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I am looking at a continuous variable, age. I am looking at time to
12-month remission and can calculate the HR and 95% confidence interval are
follows:
coxfita = coxph(Surv(rem.Remtime,rem.Rcens)~nearma$all.age,data=nearma)
exp(coxfita$coefficients)
exp(confint(coxfita))
However, because I am looking at age as a continuous variable I cannot draw
a Kaplan-Meier curve. Instead I need to draw a plot of hazard against age.
How do I do this? I don't think
plot(nearma$all.age,coxfita$linear.predictors) is quite right.
Thank you for your help,
Laura
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There is not a simple plot: hazard is a function of both time and age, so will
be a 2-d surface.
A common plan is to plot the predicted survival for a small set of ages.
ageset <- c( 40, 55, 70)
curves <- survfit(coxfita, newdata= data.frame(all.age = ageset))
plot(curves, col=1:3)
This will give a plot with 3 predicted survival curves, for ages 40, 55 and 70.
The plot of hazard vs age at any particular time point is not very
interesting: it will be h(t, age) = k exp(coefficient * age). This does not
reflect the data in any way, just your decision that age would be a linear term
in the model. If you want to explore the effect of age, then fit the
generalized additive Cox model
addfit <- coxph(Surv(rem.Remtime,rem.Rcens)~ pspline(all.age), nearma)
temp <- predict(addfit, type='terms', se=TRUE)
matplot(nearma$all.age, exp(cbind(temp$fit,
temp$fit - 2* temp$se.fit,
temp$fit + 2* temp$se.fit)),
log='y', xlab="Age", ylab="Estimated
Relative Risk")
I suspect this is what you actually want.
Terry Therneau
