vis.mirror.pt: Mirror plot comparing two probtrans objects

In hputter/mstate: Data Preparation, Estimation and Prediction in Multi-State Models

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 at some prediction time horizon.

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-mail]

See Also

plot.probtrans

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
)

hputter/mstate documentation built on Dec. 2, 2024, 8:16 p.m.