Hi All, I have a left truncated, right censored cox model: coxph(Surv(start, stop, censor) ~ x + y, mydata) I would like to know how much of the observed variance (as a number between 0 and 1) is explained by each variable. How could I do that? Adding terms sequentially and then using anova(mod1, mod2) tells me whether I get a significant improvement of the fit, but does not tell me how much variance I actually explain? BW Federico -- Federico C. F. Calboli Neuroepidemiology and Ageing Research Imperial College, St. Mary's Campus Norfolk Place, London W2 1PG Tel +44 (0)20 75941602 Fax +44 (0)20 75943193 f.calboli [.a.t] imperial.ac.uk f.calboli [.a.t] gmail.com
--- begin included message --- I have a left truncated, right censored cox model: coxph(Surv(start, stop, censor) ~ x + y, mydata) I would like to know how much of the observed variance (as a number between 0 and 1) is explained by each variable. How could I do that? Adding terms sequentially and then using anova(mod1, mod2) tells me whether I get a significant improvement of the fit, but does not tell me how much variance I actually explain? --------- end inclusion -------- There is not a "variance explained" in a Cox model, since it does not try to predict the actual survival time. There are several proposals in the literature for an "approximate" R^2. If you use summary(fit) it will print one of the older ideas, which unfortuantely was shown by later literature to not be a very good approach (replacing this is another item on my long list of "someday fix"). At present your best choice is to use the c-statistic, which is also printed out by summary. Terry Therneau