mstate: vis.mirror.pt – R documentation

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vis.mirror.pt

Mirror plot comparing two probtrans objects

Description

A mirror plot for comparing two different "probtrans" objects. Useful for comparing predicted probabilities for different levels of a covariate, or for different subgroups.

Usage

vis.mirror.pt(
  x,
  titles,
  size_titles = 5,
  horizon = NULL,
  breaks_x_left,
  breaks_x_right,
  from = 1,
  cols,
  ord,
  xlab = "Time",
  ylab = "Probability",
  legend.pos = "right"
)

Arguments

`x`	A list of two `"probtrans"` objects. The first element will be on the left of the mirror plot, and the second on the right
`titles`	A character vector c("Title for left", "Title for right")
`size_titles`	Numeric, size of the title text
`horizon`	Numeric, position denoting (in time) where to symmetrically mirror the plots. Default is maximum follow-up of from both plots.
`breaks_x_left`	Numeric vector specifying axis breaks on the left plot
`breaks_x_right`	Numeric vector specifying axis breaks on the right plot
`from`	The starting state from which the probabilities are used to plot
`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
`ord`	A vector of length equal to the number of states, specifying the order of plotting in case type=`"stacked"` or `"filled"`
`xlab`	A title for the x-axis, default is "Time"
`ylab`	A title for the y-axis, default is "Probability"
`legend.pos`	Position of the legend, default is "right"

Value

A ggplot2 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 reference 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)           

# Mirror plot split at 10 years - see vignette for more details
vis.mirror.pt(
x = list(pt.WW, pt.WM),
titles = c("WW", "WM"),
horizon = 10
)

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