ArticleStatistics in biosciences2017
ROC Curve Analysis in the Presence of Imperfect Reference Standards.
Article in Statistics in biosciences, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 7 papers.
What it found
Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.
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
7 citing papers in PubMed.
- Evaluating the predictive performance of gut microbiota for the early-stage colorectal cancer.BMC gastroenterology · 2022Article
- Performance of 3 Sets of Criteria for Potentially Inappropriate Prescribing in Older People to Identify Inadequate Drug Treatment.JAMA network open · 2022Article
- Machine Learning Prediction of Length of Stay in Adult Spinal Deformity Patients Undergoing Posterior Spine Fusion Surgery.Journal of clinical medicine · 2021Article
- Chromatin accessibility of circulating CD8Nature communications · 2021Article
- Multiple Machine Learning Approaches Based on Postoperative Prediction of Pulmonary Complications in Patients With Emergency Cerebral Hemorrhage Surgery.Frontiers in surgery · 2021Article
- Differential diagnosis between benign and malignant pleural effusion with dual-energy spectral CT.PloS one · 2018Article
- Analysis of Risk Factors and the Establishment of a Predictive Model for Thrombosis in Patients with Immune Thrombocytopenia.Clinical and applied thrombosis/hemostasis : official journal of the International Academy of Clinical and Applied Thrombosis/HemostasisArticle
Corrections and comments
PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.
Authors and funding
3 authors.
Funding
Abstract
The receiver operating characteristic (ROC) curve is an important tool for the evaluation and comparison of predictive models when the outcome is binary. If the class membership of the outcomes are known, ROC can be constructed for a model, and the ROC with greater area under the curve (AUC) indicates better performance. However in practice, imperfect reference standards often exist, in which class membership of every data point are not fully determined. This situation is especially prevalent in high-throughput biomedical data because obtaining perfect reference standards for all data points is either too costly or technically impractical. To construct ROC curves for these data, the common practice is to either ignore the uncertainties in references, or remove data points with high uncertainties. Such approaches may cause bias to the ROC curves and generate misleading results in method evaluation. Here we present a framework to incorporate membership uncertainties into the construction of ROC curve, termed the expected ROC or "eROC" curve. We develop an efficient procedure for the estimation of eROC curve. The advantages of using eROC are demonstrated using simulated and real data.
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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.