smoothROCtime: an R package for time-dependent ROC curve estimation

被引:5
|
作者
Diaz-Coto, Susana [1 ]
Martinez-Camblor, Pablo [2 ]
Perez-Fernandez, Sonia [1 ]
机构
[1] Univ Oviedo, Dept Stat, Oviedo, Asturias, Spain
[2] Dartmouth Coll, Geisel Sch Med, Hanover, NH 03755 USA
关键词
(Bio)markers; Time-dependent outcomes; Time-dependent ROC curve; Smooth time-dependent ROC curve estimation; Area under the curve; OPERATING CHARACTERISTIC CURVES; MARKERS;
D O I
10.1007/s00180-020-00955-7
中图分类号
O21 [概率论与数理统计]; C8 [统计学];
学科分类号
020208 ; 070103 ; 0714 ;
摘要
The receiver operating characteristic (ROC) curve has become one of the most used tools for analyzing the diagnostic capacity of continuous biomarkers. When the studied outcome is a time-dependent variable two main generalizations have been proposed, based on properly extensions of the sensitivity and the specificity. Different procedures have been suggested for their estimation mainly under the presence of right censorship. Most of them have been implemented, as well, in diverse types of software, including R packages. This work focuses on the R implementation for the smooth estimation of time-dependent ROC curves. The theoretical connection between them through the joint distribution function of the biomarker and time-to-event variables prompts an approximation method: considered estimators are based on the bivariate kernel density estimator for the joint density function of the bidimensional variable (Marker, Time-to-event). The use of the package is illustrated with two real-world examples.
引用
收藏
页码:1231 / 1251
页数:21
相关论文
共 50 条
  • [1] 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
  • [2] Time-dependent ROC curve estimation for interval-censored data
    Beyene, Kassu Mehari
    El Ghouch, Anouar
    [J]. BIOMETRICAL JOURNAL, 2022, 64 (06) : 1056 - 1074
  • [3] Estimation of time-dependent area under the ROC curve for long-term risk prediction
    Chambless, Lloyd E.
    Diao, Guoqing
    [J]. STATISTICS IN MEDICINE, 2006, 25 (20) : 3474 - 3486
  • [4] Nonparametric methodology for the time-dependent partial area under the ROC curve
    Hung, Hung
    Chiang, Chin-Tsang
    [J]. JOURNAL OF STATISTICAL PLANNING AND INFERENCE, 2011, 141 (12) : 3829 - 3838
  • [5] Adjusting ROC Curve for Covariates with AROC R Package
    Machado e Costa, Francisco
    Braga, Ana Cristina
    [J]. COMPUTATIONAL SCIENCE AND ITS APPLICATIONS - ICCSA 2020, PT III, 2020, 12251 : 185 - 198
  • [6] tdsa: An R package for time-dependent sensitivity analysis
    Ng, Wee Hao
    Myers, Christopher R.
    McArt, Scott H.
    Ellner, Stephen P.
    [J]. METHODS IN ECOLOGY AND EVOLUTION, 2023, 14 (11): : 2758 - 2765
  • [7] Time-dependent ROC curve analysis in medical research: current methods and applications
    Adina Najwa Kamarudin
    Trevor Cox
    Ruwanthi Kolamunnage-Dona
    [J]. BMC Medical Research Methodology, 17
  • [8] Time-dependent ROC curve analysis in medical research: current methods and applications
    Kamarudin, Adina Najwa
    Cox, Trevor
    Kolamunnage-Dona, Ruwanthi
    [J]. BMC MEDICAL RESEARCH METHODOLOGY, 2017, 17
  • [9] Review and comparison of ROC curve estimators for a time-dependent outcome with marker-dependent censoring
    Blanche, Paul
    Dartigues, Jean-Francois
    Jacqmin-Gadda, Helene
    [J]. BIOMETRICAL JOURNAL, 2013, 55 (05) : 687 - 704
  • [10] Nonparametric estimation of time-dependent ROC curves conditional on a continuous covariate
    Rodriguez-Alvarez, Maria Xose
    Meira-Machado, Luis
    Abu-Assi, Emad
    Raposeiras-Roubin, Sergio
    [J]. STATISTICS IN MEDICINE, 2016, 35 (07) : 1090 - 1102