A generalized meta-analysis model for binary diagnostic test performance
Methods for meta-analysis of diagnostic test accuracy studies must, in addition to unobserved heterogeneity, account for covariate heterogeneity, threshold effects, methodological quality and small study bias, whic constitute the major threats to the validity of meta-analytic results. These have traditionally been addressed independent of each other. Two recent methodological advances include (1) the bivariate random-effects model for joint synthesis of sensitivity and specificity, which accounts for unobsrved heterogeneity and threshold variation using random-effects and covariate and qualty effects as indepedent variables in a meta-regression; and (2) a linear regression test for funnel plot asymmetry in which the diagnostic odds ratio as effect size measure is regressed on effective sample size as a precision measure. I propose a generalized framework for diagnostic meta-analysis which integrates both developments based on a modification of the bivariate Dale's model in which two univariate random-effects logistic models for sensitivity and specificity are associated through a log-linear model of odds ratios with the effective sample size as an independent variable,. This unifies the estimation of summary test performance and assessment of the presence, extent and sources of variability. Taking advantage of the ability of gllamm to model a mixture of discrete and continous outcomes, I will discuss specification, estimation, diagnostics and prediction of the model, using a motivating dataset of 43 studies investigating FDG-PET for staging the axilla in patients with newly-diagnosed breast cancer.
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