R help - Jul 2012 - Confused about multiple imputation with rms or Hmisc packages

Hello,
I'm working on a Cox Proportional Hazards model for a cancer data set  that
has missing values for the categorical variable "Grade" in less than
10% of the observations.  I'm not a statistician, but based on my readings
of Frank Harrell's book it seems to be a candidate for using multiple
imputation technique(s).  I understand the concepts behind imputation, but using
the functions in rms and Hmisc is confounding me.  For instance, whether to use
transcan or aregImpute.

Here is a sample of my data: https://dl.dropbox.com/u/1852742/sample.csv

Drawing from Chapter 8 of Harrell's book, this is what I've been toying
with:

#recurfree_survival_fromsx  is survival time, rf_obs_sx codes for events as a
binary variable.

#The CPH model I would like to fit, using Ograde_dx as the variable for overall
grade at
#diagnosis, ord_nodes as an ordinal variable for the # lymph nodes involved.
obj=with(mydata, Surv(recurfree_survival_fromsx,rf_obs_sx))
mod=cph(obj~ord_nodes+Ograde_dx+ERorPR+HER2_Sum,data=mydata,x=T,y=T)
#Impute missing data
mydata.transcan=transcan(~Ograde_dx+tumorsize+ord_nodes+simp_stage_path+afam+
Menopause+Age,imputed=T,n.impute=10)
summary(mydata.transcan)

The issues I have are:

a)      In your opinion(s), should I even be imputing this data?  Is it
appropriate here?

b)      Even after reading the help pages and Harrell's book, I'm not
sure I used the correct imputation method, and whether I should be using
transcan or aregImpute.

c)       In the output of summary(transcan), is R-squared the best value to
describe how reliably the function could predict Ograde_dx?  What is an
acceptable level?

d)      Do I use the function fit.mult.impute to fit my final cph model?

I appreciate your help with this as it is a somewhat confusing topic.  I hope I
gave you all the information you need to answer my questions.

Sincerely,
Jahan








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