ArticleFrontiers in public health2015
Threshold-free measures for assessing the performance of medical screening tests.
Article in Frontiers in public health, 2015. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 14 papers.
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14 citing papers in PubMed.
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- Subgroup identification of targeted therapy effects on biomarker for time to event data.Cancer biomarkers : section A of Disease markers · 2023Article
- Improving Mortality Risk Prediction with Routine Clinical Data: A Practical Machine Learning Model Based on eICU Patients.International journal of general medicine · 2023Article
- Combination of Feature Selection and Resampling Methods to Predict Preterm Birth Based on Electrohysterographic Signals from Imbalance Data.Sensors (Basel, Switzerland) · 2022Article
- Estimation and inference of predictive discrimination for survival outcome risk prediction models.Lifetime data analysis · 2022Article
- A relationship between the incremental values of area under the ROC curve and of area under the precision-recall curve.Diagnostic and prognostic research · 2021Article
- Predicting acute ovarian failure in female survivors of childhood cancer: a cohort study in the Childhood Cancer Survivor Study (CCSS) and the St Jude Lifetime Cohort (SJLIFE).The Lancet. Oncology · 2020Article
- Individual-patient prediction of meningioma malignancy and survival using the Surveillance, Epidemiology, and End Results database.NPJ digital medicine · 2020Article
- Review
- Improving palliative care with deep learning.BMC medical informatics and decision making · 2018Article
- A threshold-free summary index of prediction accuracy for censored time to event data.Statistics in medicine · 2018Article
- Embedded deep learning in ophthalmology: making ophthalmic imaging smarter.Therapeutic advances in ophthalmologyReview
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3 authors.
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Abstract
backgroundThe area under the receiver operating characteristic curve (AUC) is frequently used as a performance measure for medical tests. It is a threshold-free measure that is independent of the disease prevalence rate. We evaluate the utility of the AUC against an alternate measure called the average positive predictive value (AP), in the setting of many medical screening programs where the disease has a low prevalence rate.
methodsWe define the two measures using a common notation system and show that both measures can be expressed as a weighted average of the density function of the diseased subjects. The weights for the AP include prevalence in some form, but those for the AUC do not. These measures are compared using two screening test examples under rare and common disease prevalence rates.
resultsThe AP measures the predictive power of a test, which varies when the prevalence rate changes, unlike the AUC, which is prevalence independent. The relationship between the AP and the prevalence rate depends on the underlying screening/diagnostic test. Therefore, the AP provides relevant information to clinical researchers and regulators about how a test is likely to perform in a screening population.
conclusionThe AP is an attractive alternative to the AUC for the evaluation and comparison of medical screening tests. It could improve the effectiveness of screening programs during the planning stage.
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