ISDEvaluation
swMATH ID:  35485 
Software Authors:  Haider, Humza; Hoehn, Bret; Davis, Sarah; Greiner, Russell 
Description:  Effective ways to build and evaluate individual survival distributions. An accurate model of a patient’s individual survival distribution can help determine the appropriate treatment for terminal patients. Unfortunately, risk scores (for example from Cox Proportional Hazard models) do not provide survival probabilities, singletime probability models (for instance the Gail model, predicting 5 year probability) only provide for a single time point, and standard KaplanMeier survival curves provide only population averages for a large class of patients, meaning they are not specific to individual patients. This motivates an alternative class of tools that can learn a model that provides an individual survival distribution for each subject, which gives survival probabilities across all times, such as extensions to the Cox model, Accelerated Failure Time, an extension to Random Survival Forests, and MultiTask Logistic Regression. This paper first motivates such “individual survival distribution” (ISD) models, and explains how they differ from standard models. It then discusses ways to evaluate such models – namely Concordance, 1Calibration, Integrated Brier score, and versions of L1loss – then motivates and defines a novel approach, “DCalibration”, which determines whether a model’s probability estimates are meaningful. We also discuss how these measures differ, and use them to evaluate several ISD prediction tools over a range of survival data sets. We also provide a code base for all of these survival models and evaluation measures, at url{https://github.com/haiderstats/ISDEvaluation}. 
Homepage:  https://jmlr.csail.mit.edu/papers/v21/18772.html 
Source Code:  https://github.com/haiderstats/ISDEvaluation 
Keywords:  survival analysis; risk model; patientspecific survival prediction; calibration; discrimination 
Related Software:  DeepSurv; GitHub; randomForestSRC; fastcox; survival; CRAN 
Cited in:  0 Publications 
Standard Articles
1 Publication describing the Software, including 1 Publication in zbMATH  Year 

Effective ways to build and evaluate individual survival distributions Haider, Humza; Hoehn, Bret; Davis, Sarah; Greiner, Russell 
2020
