mstate: vis.multiple.pt – R documentation

Become an expert in R — Interactive courses, Cheat Sheets, certificates and more!

vis.multiple.pt

Visualise multiple probtrans objects

Description

Helper function allow 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,
  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(s)

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

Examples

library(ggplot2)

data("aidssi")
head(aidssi)
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"
)

mstate

Data Preparation, Estimation and Prediction in Multi-State Models

v0.3.1

GPL (>= 2)

Authors

Hein Putter [aut, cre], Liesbeth C. de Wreede [aut], Marta Fiocco [aut], Ronald B. Geskus [ctb], Edouard F. Bonneville [ctb], Damjan Manevski [ctb]

Initial release

2020-12-17