The impact of including different study designs in meta-analyses of diagnostic accuracy studies.

Diagnostic accuracy may be overestimated when using certain study designs; thus, the inclusion of studies using different designs in meta-analyses may have important effects on their results, and influence clinical decision making. The main aim of this study was to explore the influence of heterogeneity (based on the inclusion of different study designs) on diagnostic accuracy in a sample of published meta-analyses of diagnostic accuracy studies. We identified 30 systematic reviews which included 95 separate meta-analyses combining the results from a total of 976 individual studies. We classified each individual study according to the study design (case-control studies, clinically relevant patient series or other), and each meta-analysis according to the heterogeneity of the included studies. Furthermore, we registered how the methodological quality of the individual studies was assessed. Finally, for each meta-analysis, the summary measure of diagnostic accuracy was categorised as Good, Fair or Poor. We used logistic regression to assess the relationship between reporting good diagnostic accuracy and heterogeneity. Meta-analyses with heterogeneous populations were over three times more likely to report good diagnostic accuracy compared to meta-analyses that included only clinically relevant patient series (adjusted odds ratio 3.07 95% CI 1.16-8.11). The combination of studies that use different designs, within the same meta-analysis, may lead to higher estimates of diagnostic accuracy.