I've understood that relative importance of predictors is a tricky question. Suggested methods range from very complex models to very simple variable transformations. I've understood that the brightest still debate which way to go on this matter. I'm looking for an easy but still appealing method to approach this in survival analysis (Cox regression).
My aim is to answer the question: which predictor is the most important one (in terms of predicting the outcome). The reason is simple: clinicians want to know which risk factor to adress first. I understand that "important" in clinical setting is not equal to "important" in the regression-world, but there is a link.
Should I compute the proportion of explainable log-likelihood that is explained by each variable (see Frank Harrell post), by using:
library(survival); library(rms) data(lung) S <- Surv(lung$time, lung$status) f <- cph(S ~ rcs(age,4) + sex, x=TRUE, y=TRUE, data=lung) plot(anova(f), what='proportion chisq')
As I understand it, its only possible to use the 'proportion chisq' for Cox models and this should suffice to convey some sense of each variables relative importance. Or should I perhaps use the default plot(anova()), which displays Wald χ2 statistic minus its degrees of freedom for assessing the partial effect of each variable?
I would appreciate some guidance if anyone has any experience on this matter.