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Review of Statistical Methods for Evaluating the Performance of Survival or Other Time-to-Event Prediction Models (from Conventional to Deep Learning Approaches)

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Abstract
The recent introduction of various high-dimensional modeling methods, such as radiomics and deep learning, has created a much greater diversity in modeling approaches for survival prediction (or, more generally, time-to-event prediction). The newness of the recent modeling approaches and unfamiliarity with the model outputs may confuse some researchers and practitioners about the evaluation of the performance of such models. Methodological literacy to critically appraise the performance evaluation of the models and, ideally, the ability to conduct such an evaluation would be needed for those who want to develop models or apply them in practice. This article intends to provide intuitive, conceptual, and practical explanations of the statistical methods for evaluating the performance of survival prediction models with minimal usage of mathematical descriptions. It covers from conventional to deep learning methods, and emphasis has been placed on recent modeling approaches. This review article includes straightforward explanations of C indices (Harrell's C index, etc.), time dependent receiver operating characteristic curve analysis, calibration plot, other methods for evaluating the calibration performance, and Brier score.
Author(s)
박서영박지은김형진박성호
Issued Date
2021
Type
Article
Keyword
AccuracyArtificial intelligenceCalibrationDeep learningDiscriminationMachine learningPerformancePrediction modelPredictive modelSurvivalTime-to-event.
DOI
10.3348/kjr.2021.0223
URI
https://oak.ulsan.ac.kr/handle/2021.oak/8486
https://ulsan-primo.hosted.exlibrisgroup.com/primo-explore/fulldisplay?docid=TN_cdi_nrf_kci_oai_kci_go_kr_ARTI_9862071&context=PC&vid=ULSAN&lang=ko_KR&search_scope=default_scope&adaptor=primo_central_multiple_fe&tab=default_tab&query=any,contains,Review%20of%20Statistical%20Methods%20for%20Evaluating%20the%20Performance%20of%20Survival%20or%20Other%20Time-to-Event%20Prediction%20Models%20(from%20Conventional%20to%20Deep%20Learning%20Approaches)&offset=0&pcAvailability=true
Publisher
KOREAN JOURNAL OF RADIOLOGY
Location
대한민국
Language
한국어
ISSN
1229-6929
Citation Volume
22
Citation Number
1
Citation Start Page
1697
Citation End Page
1707
Appears in Collections:
Medicine > Medicine
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