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Adjusting for Non-Ignorable Verification Bias in Clinical Studies for Alzheimer's Disease

Author

Listed:
  • Xiao-Hua Zhou

    (University of Washington)

  • Pete Castelluccio

    (Purdue University)

Abstract

A common problem for comparing the relative accuracy of two screening tests for Alzheimer's disease (D) in a two-stage design study is verification bias. If the verification bias can be assumed to be ignorable, Zhou and Higgs (2000) have proposed a maximum likelihood approach to compare the relative accuracy of screening tests in a two-stage design study. However, if the verification mechanism also depends on the unobserved disease status, the ignorable assumption does not hold. In this paper, we discuss how to use a profile likelihood approach to compare the relative accuracy of two screening tests for AD without assuming the ignorable verification bias mechanism.

Suggested Citation

  • Xiao-Hua Zhou & Pete Castelluccio, 2004. "Adjusting for Non-Ignorable Verification Bias in Clinical Studies for Alzheimer's Disease," UW Biostatistics Working Paper Series 1044, Berkeley Electronic Press.
    • Handle: RePEc:bep:uwabio:1044
    Note: oai:bepress.com:uwbiostat-1044
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    File URL: http://www.bepress.com/cgi/viewcontent.cgi?article=1044&context=uwbiostat
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    Citations

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    Cited by:

    1. Khanh To Duc & Monica Chiogna & Gianfranco Adimari, 2019. "Estimation of the volume under the ROC surface in presence of nonignorable verification bias," Statistical Methods & Applications, Springer;Società Italiana di Statistica, vol. 28(4), pages 695-722, December.
    2. Frederico Z. Poleto & Julio M. Singer & Carlos Daniel Paulino, 2011. "Comparing diagnostic tests with missing data," Journal of Applied Statistics, Taylor & Francis Journals, vol. 38(6), pages 1207-1222, April.

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