Meta-analysis of diagnostic accuracy studies accounting for disease prevalence: alternative parameterizations and model selection.

Full Abstract

In a meta-analysis of diagnostic accuracy studies, the sensitivities and specificities of a diagnostic test may depend on the disease prevalence since the severity and definition of disease may differ from study to study due to the design and the population considered. In this paper, we extend the bivariate nonlinear random effects model on sensitivities and specificities to jointly model the disease prevalence, sensitivities and specificities using trivariate nonlinear random-effects models. Furthermore, as an alternative parameterization, we also propose jointly modeling the test prevalence and the predictive values, which reflect the clinical utility of a diagnostic test. These models allow investigators to study the complex relationship among the disease prevalence, sensitivities and specificities; or among test prevalence and the predictive values, which can reveal hidden information about test performance. We illustrate the proposed two approaches by reanalyzing the data from a meta-analysis of radiological evaluation of lymph node metastases in patients with cervical cancer and a simulation study. The latter illustrates the importance of carefully choosing an appropriate normality assumption for the disease prevalence, sensitivities and specificities, or the test prevalence and the predictive values. In practice, it is recommended to use model selection techniques to identify a best-fitting model for making statistical inference. In summary, the proposed trivariate random effects models are novel and can be very useful in practice for meta-analysis of diagnostic accuracy studies. Copyright 2009 John Wiley & Sons, Ltd.

Author information

Author/s: Chu, Haitao (H); Nie, Lei (L); Cole, Stephen R (SR); Poole, Charles (C);

Affiliation: Department of Biostatistics, The University of North Carolina at Chapel Hill, Chapel Hill, NC 27599, USA. hchu(-atsign-)bios.unc.edu

Grants: CA16086 (Agency:NCI NIH HHS) ; P30-AI-50410 (Agency:NIAID NIH HHS) ; P30ES10126 (Agency:NIEHS NIH HHS) ; R01-AA-01759 (Agency:NIAAA NIH HHS) ; R03-AI-071763 (Agency:NIAID NIH HHS)

Journal and publication information

Publication Type: Journal Article; Research Support, N.I.H., Extramural

Journal: Statistics in medicine (Stat Med), published in England. (Language: eng)

Reference: 2009-Aug; vol 28 (issue 18) : pp 2384-99

Dates: Created 2009/07/14; Completed 2009/10/01;

PMID: 19499551, status: MEDLINE (last retrieval date: 10/1/2009, IMS Date: )

Sourced from the National Library of Medicine. Abstract text and other information may be subject to copyright.

External Links for this article
(including full text providers, if available):

Click Electronic Full-text Provider Links to see options for finding the electronic full text links to this article. Note there may be a subscription or fee required for access to the full text. See our FAQ for information on finding FREE full text articles.

This article may also be located in paper journal collections available in many libraries. Use the Journal and Publication Information above to find the full article.