A simple and robust method for multivariate meta-analysis of diagnostic test accuracy.

TitleA simple and robust method for multivariate meta-analysis of diagnostic test accuracy.
Publication TypeJournal Article
Year of Publication2017
AuthorsChen, Yong, Yulun Liu, Haitao Chu, Mei-Ling Ting Lee, and Christopher H. Schmid
JournalStat Med
Volume36
Issue1
Pagination105-121
Date Published2017 01 15
ISSN1097-0258
KeywordsComputer Simulation, Diagnostic Tests, Routine, Female, Humans, Meta-Analysis as Topic, Multivariate Analysis, Neoplasm Recurrence, Local, Ovarian Neoplasms, Research Design
Abstract

Meta-analysis of diagnostic test accuracy often involves mixture of case-control and cohort studies. The existing bivariate random-effects models, which jointly model bivariate accuracy indices (e.g., sensitivity and specificity), do not differentiate cohort studies from case-control studies and thus do not utilize the prevalence information contained in the cohort studies. The recently proposed trivariate generalized linear mixed-effects models are only applicable to cohort studies, and more importantly, they assume a common correlation structure across studies and trivariate normality on disease prevalence, test sensitivity, and specificity after transformation by some pre-specified link functions. In practice, very few studies provide justifications of these assumptions, and sometimes these assumptions are violated. In this paper, we evaluate the performance of the commonly used random-effects model under violations of these assumptions and propose a simple and robust method to fully utilize the information contained in case-control and cohort studies. The proposed method avoids making the aforementioned assumptions and can provide valid joint inferences for any functions of overall summary measures of diagnostic accuracy. Through simulation studies, we find that the proposed method is more robust to model misspecifications than the existing methods. We apply the proposed method to a meta-analysis of diagnostic test accuracy for the detection of recurrent ovarian carcinoma. Copyright © 2016 John Wiley & Sons, Ltd.

DOI10.1002/sim.7093
Alternate JournalStat Med
Original PublicationA simple and robust method for multivariate meta-analysis of diagnostic test accuracy.
PubMed ID27580758
PubMed Central IDPMC6143393
Grant ListR03 DE024750 / DE / NIDCR NIH HHS / United States
P30 CA077598 / CA / NCI NIH HHS / United States
R03 HS020666 / HS / AHRQ HHS / United States
R21 LM012197 / LM / NLM NIH HHS / United States
R21 AI103012 / AI / NIAID NIH HHS / United States
R03 HS022900 / HS / AHRQ HHS / United States
P01 CA142538 / CA / NCI NIH HHS / United States
Project: