The Moses-Littenberg meta-analytical method generates systematic differences in test accuracy compared to hierarchical meta-analytical models

Objective: To compare meta-analyses of diagnostic test accuracy using the Moses-Littenberg summary ROC (SROC) approach with those of the hierarchical SROC (HSROC) model.
Study design and setting: Twenty-six datasets from existing test accuracy systematic reviews were reanalysed with the Moses-Littenberg model, using equal weighting (‘E-ML’) and weighting by the inverse variance of the logDOR (‘W-ML’), and with the HSROC model. The diagnostic odds ratios (DOR) were estimated and covariates added to both models to estimate relative DORs (RDORs) between subgroups. Models were compared by calculating the ratio of DORs, the ratio of RDORs and P-values for detecting asymmetry and effects of covariates on DOR.
Results: Compared to the HSROC model, the Moses-Littenberg model DOR estimates were a median of 22% (‘E-ML’) and 47% (‘W-ML’) lower at Q*, and 7% and 42% lower at the central point in the data. Instances of the ML-models giving estimates higher than the HSROC model also occurred. Investigations of heterogeneity also differed; the Moses-Littenberg models on average estimating smaller differences in RDOR.
Conclusions: Moses-Littenberg meta-analyses can generate lower estimates of test accuracy, and smaller differences in accuracy, compared to mathematically superior hierarchical models. This has implications for the usefulness of meta-analyses employing this approach. We recommend meta-analysis of DTA studies to be conducted using available hierarchical model based approaches.