Description Usage Arguments Details Value Author(s) See Also Examples
Using the available longitudinal information up to a particular time point, this function computes an estimate of the information we again by obtaining an extra longitudinal measurement.
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object 
an object inheriting from class 
newdata 
a data frame that contains the longitudinal and covariate information for
the subject for whom we wish to plan the next measurement. The names of the variables in
this data frame must be the same as in the data frames that were used to fit the linear
mixed effects model (using 
Dt 
numeric scalar denoting the length of the time interval to search for the
optimal time point of the next measurement, i.e., the interval is (t, t + Delta t]
with Delta t given by 
K 
numeric scalar denoting the number of time points to cosider in the interval (t, t + Delta t]. 
idVar 
the name of the variable in 
simulateFun 
a function based on which longitudinal measurement can be simulated.
This should have as a main argument the variable 
M 
a numeric scalar denoting the number of Monte Carlo samples. 
seed 
a numeric scalar 
This functions computes the following posterior predictive distribution
E_{Y} [ E_{T^*  Y} (\log p (T_j^* \mid T_j^* > u, \{ Y_j(t), y_j(u) \}, D_n \bigr )) ],
where T_j^* denotes the timetoevent for subject j for whom we wish to plan the next visit, Y_j(t) the available longitudinal measurements of this subject up to time t, y_j(u) the future longitudinal measurement we wish to plan at time u > t, and D_n the data set that was used to fit the joint model.
A list with components:
summary 
a numeric matrix with first column the time points at which the longitudinal measurement is hypothetically taken, second column the estimated information we gain by obtaining the measurement, and third column the estimated cumulative risk of an event up to the particular time point denoted in the first column. 
full.results 
a numeric matrix with columns representing the time points, rows the Monte Carlo samples, and entries the value of log posterio predictive density. 
Dimitris Rizopoulos d.rizopoulos@erasmusmc.nl
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16  ## 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 subjectspecific
# longitudinal trajectories and a splineapproximated baseline
# risk function
lmeFit < lme(log(serBilir) ~ ns(year, 3),
random = list(id = pdDiag(form = ~ ns(year, 3))), data = pbc2)
survFit < coxph(Surv(years, status2) ~ drug, data = pbc2.id, x = TRUE)
jointFit < jointModelBayes(lmeFit, survFit, timeVar = "year")
dynInfo(jointFit, newdata = pbc2[pbc2$id == 2, ], Dt = 5)[[1]]
## End(Not run)

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