JMbayes (version 0.8-85)

plot.survfitJM: Plot Method for survfit.JMbayes and survfit.mvJMbayes Objects

Description

Produces plots of conditional probabilities of survival.

Usage

# S3 method for survfit.JMbayes
plot(x, estimator = c("both", "mean", "median"), 
    which = NULL, fun = NULL, invlink = NULL, conf.int = FALSE, 
    fill.area = FALSE, col.area = "grey", col.abline = "black", col.points = "black",
    add.last.time.axis.tick = FALSE, include.y = FALSE, main = NULL, 
    xlab = NULL, ylab = NULL, ylab2 = NULL, lty = NULL, col = NULL, 
    lwd = NULL, pch = NULL, ask = NULL, legend = FALSE, …,
    cex.axis.z = 1, cex.lab.z = 1, xlim = NULL)
    
# S3 method for survfit.mvJMbayes
plot(x, split = c(1, 1), which_subjects = NULL, 
    which_outcomes = NULL, surv_in_all = TRUE, include.y = TRUE, fun = NULL,
    abline = NULL,
    main = NULL, xlab = "Time", ylab = NULL, zlab = "Event-Free Probability",
    include_CI = TRUE, fill_area_CI = TRUE, col_points = "black", 
    pch_points = 1, col_lines = "red", col_lines_CI = "black", 
    col_fill_CI = "lightgrey", lwd_lines = 2, lty_lines_CI = 2, 
    cex_xlab = 1, cex_ylab = 1, cex_zlab = 1, cex_main = 1, 
    cex_axis = 1, …)

Arguments

x

an object inheriting from class survfit.JMbayes or class survfit.mvJMbayes.

estimator

character string specifying, whether to include in the plot the mean of the conditional probabilities of survival, the median or both. The mean and median are taken as estimates of these conditional probabilities over the M replications of the Monte Carlo scheme described in survfitJM.

which

an integer or character vector specifying for which subjects to produce the plot. If a character vector, then is should contain a subset of the values of the idVar variable of the newdata argument of survfitJM.

which_subjects

an integer vector specifying for which subjects to produce the plot.

split

a integer vector of length 2 indicating in how many panels to construct, i.e., number of rows and number of columns.

which_outcomes

integer vector indicating which longitudinal outcomes to include in the plot.

surv_in_all

logical; should the survival function be included in all panels.

fun

a vectorized function defining a transformation of the survival curve. For example, with fun=log the log-survival curve is drawn.

abline

a list with arguments to abline().

invlink

a function to transform the fitted values of the longitudinal outcome.

conf.int, include_CI

logical; if TRUE, then a pointwise confidence interval is included in the plot.

fill.area, fill_area_CI

logical; if TRUE the area defined by the confidence interval of the survival function is put in color.

col.area, col_fill_CI

the color of the area defined by the confidence interval of the survival function.

col.abline, col.points, col_points, col_lines, col_lines_CI

the color for the vertical line and the points when include.y is TRUE.

add.last.time.axis.tick

logical; if TRUE, a tick is added in the x-axis for the last available time point for which a longitudinal measurement was available.

include.y

logical; if TRUE, two plots are produced per subject, i.e., the plot of conditional probabilities of survival and a scatterplot of his longitudinal measurements.

main

a character string specifying the title in the plot.

xlab

a character string specifying the x-axis label in the plot.

ylab

a character string specifying the y-axis label in the plot.

ylab2

a character string specifying the y-axis label in the plot, when include.y = TRUE.

zlab

a character string specifying the z-axis (vertical right-hand side) label in the plot.

lty, lty_lines_CI

what types of lines to use.

col

which colors to use.

lwd, lwd_lines

the thickness of the lines.

pch, pch_points

the type of points to use.

ask

logical; if TRUE, the user is asked before each plot, see par().

legend

logical; if TRUE, a legend is included in the plot.

cex.axis.z, cex.lab.z

the par cex argument for the axis at side 4, when include.y = TRUE.

cex_xlab, cex_ylab, cex_zlab, cex_main, cex_axis

the par cex argument for the axis in side 1 (x-axis), side 2 (y-axis), side 4 (z-axis) and the title of the plot.

xlim

the par xlim argument.

…

extra graphical parameters passed to plot().

References

Rizopoulos, D. (2016). The R package JMbayes for fitting joint models for longitudinal and time-to-event data using MCMC. Journal of Statistical Software 72(7), 1--45. doi:10.18637/jss.v072.i07.

Rizopoulos, D. (2012) Joint Models for Longitudinal and Time-to-Event Data: with Applications in R. Boca Raton: Chapman and Hall/CRC.

Rizopoulos, D. (2011). Dynamic predictions and prospective accuracy in joint models for longitudinal and time-to-event data. Biometrics 67, 819--829.

See Also

survfitJM

Examples

# NOT RUN {
# we construct the composite event indicator (transplantation or death)
pbc2$status2 <- as.numeric(pbc2$status != "alive")
pbc2.id$status2 <- as.numeric(pbc2.id$status != "alive")

# we fit the joint model using splines for the subject-specific 
# longitudinal trajectories and a spline-approximated baseline
# risk function
lmeFit <- lme(log(serBilir) ~ ns(year, 2), data = pbc2,
    random = ~ ns(year, 2) | id)
survFit <- coxph(Surv(years, status2) ~ drug, data = pbc2.id, x = TRUE)
jointFit <- jointModelBayes(lmeFit, survFit, timeVar = "year")

# we will compute survival probabilities for Subject 2 in a dynamic manner, 
# i.e., after each longitudinal measurement is recorded
ND <- pbc2[pbc2$id == 2, ] # the data of Subject 2
survPreds <- vector("list", nrow(ND))
for (i in 1:nrow(ND)) {
    survPreds[[i]] <- survfitJM(jointFit, newdata = ND[1:i, ])
}

# the default call to the plot method using the first three
# longitudinal measurements
plot(survPreds[[3]])

# we produce the corresponding plot
par(mfrow = c(2, 2), oma = c(0, 2, 0, 2))
for (i in c(1,3,5,7)) {
    plot(survPreds[[i]], estimator = "median", conf.int = TRUE,
        include.y = TRUE, main = paste("Follow-up time:",
            round(survPreds[[i]]$last.time, 1)), ylab = "", ylab2 = "")
}
mtext("log serum bilirubin", side = 2, line = -1, outer = TRUE)
mtext("Survival Probability", side = 4, line = -1, outer = TRUE)
# }