Repeated-measures modeling improved comparison of diagnostic tests in meta-analysis of dependent studies

Current methods for meta-analysis of diagnostic tests do not allow utilizing all the information from papers in which several tests have been studied on the same patient sample. We demonstrate how to combine several studies of diagnostic tests, where each study reports on more than one test and some tests (but not necessarily all of them) are shared with other papers selected for the meta-analysis. We adopt statistical methodology for repeated measurements for the purpose of meta-analysis of diagnostic tests.

The method allows for missing values of some tests for some papers, takes into account different sample sizes of papers, adjusts for background and confounding factors including test-specific covariates and paper-specific covariates, and accounts for correlations of the repeated measurements within each paper. It does not need individual-level data, although it can be modified to use them, and uses the two-by-two table of test results vs. gold standard.

The results are translated from diagnostic odds ratios (DOR) to more clinically useful measures such as predictive values, post-test probabilities, and likelihood ratios. Models to capture between-study variation are introduced. The fit and influence of specific studies on the regression can be evaluated. Furthermore, model-based tests for homogeneity of DORs across papers are presented.

The use of this new method is illustrated using a recent meta-analysis of the D-dimer test for the diagnosis of deep venous thrombosis.