Dear all,

I am using R 2.9.2 on Windows XP.

I am undertaking  a simulation study to consider methods of external validation in the context of missing covariates in the validation data set.  I would like to use Royston's measure of prognostic separation as a method of external validation.  Although there is no R code for this, the author informs me that it should be easy:

1. Calculate the linear predictor xb from the proportional hazards model or other model of interest.
2. Calculate the ranks r1,...,rn of xb. In case of ties, do not average the ranks.
3. Calculate scaled rankits zi = sqrt(_pi/8) * invnormal((ri - 3/8)/(n +1/4)) where invnormal() is the inverse normal 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.

Many thanks,
Laura


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