Literature DB >> 36186236

Concordance Measures and Time-Dependent ROC Methods.

Norberto Pantoja-Galicia1, Olivia I Okereke2,3, Deborah Blacker2,3, Rebecca A Betensky4.   

Abstract

The receiver operating characteristic (ROC) curve displays sensitivity versus 1-specificity over a set of thresholds. The area under the ROC curve (AUC) is a global scalar summary of this curve. In the context of time-dependent ROC methods, we are interested in global scalar measures that summarize sequences of time-dependent AUCs over time. The concordance probability is a candidate for such purposes. The concordance probability can provide a global assessment of the discrimination ability of a test for an event that occurs at random times and may be right censored. If the test adequately differentiates between subjects who survive longer times and those who survive shorter times, this will assist clinical decisions. In this context the concordance probability may support assessment of precision medicine tools based on prognostic biomarkers models for overall survival. Definitions of time-dependent sensitivity and specificity are reviewed. Some connections between such definitions and concordance measures are also reviewed and we establish new connections via new measures of global concordance. We explore the relationship between such measures and their corresponding time-dependent AUC. To illustrate these concepts, an application in the context of Alzheimer's disease is presented.

Entities:  

Keywords:  Alzheimer’s disease; Time-dependent sensitivity and specificity; censored survival times; diagnostic test; inverse probability weighting

Year:  2021        PMID: 36186236      PMCID: PMC9523576          DOI: 10.1080/24709360.2021.1926189

Source DB:  PubMed          Journal:  Biostat Epidemiol        ISSN: 2470-9360


  16 in total

1.  Overall C as a measure of discrimination in survival analysis: model specific population value and confidence interval estimation.

Authors:  Michael J Pencina; Ralph B D'Agostino
Journal:  Stat Med       Date:  2004-07-15       Impact factor: 2.373

2.  Survival model predictive accuracy and ROC curves.

Authors:  Patrick J Heagerty; Yingye Zheng
Journal:  Biometrics       Date:  2005-03       Impact factor: 2.571

3.  Robust combination of multiple diagnostic tests for classifying censored event times.

Authors:  T Cai; S Cheng
Journal:  Biostatistics       Date:  2007-12-03       Impact factor: 5.899

4.  The SIST-M: predictive validity of a brief structured clinical dementia rating interview.

Authors:  Olivia I Okereke; Norberto Pantoja-Galicia; Maura Copeland; Bradley T Hyman; Taylor Wanggaard; Marilyn S Albert; Rebecca A Betensky; Deborah Blacker
Journal:  Alzheimer Dis Assoc Disord       Date:  2012 Jul-Sep       Impact factor: 2.703

Review 5.  A Tutorial on Evaluating the Time-Varying Discrimination Accuracy of Survival Models Used in Dynamic Decision Making.

Authors:  Aasthaa Bansal; Patrick J Heagerty
Journal:  Med Decis Making       Date:  2018-10-14       Impact factor: 2.583

6.  Evaluating the yield of medical tests.

Authors:  F E Harrell; R M Califf; D B Pryor; K L Lee; R A Rosati
Journal:  JAMA       Date:  1982-05-14       Impact factor: 56.272

7.  Incorporating the time dimension in receiver operating characteristic curves: a case study of prostate cancer.

Authors:  R Etzioni; M Pepe; G Longton; C Hu; G Goodman
Journal:  Med Decis Making       Date:  1999 Jul-Sep       Impact factor: 2.583

8.  The Structured Interview & Scoring Tool-Massachusetts Alzheimer's Disease Research Center (SIST-M): development, reliability, and cross-sectional validation of a brief structured clinical dementia rating interview.

Authors:  Olivia I Okereke; Maura Copeland; Bradley T Hyman; Taylor Wanggaard; Marilyn S Albert; Deborah Blacker
Journal:  Arch Neurol       Date:  2011-03

9.  Statistical models for longitudinal biomarkers of disease onset.

Authors:  E H Slate; B W Turnbull
Journal:  Stat Med       Date:  2000-02-29       Impact factor: 2.373

10.  Application of the time-dependent ROC curves for prognostic accuracy with multiple biomarkers.

Authors:  Yingye Zheng; Tianxi Cai; Ziding Feng
Journal:  Biometrics       Date:  2006-03       Impact factor: 2.571

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