`peakVO2.Rd`

The data involve 2231 patients with systolic heart failure who underwent cardiopulmonary stress testing at the Cleveland Clinic. The primary end point was all-cause death. In total, 39 variables were measured for each patient, including baseline clinical values and exercise stress test results. A key variable of interest is peak VO2 (mL/kg per min), the peak respiratory exchange ratio. More details regarding the data can be found in Hsich et al. (2011).

Hsich E., Gorodeski E.Z.,Blackstone E.H., Ishwaran H. and Lauer M.S. (2011). Identifying important risk factors for survival in systolic heart failure patients using random survival forests. Circulation: Cardio. Qual. Outcomes, 4(1), 39-45.

```
# \donttest{
## load the data
data(peakVO2, package = "randomForestSRC")
## random survival forest analysis
o <- rfsrc(Surv(ttodead, died)~., peakVO2)
print(o)
## partial effect of peak V02 on mortality
partial.o <- partial(o,
partial.type = "mort",
partial.xvar = "peak.vo2",
partial.values = o$xvar$peak.vo2,
partial.time = o$time.interest)
pdta.m <- get.partial.plot.data(partial.o)
## partial effect of peak V02 on survival
pvo2 <- quantile(o$xvar$peak.vo2)
partial.o <- partial(o,
partial.type = "surv",
partial.xvar = "peak.vo2",
partial.values = pvo2,
partial.time = o$time.interest)
pdta.s <- get.partial.plot.data(partial.o)
## compare the two plots
par(mfrow=c(1,2))
plot(lowess(pdta.m$x, pdta.m$yhat, f = 2/3),
type = "l", xlab = "peak VO2", ylab = "adjusted mortality")
rug(o$xvar$peak.vo2)
matplot(pdta.s$partial.time, t(pdta.s$yhat), type = "l", lty = 1,
xlab = "years", ylab = "peak VO2 adjusted survival")
legend("bottomleft", legend = paste0("peak VO2 = ", pvo2),
bty = "n", cex = .75, fill = 1:5)
# }
```