Joint plot of longitudinal and survival data
This function views the longitudinal profile of each unit with the last longitudinal measurement prior to event-time (censored or not) taken as the end-point, referred to as time zero. In doing so, the shape of the profile prior to event-time can be inspected. This can be done over a user-specified number of time units.
jointplot( object, Y.col, Cens.col, lag, split = TRUE, col1, col2, xlab, ylab, gp1lab, gp2lab, smooth = 2/3, mean.profile = FALSE, mcol1, mcol2 )
object |
an object of class |
Y.col |
an element of class |
Cens.col |
an element of class |
lag |
argument which specifies how many units in time we look back through. Defaults to the maximum observation time across all units. |
split |
logical argument which allows the profiles of units which
fail and those which are censored to be viewed in separate
panels of the same graph. This is the default option. Using |
col1 |
argument to choose the colour for the profiles of the censored units. |
col2 |
argument to choose the colour for the profiles of the failed units. |
xlab |
an element of class |
ylab |
an element of class |
gp1lab |
an element of class |
gp2lab |
an element of class |
smooth |
the smoother span. This gives the proportion of points in the
plot which influence the smooth at each value. Defaults to a value of 2/3.
Larger values give more smoothness. See |
mean.profile |
draw mean profiles if TRUE. Only applies to the
|
mcol1 |
argument to choose the colour for the mean profile of the units with a censoring indicator of zero. |
mcol2 |
argument to choose the colour for the mean profile of the units with a censoring indicator of one. |
The function tailors the xyplot
function to
produce a representation of joint data with longitudinal and survival
components.
A lattice plot.
If more than one cause of failure is present (i.e. competing risks data), then all failures are pooled together into a single failure type.
Pete Philipson (pete.philipson@northumbria.ac.uk)
Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics. 1997; 53(1): 330-339.
data(heart.valve) heart.surv <- UniqueVariables(heart.valve, var.col = c("fuyrs", "status"), id.col = "num") heart.long <- heart.valve[, c("num", "time", "log.lvmi")] heart.cov <- UniqueVariables(heart.valve, c("age", "sex"), id.col = "num") heart.valve.jd <- jointdata(longitudinal = heart.long, baseline = heart.cov, survival = heart.surv, id.col = "num", time.col = "time") jointplot(heart.valve.jd, Y.col = "log.lvmi", Cens.col = "status", lag = 5)
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