Visualise multiple probtrans objects — vis.multiple.pt • mstate

Helper function allowing to visualise state probabilities for different reference patients/covariates. Multiple "probtrans" objects are thus needed.

Usage

vis.multiple.pt(
  x,
  from = 1,
  to,
  xlab = "Time",
  ylab = "Probability",
  xlim = NULL,
  ylim = NULL,
  cols,
  lwd,
  labels,
  conf.int = 0.95,
  conf.type = c("log", "plain", "none"),
  legend.title
)

Arguments

x: A list of "probtrans" objects
from: The starting state from which the probabilities are used to plot Numeric, as in plot.probtrans
to: (Numeric) destination state
xlab: A title for the x-axis; default is "Time"
ylab: A title for the y-axis; default is "Probability"
xlim: The x limits of the plot(s), default is range of time
ylim: The y limits of the plot(s); if ylim is specified for type="separate", then all plots use the same ylim for y limits
cols: A vector specifying colors for the different transitions; default is a palette from green to red, when type="filled" (reordered according to ord, and 1 (black), otherwise
lwd: The line width, see par; default is 1
labels: Character vector labelling each element of x (e.g. label for a reference patient) - so labels = c("Patient 1", "Patient 2")
conf.int: Confidence level (%) from 0-1 for probabilities, default is 0.95 (95% CI). Setting to 0 removes the CIs.
conf.type: Type of confidence interval - either "log" or "plain" . See function details for details.
legend.title: Character - title of legend

Value

A ggplot object.

Author

Edouard F. Bonneville e.f.bonneville@lumc.nl

Examples


library(ggplot2)

data("aidssi")
head(aidssi)
#>   patnr   time status      cause ccr5
#> 1     1  9.106      1       AIDS   WW
#> 2     2 11.039      0 event-free   WM
#> 3     3  2.234      1       AIDS   WW
#> 4     4  9.878      2         SI   WM
#> 5     5  3.819      1       AIDS   WW
#> 6     6  6.801      1       AIDS   WW
si <- aidssi

# Prepare transition matrix
tmat <- trans.comprisk(2, names = c("event-free", "AIDS", "SI"))

# Run msprep
si$stat1 <- as.numeric(si$status == 1)
si$stat2 <- as.numeric(si$status == 2)

silong <- msprep(
time = c(NA, "time", "time"), 
status = c(NA, "stat1", "stat2"), 
data = si, keep = "ccr5", trans = tmat
)

# Run cox model
silong <- expand.covs(silong, "ccr5")
c1 <- coxph(Surv(time, status) ~ ccr5WM.1 + ccr5WM.2 + strata(trans),
            data = silong)
            
# 1. Prepare patient data - both CCR5 genotypes
WW <- data.frame(
ccr5WM.1 = c(0, 0), 
ccr5WM.2 = c(0, 0), 
trans = c(1, 2), 
strata = c(1, 2)
)

WM <- data.frame(
ccr5WM.1 = c(1, 0), 
ccr5WM.2 = c(0, 1),
trans = c(1, 2), 
strata = c(1, 2)
)

# 2. Make msfit objects
msf.WW <- msfit(c1, WW, trans = tmat)
msf.WM <- msfit(c1, WM, trans = tmat)

# 3. Make probtrans objects
pt.WW <- probtrans(msf.WW, predt = 0)
pt.WM <- probtrans(msf.WM, predt = 0)           

# Plot - see vignette for more details
vis.multiple.pt(
x = list(pt.WW, pt.WM), 
from = 1,
to = 2, 
conf.type = "log",
cols = c(1, 2),
labels = c("Pat WW", "Pat WM"),
legend.title = "Ref patients"
)