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Lecture

Diagnostic Tests: Sensitivity and Specificity

In course

EE-515: Fundamentals of biosensors and electronic biochips

The labels "biosensor" and "eBiochip" have been employed to refer to the most diverse systems and in several fields of application. The course is meant not only to provide means to dig into this sea

Description

This lecture covers the concepts of Sensitivity and Specificity in diagnostic tests with binary classification outputs, such as pregnancy tests and biomarker tests. Sensitivity is the probability of providing a correct answer for positive samples, while Specificity is for negative samples. The lecture explains how these parameters depend on the biomarker measured and the threshold concentration chosen. It also introduces the Receiver Operating Characteristic (ROC) curve, which is used to compare diagnostic tests based on sensitivity and specificity. The importance of Precision in determining the probability of having a disease given a positive test result is also discussed, considering the prevalence of the disease in the population.

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Instructor

Carlotta Guiducci

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Related concepts (11)

False positives and false negatives

A false positive is an error in binary classification in which a test result incorrectly indicates the presence of a condition (such as a disease when the disease is not present), while a false negative is the opposite error, where the test result incorrectly indicates the absence of a condition when it is actually present. These are the two kinds of errors in a binary test, in contrast to the two kinds of correct result (a and a ).

Sensitivity and specificity

Sensitivity and specificity mathematically describe the accuracy of a test that reports the presence or absence of a condition. If individuals who have the condition are considered "positive" and those who do not are considered "negative", then sensitivity is a measure of how well a test can identify true positives and specificity is a measure of how well a test can identify true negatives: Sensitivity (true positive rate) is the probability of a positive test result, conditioned on the individual truly being positive.

Positive and negative predictive values

The positive and negative predictive values (PPV and NPV respectively) are the proportions of positive and negative results in statistics and diagnostic tests that are true positive and true negative results, respectively. The PPV and NPV describe the performance of a diagnostic test or other statistical measure. A high result can be interpreted as indicating the accuracy of such a statistic. The PPV and NPV are not intrinsic to the test (as true positive rate and true negative rate are); they depend also on the prevalence.

Receiver operating characteristic

A receiver operating characteristic curve, or ROC curve, is a graphical plot that illustrates the diagnostic ability of a binary classifier system as its discrimination threshold is varied. The ROC curve is the plot of the true positive rate (TPR) against the false positive rate (FPR), at various threshold settings. The ROC can also be thought of as a plot of the power as a function of the Type I Error of the decision rule (when the performance is calculated from just a sample of the population, it can be thought of as estimators of these quantities).

Precision and recall

In pattern recognition, information retrieval, object detection and classification (machine learning), precision and recall are performance metrics that apply to data retrieved from a collection, corpus or sample space. Precision (also called positive predictive value) is the fraction of relevant instances among the retrieved instances. Written as a formula:. Recall (also known as sensitivity) is the fraction of relevant instances that were retrieved. Written as a formula: . Both precision and recall are therefore based on relevance.

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