Head-to-head Comparison of the Diagnostic Accuracy of Prostate-specific Membrane Antigen Positron Emission Tomography and Conventional Imaging Modalities for Initial Staging of Intermediate- to High-risk Prostate Cancer: A Systematic Review and Meta-analysis

Conventional imaging using CT and bone scan has insufficient sensitivity when staging men with high-risk localised prostate cancer. We aimed to investigate whether novel imaging using prostate-specific membrane antigen (PSMA) PET-CT might improve accuracy and affect management.

Studies of diagnostic accuracy most often report pairs of sensitivity and specificity. We demonstrate the advantage of using bivariate meta-regression models to analyze such data. We discuss the methodology of both the summary Receiver Operating Characteristic (sROC) and the bivariate approach by reanalyzing the data of a published meta-analysis. The sROC approach is the standard method for meta-analyzing diagnostic studies reporting pairs of sensitivity and specificity. This method uses the diagnostic odds ratio as the main outcome measure, which removes the effect of a possible threshold but at the same time loses relevant clinical information about test performance. The bivariate approach preserves the two-dimensional nature of the original data. Pairs of sensitivity and specificity are jointly analyzed, incorporating any correlation that might exist between these two measures using a random effects approach. Explanatory variables can be added to the bivariate model and lead to separate effects on sensitivity and specificity, rather than a net effect on the odds ratio scale as in the sROC approach. The statistical properties of the bivariate model are sound and flexible. The bivariate model can be seen as an improvement and extension of the traditional sROC approach. Show more

Publication bias and other sample size effects are issues for meta-analyses of test accuracy, as for randomized trials. We investigate limitations of standard funnel plots and tests when applied to meta-analyses of test accuracy and look for improved methods. Type I and type II error rates for existing and alternative tests of sample size effects were estimated and compared in simulated meta-analyses of test accuracy. Type I error rates for the Begg, Egger, and Macaskill tests are inflated for typical diagnostic odds ratios (DOR), when disease prevalence differs from 50% and when thresholds favor sensitivity over specificity or vice versa. Regression and correlation tests based on functions of effective sample size are valid, if occasionally conservative, tests for sample size effects. Empirical evidence suggests that they have adequate power to be useful tests. When DORs are heterogeneous, however, all tests of funnel plot asymmetry have low power. Existing tests that use standard errors of odds ratios are likely to be seriously misleading if applied to meta-analyses of test accuracy. The effective sample size funnel plot and associated regression test of asymmetry should be used to detect publication bias and other sample size related effects. Show more