Net benefit approaches to the evaluation of prediction models, molecular markers, and diagnostic tests

By Andrew J. Vickers, B. Van calster, Ewout W. Steyerberg. British medical journal, 2016

1,011 citations recorded by the source corpus.

Many decisions in medicine involve trade-offs, such as between diagnosing patients with disease versus unnecessary additional testing for those who are healthy. Net benefit is an increasingly reported decision analytic measure that puts benefits and harms on the same scale. This is achieved by specifying an exchange rate, a clinical judgment of the relative value of benefits (such as detecting a cancer) and harms (such as unnecessary biopsy) associated with models, markers, and tests. The exchange rate can be derived by asking simple questions, such as the maximum number of patients a doctor would recommend for biopsy to find one cancer. As the answers to these sorts of questions are subjective, it is possible to plot net benefit for a range of reasonable exchange rates in a “decision curve.” For clinical prediction models, the exchange rate is related to the probability threshold to determine whether a patient is classified as being positive or negative for a disease. Net benefit is useful for determining whether basing clinical decisions on a model, marker, or test would do more good than harm. This is in contrast to traditional measures such as sensitivity, specificity, or area under the curve, which are statistical abstractions not directly informative about clinical value. Recent years have seen an increase in practical applications of net benefit analysis to research data. This is a welcome development, since decision analytic techniques are of particular value when the purpose of a model, marker, or test is to help doctors make better clinical decisions.

Subfields: Economics and Econometrics, Statistics and Probability

View Paper: https://www.semanticscholar.org/paper/45d1e781dcdb5464fe0859888df799309c56fd46

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Citations in the corpus

Developing clinical prediction models: a step-by-step guide (2024, neutral): “104 105 The assumption is that the benefit of treating one patient who would experience a cardiovascular event over 10 years outweighs the harms and costs incurred by treating another nine people who will not benefit.”
Reporting and Interpreting Decision Curve Analysis: A Guide for Investigators (2018, background): “Elsewhere, we have given further details of how researchers can develop ideas about the suitable range of threshold probabilities [9].”
Metrics Reloaded: Recommendations for image analysis validation (2022, background): “A prominent example is the Net Benefit (NB) 24 metric, popular in clinical prediction tasks and designed to determine whether basing decisions on a method would do more good than harm.”