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2011 May 05
1
Confidence interval for difference in Harrell's c statistics (or equivalently Somers' D statistics)
...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...
2011 Sep 14
0
Confidence interval or p-value for difference in two c-statistics
...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...
2011 Nov 23
1
Measure of separation for survival data
...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...