A hybrid Bayesian hierarchical model combining cohort and case-control studies for meta-analysis of diagnostic tests: Accounting for partial verification bias.

TitleA hybrid Bayesian hierarchical model combining cohort and case-control studies for meta-analysis of diagnostic tests: Accounting for partial verification bias.
Publication TypeJournal Article
Year of Publication2016
AuthorsMa, Xiaoye, Yong Chen, Stephen R. Cole, and Haitao Chu
JournalStat Methods Med Res
Volume25
Issue6
Pagination3015-3037
Date Published2016 12
ISSN1477-0334
KeywordsBayes Theorem, Bias, Case-Control Studies, Cohort Studies, Diagnostic Tests, Routine, Humans, Lymphatic Metastasis, Magnetic Resonance Imaging, Meta-Analysis as Topic, Sensitivity and Specificity
Abstract

To account for between-study heterogeneity in meta-analysis of diagnostic accuracy studies, bivariate random effects models have been recommended to jointly model the sensitivities and specificities. As study design and population vary, the definition of disease status or severity could differ across studies. Consequently, sensitivity and specificity may be correlated with disease prevalence. To account for this dependence, a trivariate random effects model had been proposed. However, the proposed approach can only include cohort studies with information estimating study-specific disease prevalence. In addition, some diagnostic accuracy studies only select a subset of samples to be verified by the reference test. It is known that ignoring unverified subjects may lead to partial verification bias in the estimation of prevalence, sensitivities, and specificities in a single study. However, the impact of this bias on a meta-analysis has not been investigated. In this paper, we propose a novel hybrid Bayesian hierarchical model combining cohort and case-control studies and correcting partial verification bias at the same time. We investigate the performance of the proposed methods through a set of simulation studies. Two case studies on assessing the diagnostic accuracy of gadolinium-enhanced magnetic resonance imaging in detecting lymph node metastases and of adrenal fluorine-18 fluorodeoxyglucose positron emission tomography in characterizing adrenal masses are presented.

DOI10.1177/0962280214536703
Alternate JournalStat Methods Med Res
Original PublicationA hybrid Bayesian hierarchical model combining cohort and case-control studies for meta-analysis of diagnostic tests: Accounting for partial verification bias.
PubMed ID24862512
PubMed Central IDPMC4245380
Grant ListP30 CA077598 / CA / NCI NIH HHS / United States
U54 MD008620 / MD / NIMHD 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
R01 AI130460 / AI / NIAID NIH HHS / United States
Project: