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The identification of individuals who 'died far too early' or 'lived far too long' as compared to their survival probabilities from a Cox regression can lead to the detection of new prognostic factors. Methods to identify outliers are generally based on residuals. For Cox regression only deviance residuals have been considered for this purpose. Nardi and Schemper 1999 showed that these residuals are not very suitable. Instead, they developed and proposed two new types of residuals: the suggested log-odds and normal deviate residuals are simple, intuitively appealing and their theoretical properties and empirical performance make them very suitable for outlier identification. In a later paper Nardi and Schemper 2003 use these residuals to discriminate between alternative parametric models of survival and to judge the goodness of fit of these models to given data sets. The SAS-Macro SURES calculates the residuals only for Cox regression. A similar macro for parametric models of survival is available (see SAS-Macro COMPASS-COMparing PArametric Survival modelS using normal-deviate residuals).


Nardi, A., Schemper, M. (2003): "Comparing Cox and Parametric Models in Clinical Studies", Statistics in Medicine 22, 3597 - 3610
Nardi, A., Schemper, M. (1999): "New Residuals for Cox Regression and Their Application to Outlier Screening", Biometrics 55, 523 - 529
Andrews, D. F., Herzberg, A. M. (1985): Data, New York, Springer


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SURES: SAS macro