title: Methods for the joint meta-analysis of multiple tests.
abstract: Existing methods for meta-analysis of diagnostic test accuracy focus primarily on a single index test. We propose models for the joint meta-analysis of studies comparing multiple index tests on the same participants in paired designs. These models respect the grouping of data by studies, account for the within-study correlation between the tests' true-positive rates (TPRs) and between their false-positive rates (FPRs) (induced because tests are applied to the same participants), and allow for between-study correlations between TPRs and FPRs (such as those induced by threshold effects). We estimate models in the Bayesian setting. We demonstrate using a meta-analysis of screening for Down syndrome with two tests: shortened humerus (arm bone), and shortened femur (thigh bone). Separate and joint meta-analyses yielded similar TPR and FPR estimates. For example, the summary TPR for a shortened humerus was 35.3% (95% credible interval (CrI): 26.9, 41.8%) versus 37.9% (27.7, 50.3%) with joint versus separate meta-analysis. Joint meta-analysis is more efficient when calculating comparative accuracy: the difference in the summary TPRs was 0.0% (-8.9, 9.5%; TPR higher for shortened humerus) with joint versus 2.6% (-14.7, 19.8%) with separate meta-analyses. Simulation and empirical analyses are needed to refine the role of the proposed methodology.
authors.affiliation: Center for Evidence-based Medicine, School of Public Health, Brown University, Providence, RI, USA.|Department of Health Services, Policy & Practice, School of Public Health, Brown University, Providence, RI, USA.
authors.forename: Thomas A
authors.initials: TA
authors.lastname: Trikalinos
authors.affiliation: Center for Evidence-based Medicine, School of Public Health, Brown University, Providence, RI, USA.|Department of Quantitative Health Sciences, University of Massachusetts Medical School, Worcester, MA, USA.|Consulting Statistician, Sudbury, MA, USA.
authors.forename: David C
authors.initials: DC
authors.lastname: Hoaglin
authors.affiliation: Office of Portfolio Analysis, National Institutes of Health, Bethesda, MD, USA.
authors.forename: Kevin M
authors.initials: KM
authors.lastname: Small
authors.affiliation: Research Design Center/Biostatistics Research Center, Clinical and Translational Science Institute, Tufts University, and Institute for Clinical Research and Health Policy Studies, Tufts Medical Center, Boston, MA, USA.
authors.forename: Norma
authors.initials: N
authors.lastname: Terrin
authors.affiliation: Center for Evidence-based Medicine, School of Public Health, Brown University, Providence, RI, USA.|Department of Biostatistics, School of Public Health, Brown University, Providence, RI, USA.
authors.forename: Christopher H
authors.initials: CH
authors.lastname: Schmid
mesh_terms: D002986:Clinical Trials as Topic / Q000706:statistics & numerical data*; D003198:Computer Simulation; D003627:Data Interpretation, Statistical; D003933:Diagnosis; D016013:Likelihood Functions; D015201:Meta-Analysis as Topic; D015233:Models, Statistical; D015999:Multivariate Analysis; D017063:Outcome Assessment, Health Care / Q000379:methods*; D012044:Regression Analysis; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity
keywords: Bayesian analysis; joint meta-analysis; multinomial likelihood; multivariate normal; regularized regression; restricted maximum likelihood