Displaying 3 results from an estimated 3 matches for "tumorsize".
2018 Jan 15
1
Time-dependent coefficients in a Cox model with categorical variants
Suppose I have a dataset contain three variants, looks like
> head(dta)
Sex tumorsize Histology time status
0 1.5 2 12.1000 0
1 1.8 1 38.4000 0
.....................
Sex: 1 for male; 0 for female., two levels
Histology: 1 for SqCC; 2 for High risk AC; 3 for low ris...
2018 Jan 18
1
Time-dependent coefficients in a Cox model with categorical variants
...t),
function(x, t, ...) x* log(t)))
Terry Therneau
PS I've rarely found x*log(t) to be useful, but perhaps you have already looked at the cox.zph plots and see that shape.
Suppose I have a dataset contain three variants, looks like
head(dta)
Sex tumorsize Histology time status
0 1.5 2 12.1000 0
1 1.8 1 38.4000 0
.....................
Sex: 1 for male; 0 for female., two levels
Histology: 1 for SqCC; 2 for High risk AC; 3 for low r...
2012 Jul 05
0
Confused about multiple imputation with rms or Hmisc packages
...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...