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Abstract

The relative importance of prognostic factors in regression can be measured either by standardized regression coefficients or by percentages of explained variation in a dependent variable. One advantage of using explained variation is the direct comparability of qualitative prognostic factors with others, or of groups of prognostic factors. The description of relative importance can be accomplished within marginal or partial effects analyses. It is demonstrated that it is possible not only to provide a descriptive ranking of prognostic factors according to their statistically determined importance, but also to make inferences concerning their relative importance, employing bootstrap techniques and procedures for multiple comparisons. The methods presented, which are new in the context of Cox regression, are exemplified by analyses of studies of lung cancer and breast cancer.