[R] Question on survival
therneau at mayo.edu
Fri Oct 12 15:08:56 CEST 2012
It is easy to get a cumulative hazard curve.
First, decide what values of "age", "a", and "b" you want curves for
tdata<- data.frame(age=55, a=2, b=6)
Get the curves, there will be one for each strata in the output
sfit<- survfit(coxPhMod, newdata= tdata)
plot(sfit, fun='cumhaz', col=1:4, xlab= etc etc)
Hazard functions are something else again, estimating these rigorously is
akin to density estimation. A quick and dirty method is to use smooth.spline.
temp<- sfit #grab the first curve
tfit<- smooth.spline(temp$time, -log(temp$surv), df= 5) #smooth the cum haz
That value of "df=5" is made up -- you need to decide for yourself how much smoothing to do.
I make no claims that this is statistically well grounded, it's just a good way to get
a quick idea.
PS; There is no such thing as "THE" baseline hazard function; predictions are always for some particular value of the covariates. In a book it is sometimes useful to pick a particular set of x values as a default in order to simplify notation, often x=0, and label that as a baseline. But in actual computation all zeros is usually crazy (age=0, weight=0, blood pressure=0, etc).
I'm going crazy trying to plot a quite simple graph.
i need to plot estimated hazard rate from a cox model.
supposing the model i like this:
coxPhMod=coxph(Surv(TIME, EV) ~ AGE+A+B+strata(C) data=data)
with 4 level for C.
how can i obtain a graph with 4 estimated (better smoothed) hazard curve
(base-line hazard + 3 proportional) to highlight the effect of C.
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