Description Usage Arguments Details Value Author(s) References See Also Examples
Plots the conditional timetoevent distribution for a
new subject calculated using the dynSurv
function.
1 2 3 4 5 6 7 8 9 10 11 12 
x 
an object of class 
main 
an overall title for the plot: see 
xlab 
a title for the x [time] axis: see 
ylab1 
a character vector of the titles for the K longitudinal
outcomes yaxes: see 
ylab2 
a title for the eventtime outcome axis: see

grid 
adds a rectangular grid to an existing plot: see

estimator 
a character string that can take values 
smooth 
logical: whether to overlay a smooth survival curve (see
Details). Defaults to 
... 
additional plotting arguments; currently limited to 
The joineRML
package is based on a semiparametric model,
such that the baseline hazards function is left unspecified. For
prediction, it might be preferable to have a smooth survival curve. Rather
than changing modelling framework a prior, a constrained Bsplines
nonparametric median quantile curve is estimated using
cobs
, with a penalty function of λ=1, and
subject to constraints of monotonicity and S(t)=1.
A dynamic prediction plot.
Graeme L. Hickey (graemeleehickey@gmail.com)
Ng P, Maechler M. A fast and efficient implementation of qualitatively constrained quantile smoothing splines. Statistical Modelling. 2007; 7(4): 315328.
Rizopoulos D. Dynamic predictions and prospective accuracy in joint models for longitudinal and timetoevent data. Biometrics. 2011; 67: 819–829.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30  ## Not run:
# Fit a joint model with bivariate longitudinal outcomes
data(heart.valve)
hvd < heart.valve[!is.na(heart.valve$log.grad) & !is.na(heart.valve$log.lvmi), ]
fit2 < mjoint(
formLongFixed = list("grad" = log.grad ~ time + sex + hs,
"lvmi" = log.lvmi ~ time + sex),
formLongRandom = list("grad" = ~ 1  num,
"lvmi" = ~ time  num),
formSurv = Surv(fuyrs, status) ~ age,
data = list(hvd, hvd),
inits = list("gamma" = c(0.11, 1.51, 0.80)),
timeVar = "time",
verbose = TRUE)
hvd2 < droplevels(hvd[hvd$num == 1, ])
out1 < dynSurv(fit2, hvd2)
plot(out1, main = "Patient 1")
## End(Not run)
## Not run:
# Monte Carlo simulation with 95% confidence intervals on plot
out2 < dynSurv(fit2, hvd2, type = "simulated", M = 200)
plot(out2, main = "Patient 1")
## End(Not run)

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