Modeling continuous diagnostic test data using approximate Dirichlet process distributions
- 22 July 2011
- journal article
- Published by Wiley in Statistics in Medicine
- Vol. 30 (21), 2648-2662
- https://doi.org/10.1002/sim.4320
Abstract
There is now a large literature on the analysis of diagnostic test data. In the absence of a gold standard test, latent class analysis is most often used to estimate the prevalence of the condition of interest and the properties of the diagnostic tests. When test results are measured on a continuous scale, both parametric and nonparametric models have been proposed. Parametric methods such as the commonly used bi‐normal model may not fit the data well; nonparametric methods developed to date have been relatively complex to apply in practice, and their properties have not been carefully evaluated in the diagnostic testing context. In this paper, we propose a simple yet flexible Bayesian nonparametric model which approximates a Dirichlet process for continuous data. We compare results from the nonparametric model with those from the bi‐normal model via simulations, investigating both how much is lost in using a nonparametric model when the bi‐normal model is correct and how much can be gained in using a nonparametric model when normality does not hold. We also carefully investigate the trade‐offs that occur between flexibility and identifiability of the model as different Dirichlet process prior distributions are used. Motivated by an application to tuberculosis clustering, we extend our nonparametric model to accommodate two additional dichotomous tests and proceed to analyze these data using both the continuous test alone as well as all three tests together. Copyright © 2011 John Wiley & Sons, Ltd.Keywords
This publication has 28 references indexed in Scilit:
- On Model Expansion, Model Contraction, Identifiability and Prior Information: Two Illustrative Scenarios Involving Mismeasured VariablesStatistical Science, 2005
- Modelling risk when binary outcomes are subject to errorStatistics in Medicine, 2004
- Diversity of Mycobacterium tuberculosis isolates in an immigrant population: evidence against a founder effect.American Journal of Epidemiology, 2004
- Estimating disease prevalence in the absence of a gold standardStatistics in Medicine, 2002
- Screening without a "Gold Standard": The Hui-Walter Paradigm RevisitedAmerican Journal of Epidemiology, 2001
- High-resolution minisatellite-based typing as a portable approach to global analysis ofMycobacterium tuberculosismolecular epidemiologyProceedings of the National Academy of Sciences of the United States of America, 2001
- Using a combination of reference tests to assess the accuracy of a new diagnostic testStatistics in Medicine, 1999
- Typing of mycobacteria using spoligotypingThorax, 1998
- Bayesian Estimation of Disease Prevalence and the Parameters of Diagnostic Tests in the Absence of a Gold StandardAmerican Journal of Epidemiology, 1995
- Estimation of test error rates, disease prevalence and relative risk from misclassified data: a reviewJournal of Clinical Epidemiology, 1988