Quantifying predictive accuracy in survival models.
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 R2statistics often used to assess the predictive ability of these models. The discussion focuses on Harrell's C, Kent and O'Quigley's R2, and Royston and Sauerbrei's R2. 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.
Center of Biostatistics and Bioinformatics, University of Mississippi Medical Center, 2500 North State Street, Jackson, MS, 39216, USA. slirette2@umc.edu.|Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA. slirette2@umc.edu.
Seth T
ST
Lirette
Department of Biostatistics, University of Alabama at Birmingham, Birmingham, AL, USA.
Inmaculada
I
Aban
D006801:Humans; D015233:Models, Statistical; D016016:Proportional Hazards Models; D012984:Software; D016019:Survival Analysis
R 2 statistics; Survival analysis; c statistic; discrimination; predictive accuracy