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- W2584829769 abstract "Abstract Existing goodness-of-fit tests for survival data are either exclusively graphical in nature or only test specific model assumptions, such as the proportional hazards assumption. We describe a flexible, parameter-free goodness-of-fit test that provides a simple numerical assessment of a model’s suitability regardless of the structure of the underlying model. Intuitively, the goodness-of-fit test utilizes the fact that for a good model early event occurrence is predicted to be just as likely as late event occurrence, whereas a bad model has a bias towards early or late events. Formally, the goodness-of-fit test is based on a novel generalized Martingale residual which we call the martingale survival residual. The martingale survival residual has a uniform probability density function defined on the interval −0.5 to +0.5 if censoring is either absent or accounted for as one outcome in a competing hazards framework. For a good model, the set of calculated residuals is statistically indistinguishable from the uniform distribution, which is tested using the Kolmogorov-Smirnov statistic." @default.
- W2584829769 created "2017-02-10" @default.
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- W2584829769 date "2017-01-31" @default.
- W2584829769 modified "2023-09-28" @default.
- W2584829769 title "A general goodness-of-fit test for survival analysis" @default.
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- W2584829769 doi "https://doi.org/10.1101/104406" @default.
- W2584829769 hasPublicationYear "2017" @default.
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