Testing Calibration of Cox Survival Models at Extremes of Event Risk
نویسندگان
چکیده
منابع مشابه
Testing calibration of risk models at extremes of disease risk.
Risk-prediction models need careful calibration to ensure they produce unbiased estimates of risk for subjects in the underlying population given their risk-factor profiles. As subjects with extreme high or low risk may be the most affected by knowledge of their risk estimates, checking the adequacy of risk models at the extremes of risk is very important for clinical applications. We propose a...
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Comparison of Artificial Neural Networks and Cox Regression Models in Prediction of Kidney Transplant Survival
Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...
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Cox regression model serves as a statistical method for analyzing the survival data, which requires some options such as hazard proportionality. In recent decades, artificial neural network model has been increasingly applied to predict survival data. This research was conducted to compare Cox regression and artificial neural network models in prediction of kidney transplant survival. The prese...
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Probabilistic properties of Cox processes of relevance for statistical modeling and inference are studied. Particularly, we study the most important classes of Cox processes, including log Gaussian Cox processes, shot noise Cox processes, and permanent Cox processes. We consider moment properties and point process operations such as thinning, displacements, and superpositioning. We also discuss...
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ژورنال
عنوان ژورنال: Frontiers in Genetics
سال: 2018
ISSN: 1664-8021
DOI: 10.3389/fgene.2018.00177