Many different methods for evaluating diagnostic test results in the absence of a gold standard have been proposed. In this paper, we discuss how one common method, a maximum likelihood estimate for a latent class model found via the Expectation-Maximization (EM) algorithm can be applied to longitudinal data where test sensitivity changes over time. We also propose two simplified and nonparametric methods which use data-based indicator variables for disease status and compare their accuracy to the maximum likelihood estimation (MLE) results. We find that with high specificity tests, the performance of simpler approximations may be just as high as the MLE.
Bibliographical noteFunding Information:
This project has been funded in whole or in part with federal funds from the National Cancer Institute, National Institutes of Health, under Contract No. HHSN261200800001E. The content of this manuscript does not necessarily reflect the view or policies of the Department of Health and Human Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government.
© 2022 The Authors. Biometrics published by Wiley Periodicals LLC on behalf of International Biometric Society.
- Ebola virus disease
- diagnostic testing
- latent class model
- multiple testing
- nongold-standard test
- nonparametric model
PubMed: MeSH publication types
- Journal Article
- Research Support, N.I.H., Extramural