Sensitivity, Specificity, and Predictive Values: Foundations, Pliabilities, and Pitfalls in Research and Practice

By Robert Trevethan. Frontiers in Public Health, 2017

1,388 citations recorded by the source corpus.

Within the context of screening tests, it is important to avoid misconceptions about sensitivity, specificity, and predictive values. In this article, therefore, foundations are first established concerning these metrics along with the first of several aspects of pliability that should be recognized in relation to those metrics. Clarification is then provided about the definitions of sensitivity, specificity, and predictive values and why researchers and clinicians can misunderstand and misrepresent them. Arguments are made that sensitivity and specificity should usually be applied only in the context of describing a screening test’s attributes relative to a reference standard; that predictive values are more appropriate and informative in actual screening contexts, but that sensitivity and specificity can be used for screening decisions about individual people if they are extremely high; that predictive values need not always be high and might be used to advantage by adjusting the sensitivity and specificity of screening tests; that, in screening contexts, researchers should provide information about all four metrics and how they were derived; and that, where necessary, consumers of health research should have the skills to interpret those metrics effectively for maximum benefit to clients and the healthcare system.

Subfields: Economics and Econometrics, General Health Professions, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/f279a67a822702be7db2cb551b15b85667e0525d

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In Economics and Econometrics

1. Giulia Fiscon
2. Paola Paci
3. V. Licursi
4. Federica Conte

In General Health Professions

1. Giulia Fiscon
2. Paola Paci
3. V. Licursi
4. Federica Conte

In Statistics and Probability

1. Giulia Fiscon
2. Paola Paci
3. V. Licursi
4. Federica Conte

Citations in the corpus

• MIENTURNET: an interactive web tool for microRNA-target enrichment and network-based analysis (2019, methodology): “…in capturing the most tissue-representative miRNAs, we computed the positive predictive value (PPV) for different tissues, which is defined as [27]: number of true positives ( number of false positives + number of true positives ) where a “true positive” is an outcome where the model…”