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  4. Receiver Operating Characteristic Curve Analysis for Cure Survival Data
 
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Receiver Operating Characteristic Curve Analysis for Cure Survival Data

Date Issued
2014
Date
2014
Author(s)
Tien, Wan-Ting
URI
http://ntur.lib.ntu.edu.tw//handle/246246/264039
Abstract
Benefited from the advanced technology and medical science, more and more effective treatments for different kinds of incurable diseases have been invented. For instance, patients will not die of cancer if the radiation kills all cancer cells, so there are plenty of right-censored data at the end of the observation period. The Kaplan-Meier type estimator of survival curve shows a long and stable plateau in the tail. A characteristic of such survival data is that the survival function does not converge to zero as time goes to infinity. It is called "cure survival data". As a result, using biomarkers to discriminate uncured patients from all subjects becomes an important issue. It is related to the connection between classifications and the true status. Our primary research aim is to extend the application of true positive rate (TPR), false positive rate (FPR), and the area under receiver operating characteristic (ROC) curve (AUC) from classical survival data to cure survival data. And we will analyze the data of an angiography cohort study.
Subjects
存活分析
治癒存活資料
生物指標
真陽性率
偽陽性率
ROC曲線下面積
SDGs

[SDGs]SDG3

Type
thesis
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ntu-103-R01221025-1.pdf

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(MD5):e93d6fa18d093f8b17f4ca765041e880

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