Meta-DiSc: a software for meta-analysis of test accuracy data

Abstract

Background: Systematic reviews and meta-analyses of test accuracy studies are increasingly being recognised as central in guiding clinical practice. However, there is currently no dedicated and comprehensive software for meta-analysis of diagnostic data. In this article, we present Meta-DiSc, a Windows-based, user-friendly, freely available (for academic use) software that we have developed, piloted, and validated to perform diagnostic meta-analysis.

Results: Meta-DiSc a) allows exploration of heterogeneity, with a variety of statistics including chi-square, I-squared and Spearman correlation tests, b) implements meta-regression techniques to explore the relationships between study characteristics and accuracy estimates, c) performs statistical pooling of sensitivities, specificities, likelihood ratios and diagnostic odds ratios using fixed and random effects models, both overall and in subgroups and d) produces high quality figures, including forest plots and summary receiver operating characteristic curves that can be exported for use in manuscripts for publication. All computational algorithms have been validated through comparison with different statistical tools and published meta-analyses. Meta-DiSc has a Graphical User Interface with roll-down menus, dialog boxes, and online help facilities.

Conclusion: Meta-DiSc is a comprehensive and dedicated test accuracy meta-analysis software. It has already been used and cited in several meta-analyses published in high-ranking journals. The software is publicly available at http://www.hrc.es/investigacion/metadisc_en.htm.

Figure 1

Available tools in Meta-DiSc. Tools implemented in the software Meta-DiSc to perform different steps of meta-analysis of diagnostic tests accuracy.

Figure 2

Meta-Disc datasheet. Meta-DiSc data set with details of test accuracy studies of ultrasound in the prediction of endometrial cancer.

Figure 3

Forest plot. Forrest plot of sensitivities (3a) and specificities (3b) from test accuracy studies of ultrasound in the prediction of endometrial cancer.

Figure 4

Forest plot. Forrest plot of likelihood ratios for positive (4a) and negative (4b) test results from studies of ultrasound in the prediction of endometrial cancer.

Figure 5

Forrest plot. Forest plot of diagnostic odds ratios (dOR) from test accuracy studies of ultrasound in the prediction of endometrial cancer.

Figure 6

ROC Space. Representation of sensitivity against (1-specificity) in Receiver Operating Characteristics space for each study of ultrasound in the prediction of endometrial cancer.

Figure 7

Forrest plot. Forrest plots of Likelihood ratios for positive (7a) and negative (7b) test results in one homogenous subgroup of studies of non-HRT users, with a test threshold of ≤ 5 mm, and using a single layer technique.

Figure 8

sROC curve. Receiver operating characteristics curve for all studies included in systematic review of ultrasound for prediction of endometrial cancer.