- Here, we'll disscuss three types of diagonostics for the Cox model: Testing the proportional hazards assumption. Examining influential observations (or outliers). Detecting nonlinearity in relationship between the log hazard and the covariates. In order to check these model assumptions,
**Residuals**method are used. - Assuming that the STATUS variable is named status, that a value of 1 indicates an observed event time and that the default name of the
**cumulative**hazard function or Cox-Snell**residuals**(HAZ_1) is used, the following commands will compute the**martingale**and deviance**residuals**for the Cox regression model. compute**martingale**= (status=1)-HAZ_1. - The method was derived from Lin, Wei and Ying (1993), who applied the method in checking the Cox model with
**cumulative**sums of**martingale**-based**residuals**. It is shown that this**martingale**-based bootstrap gives a correct first-order asymptotic approximation to the distribution function of the corresponding functional of the Kaplan-Meier ... - In SAS help, "The methods are derived from
**cumulative**sums of**martingale residuals**over follow-up times or covariate values. " is described. But I think that the method is following below the procedure. 1) At interesting covariate, generate random number for survival data with hazard ratio of that is obtained by the raw data. - SUMMARY This paper presents a new class of graphical and numerical methods for checking the adequacy of the Cox regression model. The procedures are derived from
**cumulative**sums of**martingale**-based**residuals**over follow-up time and/or covariate values. The distributions of these stochastic processes under the assumed model can be approximated by zero-mean Gaussian