期刊
COMPUTER METHODS AND PROGRAMS IN BIOMEDICINE
卷 71, 期 2, 页码 155-163出版社
ELSEVIER IRELAND LTD
DOI: 10.1016/S0169-2607(02)00077-9
关键词
bias correction; explained variation; monotone likelihood; proportional hazards; relative importance; separation; shrinkage
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. (C) 2002 Elsevier Science Ireland Ltd. All rights reserved.
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