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

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.

Keywords: Bayesian method; cohort and case–control studies; diagnostic test; meta-analysis; partial verification bias.

Figure 1

Quantile contours of posterior densities from estimates of the meta-analysis of gadolinium-enhanced MRI in detecting lymph node metastases assuming scaled Wishart prior. A–D plot posterior Se versus prevalence (π), Sp versus π, Se versus Sp and PPV versus NPV, respectively, at quantile levels 0.25, 0.5, 0.75, 0.9 and 0.95.

Figure 2

SROC curves from the Hybrid GLMM and the bivariate GLMM using MLE approach. Solid lines are the SROC curve from the hybrid GLMM estimates and the 95% prediction region for the summary point estimates of Se and Sp. Dashed lines are the SROC curve from the bivaraite estimates and the 95% prediction region for the summary point estimates of Se and Sp. Black and gray circles are the observed Se and Sp from studies with and without missing counts, respectively. Red and blue triangles are the posterior estimates of Se and Sp from the Hybrid GLMM and the Bivariate GLMM ignoring partial verification, respectively.

Figure 3

Density plots of posterior estimates of the meta-analysis of gadoliniumenhanced MRI in detecting lymph node metastases under different prior assumptions. Panel A plots posterior densities of Se, Sp and prevalence (π). Panel B plots posteriors densities of PPV and NPV.