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‘Cross hairs’ plots for diagnostic meta‐analysis
Author(s) -
Phillips Bob,
Stewart Lesley A.,
Sutton Alex J.
Publication year - 2010
Publication title -
research synthesis methods
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 3.376
H-Index - 35
eISSN - 1759-2887
pISSN - 1759-2879
DOI - 10.1002/jrsm.26
Subject(s) - plot (graphics) , receiver operating characteristic , sensitivity (control systems) , computer science , meta analysis , key (lock) , forest plot , publication bias , confidence interval , data mining , artificial intelligence , statistics , machine learning , data science , mathematics , medicine , pathology , computer security , electronic engineering , engineering
Understanding diagnostic test accuracy (DTA) data, especially meta‐analysis of this information, can be challenging for researchers and clinicians. The use of plots of receiver‐operator curve (ROC) space showing individual studies and summary estimates of diagnostic accuracy has become common but can be difficult to interpret. The assessment of heterogeneity in sensitivity and specificity across studies can be particularly difficult. In this paper, we review the key concepts of assessing DTA, starting at the level of individual studies and progressing to the setting of research synthesis. We explore the standard displays of this information and then propose and explain an alternative approach to summarizing key data. These ‘cross‐hairs’ plots display the individual studies in ROC space with paired confidence intervals representing sensitivity and specificity, and allow for the results of meta‐analysis to be overlaid on the plot. We suggest that these plots are more easily interpreted, and are a more informative graphical form than common approaches. Copyright © 2011 John Wiley & Sons, Ltd.