Comparing the importance of prognostic factors in Cox and logistic regression using SAS
Two SAS macro programs are presented that evaluate the relative importance of prognostic factors in the proportional hazards regression model and in the logistic regression model. The importance of a prognostic factor is quantified by the proportion of variation in the outcome attributable to this factor. For proportional hazards regression, the program %relimpcr uses the recently proposed measure V to calculate the proportion of explained variation (PEV). For the logistic model, the R2 measure based on squared raw residuals is used by the program %relimplr. Both programs are able to compute marginal and partial PEV, to compare PEVs of factors, of groups of factors, and even to compare PEVs of different models. The programs use a bootstrap resampling scheme to test differences of the PEVs of different factors. Confidence limits for P-values are provided. The programs further allow to base the computation of PEV on models with shrinked or bias-corrected parameter estimates. The SAS macros are freely available at www.akh-wien.ac.at/imc/biometrie/relimp
relative importance proportional hazards explained variation bias correction monotone likelihood separation shrinkage