Comparing outcomes from tailored meta-analysis with outcomes from a setting specific test accuracy study using...

Meta-analyses of test accuracy studies may provide estimates that are highly improbable in clinical practice. Tailored meta-analysis produces plausible estimates for the accuracy of a test within a specific setting by tailoring the selection of included studies compatible with a specific setting using information from the target setting. The aim of this study was to validate the tailored meta-analysis approach by comparing outcomes from tailored meta-analysis with outcomes from a setting specific test accuracy study.

A retrospective cohort study of primary care electronic health records provided setting-specific data on the test positive rate and disease prevalence. This was used to tailor the study selection from a review of faecal calprotectin testing for inflammatory bowel disease for meta-analysis using the binomial method and the Mahalanobis distance method. Tailored estimates were compared to estimates from a study of test accuracy in primary care using the same routine dataset.

Tailoring resulted in the inclusion of 3/14 (binomial method) and 9/14 (Mahalanobis distance method) studies in meta-analysis. Sensitivity and specificity from tailored meta-analysis using the binomial method were 0.87 (95% CI 0.77 to 0.94) and 0.65 (95% CI 0.60 to 0.69) and 0.98 (95% CI 0.83 to 0.999) and 0.68 (95% CI 0.65 to 0.71), respectively using the Mahalanobis distance method. The corresponding estimates for the conventional meta-analysis were 0.94 (95% CI 0.90 to 0.97) and 0.67 (95% CI 0.57 to 0.76) and for the FC test accuracy study of primary care data 0.93 (95%CI 0.89 to 0.96) and 0.61 (95% CI 0.6 to 0.63) to detect IBD at a threshold of 50 microg/g. Although the binomial method produced a plausible estimate, the tailored estimates of sensitivity and specificity were not closer to the primary study estimates than the estimates from conventional meta-analysis including all 14 studies.

Tailored meta-analysis does not always produce estimates of sensitivity and specificity that lie closer to the estimates derived from a primary study in the setting in question. Potentially, tailored meta-analysis may be improved using a constrained model approach and this requires further investigation.