-- begin included message ----- 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 --- End included 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