ArticleStatistics in medicine2019
Quantifying risk stratification provided by diagnostic tests and risk predictions: Comparison to AUC and decision curve analysis.
Article in Statistics in medicine, 2019. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 12 papers.
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Who cites it
12 citing papers in PubMed, 13 citations in OpenAlex.
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- Artificial intelligence-based image analysis in clinical testing: lessons from cervical cancer screening.Journal of the National Cancer Institute · 2024Article
- Imaging-Based Versus Pathologic Survival Stratifications of Diffuse Glioma According to the 2021 WHO Classification System.Korean journal of radiology · 2023Article
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- The power of public-private partnership in medical technology innovation: Lessons from the development of FDA-cleared medical devices for assessment of concussion.Journal of clinical and translational science · 2022Review
- A simple framework to identify optimal cost-effective risk thresholds for a single screen: Comparison to Decision Curve Analysis.Journal of the Royal Statistical Society. Series A, (Statistics in Society) · 2021Article
- Clinical and Genetic Risk Prediction of Cognitive Impairment After Blood or Marrow Transplantation for Hematologic Malignancy.Journal of clinical oncology : official journal of the American Society of Clinical Oncology · 2020Article
Corrections and comments
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Authors and funding
1 author at 1 institution in 1 country.
Funding
Abstract
A property of diagnostic tests and risk models deserving more attention is risk stratification, defined as the ability of a test or model to separate those at high absolute risk of disease from those at low absolute risk. Risk stratification fills a gap between measures of classification (ie, area under the curve (AUC)) that do not require absolute risks and decision analysis that requires not only absolute risks but also subjective specification of costs and utilities. We introduce mean risk stratification (MRS) as the average change in risk of disease (posttest-pretest) revealed by a diagnostic test or risk model dichotomized at a risk threshold. Mean risk stratification is particularly valuable for rare conditions, where AUC can be high but MRS can be low, identifying situations that temper overenthusiasm for screening with the new test/model. We apply MRS to the controversy over who should get testing for mutations in BRCA1/2 that cause high risks of breast and ovarian cancers. To reveal different properties of risk thresholds to refer women for BRCA1/2 testing, we propose an eclectic approach considering MRS and other metrics. The value of MRS is to interpret AUC in the context of BRCA1/2 mutation prevalence, providing a range of risk thresholds at which a risk model is "optimally informative," and to provide insight into why net benefit arrives to its conclusion.
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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.