Cumulative/dynamic ROC curve estimation

被引:19
|
作者
Martinez-Camblor, Pablo [1 ,2 ]
Bayon, Gustavo F. [3 ]
Perez-Fernandez, Sonia [4 ]
机构
[1] HUCA, Asturias, Spain
[2] Univ Autonoma Chile, Santiago, Chile
[3] IUOPA, Asturias, Spain
[4] Univ Oviedo, Dept Estadist, Asturias, Spain
关键词
Cumulative; dynamic ROC curve; censored data; Cox regression model; sensitivity; specificity; OPERATING CHARACTERISTIC CURVES; PERMUTATION TEST; SURVIVAL; ACCURACY; POINT;
D O I
10.1080/00949655.2016.1175442
中图分类号
TP39 [计算机的应用];
学科分类号
081203 ; 0835 ;
摘要
Receiver operating-characteristic (ROC) curve is a popular graphical method frequently used in order to study the diagnostic capacity of continuous (bio)markers. When the considered outcome is a time-dependent variable, the direct generalization is known ascumulative/dynamic ROC curve. For a fixed point of time, t, one subject is allocated into the positive group if the event happens before t and into the negative group if the event is not happened at t. The presence of censored subject, which can not be directly assigned into a group, is the main handicap of this approach. The proposed cumulative/dynamic ROC curve estimator assigns a probability to belong to the negative (positive) group to the subjects censored previously to t. The performance of the resulting estimator is studied from Monte Carlo simulations. Some real-world applications are reported. Results suggest that the new estimators provide a good approximation to the real cumulative/dynamic ROC curve.
引用
收藏
页码:3582 / 3594
页数:13
相关论文
共 50 条
  • [31] Process Monitoring ROC Curve for Evaluating Dynamic Screening Methods
    Qiu, Peihua
    Xia, Zhiming
    You, Lu
    [J]. TECHNOMETRICS, 2020, 62 (02) : 236 - 248
  • [32] smoothROCtime: an R package for time-dependent ROC curve estimation
    Diaz-Coto, Susana
    Martinez-Camblor, Pablo
    Perez-Fernandez, Sonia
    [J]. COMPUTATIONAL STATISTICS, 2020, 35 (03) : 1231 - 1251
  • [33] smoothROCtime: an R package for time-dependent ROC curve estimation
    Susana Díaz-Coto
    Pablo Martínez-Camblor
    Sonia Pérez-Fernández
    [J]. Computational Statistics, 2020, 35 : 1231 - 1251
  • [34] ESTIMATION OF AREA UNDER THE ROC CURVE UNDER NONIGNORABLE VERIFICATION BIAS
    Yu, Wenbao
    Kim, Jae Kwang
    Park, Taesung
    [J]. STATISTICA SINICA, 2018, 28 (04) : 2149 - 2166
  • [35] Rank-based kernel estimation of the area under the ROC curve
    Yin, Jingjing
    Hao, Yi
    Samawi, Hani
    Rochani, Haresh
    [J]. STATISTICAL METHODOLOGY, 2016, 32 : 91 - 106
  • [36] Nonparametric estimation of the ROC curve based on smoothed empirical distribution functions
    Alicja Jokiel-Rokita
    Michał Pulit
    [J]. Statistics and Computing, 2013, 23 : 703 - 712
  • [37] ROC curve estimation under test-result-dependent sampling
    Wang, Xiaofei
    Ma, Junling
    George, Stephen L.
    [J]. BIOSTATISTICS, 2013, 14 (01) : 160 - 172
  • [38] EXTENSION OF ROC CURVE
    Takenouchi, Takashi
    Eguchi, Shinto
    [J]. 2009 IEEE INTERNATIONAL WORKSHOP ON MACHINE LEARNING FOR SIGNAL PROCESSING, 2009, : 434 - +
  • [39] Nonparametric estimation of the ROC curve based on smoothed empirical distribution functions
    Jokiel-Rokita, Alicja
    Pulit, Micha
    [J]. STATISTICS AND COMPUTING, 2013, 23 (06) : 703 - 712
  • [40] Bayesian ROC curve estimation under binormality using a rank likelihood
    Gu, Jiezhun
    Ghosal, Subhashis
    [J]. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2009, 139 (06) : 2076 - 2083