ArticleStatistics in medicine2017
First things first: risk model performance metrics should reflect the clinical application.
Article in Statistics in medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
What it found
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The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.
The trial behind it
Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.
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Who cites it
6 citing papers in PubMed.
- Advanced Bayesian BMD-Derived Genome-Wide Polygenic Scores Enhance Clinical FRAX-Based Fracture Risk Prediction in Postmenopausal Women.Calcified tissue international · 2026Article
- Review
- Validation and update of a multivariable prediction model for the identification and management of patients at risk for hepatocellular carcinoma.Clinical proteomics · 2021Article
- Consistency of variety of machine learning and statistical models in predicting clinical risks of individual patients: longitudinal cohort study using cardiovascular disease as exemplar.BMJ (Clinical research ed.) · 2020Article
- Three myths about risk thresholds for prediction models.BMC medicine · 2019Article
- Cardiovascular disease: The rise of the genetic risk score.PLoS medicine · 2018Article
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2 authors.
Funding
Abstract
Developing new measures of risk model performance is an active line of research, often motivated by the conventional wisdom that area under the ROC curve is an 'insensitive' measure of the additional predictive capacity offered by new biomarkers. Without endorsing area under the ROC curve, we argue that this charge is not substantiated. Three articles in this issue discuss alternative metrics of risk model performance: NRI(p) (two-category net reclassification index at the event rate), integrated discrimination index, and R-squared statistics. Guided by the principle that performance metrics should match the intended use of a risk prediction model, we argue that routine use of these indices is not justified. Instead, we recommend decision-theoretic measures to evaluate risk prediction models for applications in which clinically relevant risk thresholds have been established for classifying individuals. In the absence of established risk thresholds, additional research is needed to develop suitable metrics. Copyright © 2017 John Wiley & Sons, Ltd.
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Registered trials
Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.