title: Comparing the importance of prognostic factors in Cox and logistic regression using SAS.
abstract: 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 R(2) 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
authors.affiliation: Department of Medical Computer Sciences, University of Vienna, A-1090 Vienna, Spitalgasse 23, Austria. georg.heinze@akh-wien.ac.at
authors.forename: Georg
authors.initials: G
authors.lastname: Heinze
authors.forename: Michael
authors.initials: M
authors.lastname: Schemper
mesh_terms: D003627:Data Interpretation, Statistical; D006801:Humans; D007230:Infant, Low Birth Weight; D007231:Infant, Newborn; D016015:Logistic Models; D016016:Proportional Hazards Models