Latent class modeling approaches for assessing diagnostic error without a gold standard: With applications to p53 immunohistochemical assays in bladder tumors

Paul S. Albert, Lisa M. McShane, Joanna H. Shih, R. Aamodt, C. Cordon-Cardo, R. Cote, Y. Fradet, H. B. Grossman, F. Waldman

Research output: Contribution to journalReview article

57 Scopus citations

Abstract

Improved characterization of tumors for purposes of guiding treatment decisions for cancer patients will require that accurate and reproducible assays be developed for a variety of tumor markers. No gold standards exist for most tumor marker assays. Therefore, estimates of assay sensitivity and specificity cannot be obtained unless a latent class model-based approach is used. Our goal in this article is to estimate sensitivity and specificity for p53 immunohistochemical assays of bladder tumors using data from a reproducibility study conducted by the National Cancer Institute Bladder Tumor Marker Network. We review latent class modeling approaches proposed by previous authors, and we find that many of these approaches impose assumptions about specimen heterogeneity that are not consistent with the biology of bladder tumors. We present flexible mixture model alternatives that are biologically plausible for our example, and we use them to estimate sensitivity and specificity for our p53 assay example. These mixture models are shown to offer an improvement over other methods in a variety of settings, but we caution that, in general, care must be taken in applying latent class models.

Original languageEnglish (US)
Pages (from-to)610-619
Number of pages10
JournalBiometrics
Volume57
Issue number2
DOIs
StatePublished - Jun 2001

Keywords

  • Biomarkers
  • Misclassification
  • Repeated binary data
  • Sensitivity
  • Specificity

ASJC Scopus subject areas

  • Agricultural and Biological Sciences(all)
  • Public Health, Environmental and Occupational Health
  • Agricultural and Biological Sciences (miscellaneous)
  • Applied Mathematics
  • Statistics and Probability

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