ReviewSeminars in oncology2010
Traditional statistical methods for evaluating prediction models are uninformative as to clinical value: towards a decision analytic framework.
Review in Seminars in oncology, 2010. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 51 papers, 4 of them syntheses that pooled it.
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.
Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.
Who cites it
51 citing papers in PubMed, 4 syntheses or guidelines pooled it.
- Prognostic value of left atrial function in cardiac amyloidosis: a systematic review and meta‑analysis.The international journal of cardiovascular imaging · 2026Pooled it
- Methodological guidance for the evaluation and updating of clinical prediction models: a systematic review.BMC medical research methodology · 2022Pooled it
- Prognostic factors and prediction models for acute aortic dissection: a systematic review.BMJ open · 2021Pooled it
- Multigene panels in prostate cancer risk assessment: a systematic review.Genetics in medicine : official journal of the American College of Medical Genetics · 2016Pooled it
- A Novel Risk-Prediction Scoring System for Sepsis among Patients with Acute Pancreatitis: A Retrospective Analysis of a Large Clinical Database.International journal of clinical practice · 2022Trial
- Comparison of the Cox Proportional Hazards Model and Random Survival Forest Algorithm for Predicting Patient-Specific Survival Probabilities in Clinical Trial Data.Biometrical journal. Biometrische Zeitschrift · 2026Article
- A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models - part 2: time-to-event outcomes.Diagnostic and prognostic research · 2025Article
- C-reactive protein and cardiovascular risk in the general population.European heart journal · 2025Article
- Advances in artificial intelligence to model the impact of El Niño-Southern Oscillation on crop yield variability.MethodsX · 2025Review
- A decomposition of Fisher's information to inform sample size for developing or updating fair and precise clinical prediction models for individual risk-part 1: binary outcomes.Diagnostic and prognostic research · 2025Article
- Development and validation of a nomogram for individualized prediction of spontaneous extremely preterm birth at < 28 weeks in twin pregnancy: a retrospective cohort study.Archives of gynecology and obstetrics · 2024Article
- Development and validation of models for predicting preterm birth and gestational latency following emergency cervical cerclage: A multicenter cohort study.Acta obstetricia et gynecologica Scandinavica · 2024Article
- Evaluating the clinical utility of an easily applicable prediction model of suicide attempts, newly developed and validated with a general community sample of adults.BMC psychiatry · 2024Article
- Diabetes Study of Children of Diverse Ethnicity and Race: Study design.Diabetes/metabolism research and reviews · 2024Observational
- Integrating Machine Learning into Statistical Methods in Disease Risk Prediction Modeling: A Systematic Review.Health data science · 2024Review
- Pitfalls of the concordance index for survival outcomes.Statistics in medicine · 2023Article
- Integrating economic considerations into cutpoint selection may help align clinical decision support toward value-based healthcare.Journal of the American Medical Informatics Association : JAMIA · 2023Article
- Decision Curve Analysis for Personalized Treatment Choice between Multiple Options.Medical decision making : an international journal of the Society for Medical Decision Making · 2023Article
- Fibroblast growth factor 21 is an independent predictor of prevalent and incident obstructive sleep apnea.iScience · 2023Article
- A Framework for Using Real-World Data and Health Outcomes Modeling to Evaluate Machine Learning-Based Risk Prediction Models.Value in health : the journal of the International Society for Pharmacoeconomics and Outcomes Research · 2022Article
Corrections and comments
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Authors and funding
2 authors.
Funding
Abstract
Cancer prediction models are becoming ubiquitous, yet we generally have no idea whether they do more good than harm. This is because current statistical methods for evaluating prediction models are uninformative as to their clinical value. Prediction models are typically evaluated in terms of discrimination or calibration. However, it is generally unclear how high discrimination needs to be before it is considered "high enough"; similarly, there are no rational guidelines as to the degree of miscalibration that would discount clinical use of a model. Classification tables do present the results of models in more clinically relevant terms, but it is not always clear which of two models is preferable on the basis of a particular classification table, or even whether either model should be used at all. Recent years have seen the development of straightforward decision analytic techniques that evaluate prediction models in terms of their consequences. This depends on the simple approach of weighting true and false positives differently, to reflect that, for example, delaying the diagnosis of a cancer is more harmful than an unnecessary biopsy. Such decision analytic techniques hold the promise of determining whether clinical implementation of prediction models would do more good than harm.
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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.