Description Usage Arguments Details Value Author(s) References See Also Examples
Calculates the conditional expected longitudinal values for a
new subject from the last observation time given their longitudinal
history data and a fitted mjoint
object.
1 2 3 4 5 6 7 8 9 10 11 12 13 
object 
an object inheriting from class 
newdata 
a list of 
newSurvData 
a 
u 
an optional time that must be greater than the last observed
measurement time. If omitted (default is 
type 
a character string for whether a firstorder
( 
M 
for 
scale 
a numeric scalar that scales the variance parameter of the proposal distribution for the MetropolisHastings algorithm, which therefore controls the acceptance rate of the sampling algorithm. 
ci 
a numeric value with value in the interval (0, 1) specifying
the confidence interval level for predictions of 
progress 
logical: should a progress bar be shown on the console to
indicate the percentage of simulations completed? Default is

ntimes 
an integer controlling the number of points to discretize the
extrapolated time region into. Default is 
level 
an optional integer giving the level of grouping to be used in
extracting the residuals from object. Level values increase from outermost
to innermost grouping, with level 0 corresponding to the population model
fit and level 1 corresponding to subjectspecific model fit. Defaults to

Dynamic predictions for the longitudinal data submodel based on an observed measurement history for the longitudinal outcomes of a new subject are based on either a firstorder approximation or Monte Carlo simulation approach, both of which are described in Rizopoulos (2011). Namely, given that the subject was last observed at time t, we calculate the conditional expectation of each longitudinal outcome at time u as
E[y_k(u)  T ≥ t, y, θ] \approx x^T(u)β_k + z^T(u)\hat{b}_k,
where T is the failure time for the new subject, and y is the stackedvector of longitudinal measurements up to time t.
First order predictions
For type="firstorder"
, \hat{b} is the mode of the posterior
distribution of the random effects given by
\hat{b} = {\arg \max}_b f(b  y, T ≥ t; θ).
The predictions are based on plugging in θ = \hat{θ}, which
is extracted from the mjoint
object.
Monte Carlo simulation predictions
For type="simulated"
, θ is drawn from a multivariate
normal distribution with means \hat{θ} and variancecovariance
matrix both extracted from the fitted mjoint
object via the
coef()
and vcov()
functions. \hat{b} is drawn from the
the posterior distribution of the random effects
f(b  y, T ≥ t; θ)
by means of a MetropolisHasting algorithm with independent multivariate
noncentral tdistribution proposal distributions with
noncentrality parameter \hat{b} from the firstorder prediction and
variancecovariance matrix equal to scale
\times the inverse
of the negative Hessian of the posterior distribution. The choice os
scale
can be used to tune the acceptance rate of the
MetropolisHastings sampler. This simulation algorithm is iterated M
times, at each time calculating the conditional survival probability.
A list object inheriting from class dynLong
. The list returns
the arguments of the function and a list containing K
data.frame
s of 2 columns, with first column (named
timeVar[k]
; see mjoint
) denoting times and the second
column (named y.pred
) denoting the expected outcome at each time
point.
Graeme L. Hickey (graemeleehickey@gmail.com)
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  ## 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, ])
dynLong(fit2, hvd2)
dynLong(fit2, hvd2, u = 7) # outcomes at 7years only
out < dynLong(fit2, hvd2, type = "simulated")
out
## End(Not run)

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