Jan Y Verbakel1, Ewout W Steyerberg2, Hajime Uno3, Bavo De Cock4, Laure Wynants4, Gary S Collins5, Ben Van Calster6. 1. KU Leuven, Department of Public Health and Primary Care, Leuven, Belgium; Nuffield Department of Primary Care Health Sciences, University of Oxford, Oxford, UK. 2. Department of Biomedical Data Sciences, Leiden University Medical Centre (LUMC), Leiden, the Netherlands. 3. Division of Population Sciences, Dana-Farber Cancer Institute, Boston, MA, USA. 4. KU Leuven, Department of Development and Regeneration, Leuven, Belgium. 5. Centre for Statistics in Medicine, Nuffield Department of Orthopaedics, Rheumatology and Musculoskeletal Sciences, University of Oxford, Oxford, UK; Oxford University Hospitals NHS Foundation Trust, Oxford, UK. 6. Department of Biomedical Data Sciences, Leiden University Medical Centre (LUMC), Leiden, the Netherlands; KU Leuven, Department of Development and Regeneration, Leuven, Belgium. Electronic address: ben.vancalster@kuleuven.be.
Abstract
OBJECTIVES: Receiver operating characteristic (ROC) curves show how well a risk prediction model discriminates between patients with and without a condition. We aim to investigate how ROC curves are presented in the literature and discuss and illustrate their potential limitations. STUDY DESIGN AND SETTING: We conducted a pragmatic literature review of contemporary publications that externally validated clinical prediction models. We illustrated limitations of ROC curves using a testicular cancer case study and simulated data. RESULTS: Of 86 identified prediction modeling studies, 52 (60%) presented ROC curves without thresholds and one (1%) presented an ROC curve with only a few thresholds. We illustrate that ROC curves in their standard form withhold threshold information have an unstable shape even for the same area under the curve (AUC) and are problematic for comparing model performance conditional on threshold. We compare ROC curves with classification plots, which show sensitivity and specificity conditional on risk thresholds. CONCLUSION: ROC curves do not offer more information than the AUC to indicate discriminative ability. To assess the model's performance for decision-making, results should be provided conditional on risk thresholds. Therefore, if discriminatory ability must be visualized, classification plots are attractive.
OBJECTIVES: Receiver operating characteristic (ROC) curves show how well a risk prediction model discriminates between patients with and without a condition. We aim to investigate how ROC curves are presented in the literature and discuss and illustrate their potential limitations. STUDY DESIGN AND SETTING: We conducted a pragmatic literature review of contemporary publications that externally validated clinical prediction models. We illustrated limitations of ROC curves using a testicular cancer case study and simulated data. RESULTS: Of 86 identified prediction modeling studies, 52 (60%) presented ROC curves without thresholds and one (1%) presented an ROC curve with only a few thresholds. We illustrate that ROC curves in their standard form withhold threshold information have an unstable shape even for the same area under the curve (AUC) and are problematic for comparing model performance conditional on threshold. We compare ROC curves with classification plots, which show sensitivity and specificity conditional on risk thresholds. CONCLUSION: ROC curves do not offer more information than the AUC to indicate discriminative ability. To assess the model's performance for decision-making, results should be provided conditional on risk thresholds. Therefore, if discriminatory ability must be visualized, classification plots are attractive.
Authors: Sujay Kulshrestha; Dmitriy Dligach; Cara Joyce; Richard Gonzalez; Ann P O'Rourke; Joshua M Glazer; Anne Stey; Jacqueline M Kruser; Matthew M Churpek; Majid Afshar Journal: JAMIA Open Date: 2021-03-01
Authors: S D Kuil; S Hidad; J C Fischer; J Harting; C M P M Hertogh; J M Prins; M D de Jong; F van Leth; C Schneeberger Journal: Clin Infect Dis Date: 2021-12-06 Impact factor: 9.079