Here is an example of how to do it.
> library(survival)
> vfit <- coxph(Surv(time, status) ~ celltype + trt, data=veteran)
> userinput <- data.frame(celltype="smallcell", trt = 1)
> usercurve <- survfit(vfit, newdata=userinput) #the entire predicted
survival curve
> user2 <- summary(usercurve, time= 2*365.25) # 2 year time point
> user2$surv
[1] 0.0007438084
Some comments:
1. The summary function for survival curves was written so that people
could print out shorter summaries, but it works nicely for picking off a
particular time point. Since the curve is a step function and there
isn't likely to be a step at exactly "x" years, this is a bit more work
to do yourself. You might want to include the confidence limits in your
web report as well.
2. Put the whole formula into your coxph call. I have never ever
understood why people use the construct
tempvar <- with(data, Surv(time, status))
coxph(tempvar ~ age + sex + ....
It leaves you with harder to read code, poorer documentation (the
printout from coxph no longer shows the actual response variable), leads
to hard-to-diagnose failures for certain uses of predict, ... the list
goes on. I have not yet thought of a single good reason for doing it
other than "because you can".
3. Make the user data the same as the original. In the veteran cancer
data set "trt" is a numeric 0/1 variable, you had it as a factor in the
new data set.
4. Your should get your keyboard fixed -- it appears that the spacebar
is disabled when writing code :-)
5. If you plot the survival curve for the veterans cancer data set it
only reaches to about 2 1/2 years, so the summary for 5 years will
return NULL.
Terry Therneau
On 07/18/2012 05:00 AM, r-help-requ...@r-project.org wrote:
I am a medical student and as a capstone for my summer research project I am
going to create a simple online web "calculator" for users to input their
relevant data, and a probability of relapse within 5 years will be computed
and returned based on the Cox PH model I have developed.
The issue I'm having is finding a definitive method/function to feed the
user's "newdata" and return the probability of relapse within 5 years. I
have googled this and the answers seems to be inconsistent; I have variously
seen people recommend survest(), survfit(), and predict.coxph(). Terry had
a useful answer
http://r.789695.n4.nabble.com/how-to-calculate-predicted-probability-of-Cox-model-td4515265.html
here but I didn't quite understand what he meant in his last sentence.
Here is some code for you to quickly illustrate what you suggest.
library(rms)
library(survival)
library(Hmisc)
data(veteran)
dd=datadist(veteran)
options(datadist='dd')
options(digits=4)
obj=with(veteran,Surv(time,status))
vetcoxph=coxph(obj~celltype+trt,data=veteran) #I will fit models from
both the survival and rms packages so you can
#use what you like
vetcph=cph(obj~celltype+trt,data=veteran,surv=TRUE,time.inc=5*365,x=T,y=T)
#let's say the user inputted that their cell type was smallcell and their
treatment was "1".
userinput=data.frame(celltype='smallcell',trt=factor(1))
I really appreciate your recommendations
Best,
Jahan
______________________________________________
R-help@r-project.org mailing list
https://stat.ethz.ch/mailman/listinfo/r-help
PLEASE do read the posting guide http://www.R-project.org/posting-guide.html
and provide commented, minimal, self-contained, reproducible code.
