Literature DB >> 26481370

Quantifying predictive accuracy in survival models.

Seth T Lirette1,2, Inmaculada Aban3.   

Abstract

For time-to-event outcomes in medical research, survival models are the most appropriate to use. Unlike logistic regression models, quantifying the predictive accuracy of these models is not a trivial task. We present the classes of concordance (C) statistics and R 2 statistics often used to assess the predictive ability of these models. The discussion focuses on Harrell's C, Kent and O'Quigley's R 2, and Royston and Sauerbrei's R 2. We present similarities and differences between the statistics, discuss the software options from the most widely used statistical analysis packages, and give a practical example using the Worcester Heart Attack Study dataset.

Keywords:  R 2 statistics; Survival analysis; c statistic; discrimination; predictive accuracy

Mesh:

Year:  2015        PMID: 26481370     DOI: 10.1007/s12350-015-0296-z

Source DB:  PubMed          Journal:  J Nucl Cardiol        ISSN: 1071-3581            Impact factor:   5.952


  9 in total

1.  Using SAS to calculate the Kent and O'Quigley measure of dependence for Cox proportional hazards regression model.

Authors:  H Heinzl
Journal:  Comput Methods Programs Biomed       Date:  2000-08       Impact factor: 5.428

2.  A new measure of prognostic separation in survival data.

Authors:  Patrick Royston; Willi Sauerbrei
Journal:  Stat Med       Date:  2004-03-15       Impact factor: 2.373

3.  A simulation study of predictive ability measures in a survival model II: explained randomness and predictive accuracy.

Authors:  B Choodari-Oskooei; P Royston; Mahesh K B Parmar
Journal:  Stat Med       Date:  2012-07-05       Impact factor: 2.373

4.  Estimation of time-dependent area under the ROC curve for long-term risk prediction.

Authors:  Lloyd E Chambless; Guoqing Diao
Journal:  Stat Med       Date:  2006-10-30       Impact factor: 2.373

5.  A simulation study of predictive ability measures in a survival model I: explained variation measures.

Authors:  Babak Choodari-Oskooei; Patrick Royston; Mahesh K B Parmar
Journal:  Stat Med       Date:  2011-04-26       Impact factor: 2.373

Review 6.  Multivariable prognostic models: issues in developing models, evaluating assumptions and adequacy, and measuring and reducing errors.

Authors:  F E Harrell; K L Lee; D B Mark
Journal:  Stat Med       Date:  1996-02-28       Impact factor: 2.373

7.  On the C-statistics for evaluating overall adequacy of risk prediction procedures with censored survival data.

Authors:  Hajime Uno; Tianxi Cai; Michael J Pencina; Ralph B D'Agostino; L J Wei
Journal:  Stat Med       Date:  2011-01-13       Impact factor: 2.373

8.  Quantifying discrimination of Framingham risk functions with different survival C statistics.

Authors:  Michael J Pencina; Ralph B D'Agostino; Linye Song
Journal:  Stat Med       Date:  2012-02-17       Impact factor: 2.373

9.  Recent changes in attack and survival rates of acute myocardial infarction (1975 through 1981). The Worcester Heart Attack Study.

Authors:  R J Goldberg; J M Gore; J S Alpert; J E Dalen
Journal:  JAMA       Date:  1986 May 23-30       Impact factor: 56.272

  9 in total
  1 in total

1.  A framework for meta-analysis of prediction model studies with binary and time-to-event outcomes.

Authors:  Thomas Pa Debray; Johanna Aag Damen; Richard D Riley; Kym Snell; Johannes B Reitsma; Lotty Hooft; Gary S Collins; Karel Gm Moons
Journal:  Stat Methods Med Res       Date:  2018-07-23       Impact factor: 3.021

  1 in total

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