Plot Predictions From a Cause-specific Cox Proportional Hazard Regression
Plot predictions from a Cause-specific Cox proportional hazard regression.
## S3 method for class 'predictCSC' autoplot( object, ci = object$se, band = object$band, plot = TRUE, smooth = FALSE, digits = 2, alpha = NA, group.by = "row", reduce.data = FALSE, ... )
object |
Object obtained with the function |
ci |
[logical] If |
band |
[logical] If |
plot |
[logical] Should the graphic be plotted. |
smooth |
[logical] Should a smooth version of the risk function be plotted instead of a simple function? |
digits |
[integer] Number of decimal places. |
alpha |
[numeric, 0-1] Transparency of the confidence bands. Argument passed to |
group.by |
[character] The grouping factor used to color the prediction curves. Can be |
reduce.data |
[logical] If |
... |
Additional parameters to cutomize the display. |
Invisible. A list containing:
plot: the ggplot object.
data: the data used to create the plot.
predict.CauseSpecificCox to compute risks based on a CSC model.
library(survival)
library(rms)
library(ggplot2)
library(prodlim)
#### simulate data ####
set.seed(10)
d <- sampleData(1e2, outcome = "competing.risks")
seqTau <- c(0,unique(sort(d[d$event==1,time])), max(d$time))
#### CSC model ####
m.CSC <- CSC(Hist(time,event)~ X1 + X2 + X6, data = d)
pred.CSC <- predict(m.CSC, newdata = d[1:2,], time = seqTau, cause = 1, band = TRUE)
autoplot(pred.CSC, alpha = 0.2)
#### stratified CSC model ####
m.SCSC <- CSC(Hist(time,event)~ strata(X1) + strata(X2) + X6,
data = d)
pred.SCSC <- predict(m.SCSC, time = seqTau, newdata = d[1:4,],
cause = 1, keep.newdata = TRUE, keep.strata = TRUE)
autoplot(pred.SCSC, group.by = "strata")Please choose more modern alternatives, such as Google Chrome or Mozilla Firefox.