc.) in pools has become a standard method of data collection. This experimental design, known as group testing (or pooled testing), can provide a large reduction in testing costs and can offer nearly the same precision as individual testing. To account for covariate information on individual subjects, regression models for group testing data have been proposed recently. However, there are currently no tools available to check the adequacy of these models. In this paper, we present various global goodness-of-fit tests for regression models with group testing data. We use simulation to examine the small-sample size and power properties of the tests for different pool composition strategies. We illustrate our methods using two infectious disease data sets, one from an HIV study in Kenya and one from the Infertility Prevention Project.

Original languageEnglish (US)
Pages (from-to)2912-2928
Number of pages17
JournalStatistics in Medicine
Volume28
Issue number23
DOIs
StatePublished - Oct 15 2009

Keywords

  • IOS test
  • Infertility prevention project
  • Latent binary response
  • Logistic regression
  • Pooled testing
  • Score test

ASJC Scopus subject areas

  • Epidemiology
  • Statistics and Probability

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