ArticleMedical decision making : an international journal of the Society for Medical Decision Making2018
Simple Decision-Analytic Functions of the AUC for Ruling Out a Risk Prediction Model and an Added Predictor.
Article in Medical decision making : an international journal of the Society for Medical Decision Making, 2018. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 4 papers.
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4 citing papers in PubMed.
- Evaluating Risk Prediction with Data Collection Costs: Novel Estimation of Test Tradeoff Curves.Medical decision making : an international journal of the Society for Medical Decision Making · 2024Article
- Metrics for Evaluating Polygenic Risk Scores.JNCI cancer spectrum · 2021Article
- Simple Methods for Evaluating 4 Types of Biomarkers: Surrogate Endpoint, Prognostic, Predictive, and Cancer Screening.Biomarker insights · 2020Review
- From high school to postsecondary education, training, and employment: Predicting outcomes for young adults with autism spectrum disorder.Autism & developmental language impairmentsArticle
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Abstract
backgroundWhen using risk prediction models, an important consideration is weighing performance against the cost (monetary and harms) of ascertaining predictors.
methodsThe minimum test tradeoff (MTT) for ruling out a model is the minimum number of all-predictor ascertainments per correct prediction to yield a positive overall expected utility. The MTT for ruling out an added predictor is the minimum number of added-predictor ascertainments per correct prediction to yield a positive overall expected utility.
resultsAn approximation to the MTT for ruling out a model is 1/[P (H(AUC LIMITATION: The latter approximation requires the Tangent Condition that the true positive rate at the point on the ROC curve with a slope of 1 is larger for Model 2 than Model 1.
conclusionThese approximations are suitable for back-of-the-envelope calculations. For example, in a study predicting the risk of invasive breast cancer, Model 2 adds to the predictors in Model 1 a set of 7 single nucleotide polymorphisms (SNPs). Based on the AUCs and the Tangent Condition, an MTT of 7200 was computed, which indicates that 7200 sets of SNPs are needed for every correct prediction of breast cancer to yield a positive overall expected utility. If ascertaining the SNPs costs $500, this MTT suggests that SNP ascertainment is not likely worthwhile for this risk prediction.
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