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Directed by these discussions, an applicable signature extracting and diagnosing approach is designed.
Focused on this problem, this paper summarizes a thorough investigation into it from theoretical analysis to an applicable diagnosing approach design, as well as experimental verification.
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By applying an intention to diagnose approach, we avoided the overestimation of sensitivity and specificity by including non-evaluable results in our calculation.
Objective To determine whether a 3×2 table, using an intention to diagnose approach, is better than the "classic" 2×2 table at handling transparent reporting and non-evaluable results, when assessing the accuracy of a diagnostic test.
Simulation studies showed that the intent-to-diagnose approach under-estimate sensitivity and specificity.
The intent-to-diagnose approach provides lower estimates for sensitivity, specificity and AUC as it is the most conservative approach.
The intent-to-diagnose approach largely under-estimates both sensitivity and specificity (12.6% and 11.9% bias, respectively).
For each simulated meta-analysis data set, the extended TGLMM, Model 1-3 and the intent-to-diagnose approach are fitted.
The performance of the intent-to-diagnose approach, the alternative approaches and the extended TGLMM approach is examined by extensive simulation studies.
The paper conducted a meta-analysis of coronary CT angiography studies and presented an intent-to-diagnose approach together with three commonly applied alternative approaches.
Conclusion Parameters for diagnostic performance significantly decrease if non-evaluable results are included by a 3×2 table for analysis (intention to diagnose approach).
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Justyna Jupowicz-Kozak
CEO of Professional Science Editing for Scientists @ prosciediting.com