Effective Ways to Build and Evaluate Individual Survival Distributions Effective Ways to Build and Evaluate Individual Survival Distributions
Paper summary The paper looks at approaches to predicting individual survival time distributions (isd). The motivation is shown in the figure below. Between two patients the survival time varies greatly so we should be able to predict a distribution like the red curve. https://i.imgur.com/2r9JvUp.png The paper studies the following methods: - class-based survival curves Kaplan-Meier [31] - Kalbfleisch-Prentice extension of the Cox (cox-kp) [29] - Accelerated Failure Time (aft) model [29] - Random Survival Forest model with Kaplan-Meier extensions (rsf-km) - elastic net Cox (coxen-kp) [55] - Multi-task Logistic Regression (mtlr) [57] Looking at the predictions of these methods side by side we can observe some systematic differences between the methods: https://i.imgur.com/vJoCL4a.png The paper presents a "D-Calibration" metric (distributional calibration) which represents of the method answers this question: Should the patient believe the predictions implied by the survival curve? https://i.imgur.com/MX8CbZ7.png
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Effective Ways to Build and Evaluate Individual Survival Distributions
Humza Haider and Bret Hoehn and Sarah Davis and Russell Greiner
arXiv e-Print archive - 2018 via Local arXiv
Keywords: cs.LG, stat.ML

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Summary by Joseph Paul Cohen 1 month ago
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