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...ta) and the respective c statistics for the variables in both cases. What I would now like to do is to prove that there is no statistically significant difference between the statistics (between the dev and val datasets.) Any help would be much appreciated. > rdev <- rcorrcens(Surv(stimes1,eind1)~gendat1+neurodat1) > rdev Somers' Rank Correlation for Censored Data Response variable:Surv(stimes1, eind1) C Dxy aDxy SD Z P n gendat1 0.534 0.069 0.069 0.017 3.98 0.0001 1500 neurodat1 0.482 -0.036 0.036 0.011 3.18 0.0015 1500 > rval <- rcorr...

...ta I gather that this can be done using the lincom command but I am not aware of anything similar in R. I am doing a simulation study and have simulated two datasets (independent). I can obtain c-statistics for each variable in each dataset using this code: > rdev <- rcorrcens(Surv(stimes1,eind1)~gendat1+neurodat1) > rdev Somers' Rank Correlation for Censored Data Response variable:Surv(stimes1, eind1) C Dxy aDxy SD Z P n gendat1 0.534 0.069 0.069 0.017 3.98 0.0001 1500 neurodat1 0.482 -0.036 0.036 0.011 3.18 0.0015 1500 > rval <- rcorr...

...distribution function, and where n is the sample size and _pi = 3.141592654 ... 4. In case of ties in the original xb, substitute average zi's over each of the tied sets. 5. Perform regression (e.g. Cox) on z = z1,...,zn. I can obviously do steps 1 and 2: # step 1 # cox1 <- cph(Surv(stime1,eind1)~adat1+bdat1+c11+c21) c1dat <- cox1$linear.predictors # step 2 # ris <- rank(c1dat,ties.method="minimum") Can anyone advise how I might invoke the inverse normal. I thought 'qnorm' may be an option but I'm lacking the necessary parameters within the framework above. M...