Description Usage Arguments Value Author(s) References
This function fits a joint model for multivariate longitudinal markers (possibly summarized into latent processes) and clinical endpoints. The estimation is performed within the Maximum Likelihood Framework benefiting from an exact likelihood formulation. More details are given below.
Each dimension (constituted of a unique longitudinal marker or of several longitudinal markers measuring the same underlying latent process) is modeled according to a mixed model that handles continuous (Gaussian or nonGaussian, curvilinear) data. The technique for each dimension follows the methodology of lcmm and multlcmm functions (lcmm package). All the dimensions can be correlated through correlated random effects. The dimensions are linked with one or two competing clinical endpoints through a joint degradation process model. Specifically, each clinical endpoint is defined as a binary repeated endpoint either measured at various visit times or derived from a timetoevent discretized into time intervals. The model assumes that the clinical endpoint reaches 1 when its underlying degradation process becomes above a threshold to estimate, this degradation process being defined as a linear combination of the longitudinal dimensions.
The methodology is fully described in : ProustLima C, Philipps V, Dartigues JF (2018). A joint model for multiple dynamic processes and clinical endpoints: application to Alzheimer’s disease. https://arxiv.org/abs/1803.10043
1 2 3 4 
Y 
a list of 
D 
a list of twosided formula defining the event part of the
model. The left side should be either 
var.time 
a character vector indicating the name of the time variable of each dimension. The scales of these different time variables should be the same. 
RE 
an indicator of the random effect structure between dimensions. Should
be either "blockdiag" for independent random effects between
dimensions (the internal structure being defined in the 
BM 
in the case where Brownian motions are included in the

breaks 
optional vector specifying the break points in the case where the event time is discretized into time intervals. 
delayed 
logical vector indicating, for each event, if delayed entry should be accounted for. 
B 
optional specification for the initial values for the parameters. 
posfix 
optional vector specifying the indices in vector B of the parameters that should not be estimated. By default, all parameters are estimated. 
maxiter 
optional maximum number of iterations for the Marquardt iterative algorithm. By default, maxiter=XXX. 
eps 
optional thresholds for the convergence criteria. Default is set to 0.0001 for the parameters stability, to 0.0001 for the loglikelihood stability, and to 0.001 for the criterion based on second derivatives. 
nproc 
optional integer indicating the number of processors to be used for parallel computation. Default is set to 1 (i.e., the algorithm runs sequentially). 
verbose 
logical indicating if information about computation should be reported. Default to FALSE. 
file 
optional filename in which to report information about computation (if

pred 
logical indicating if subjectspecific predictions should be computed. Default is set to FALSE. 
istop 
convergence status: 1 if the model converged properly, 2 if the maximum number of iterations was reached without convergence, >2 if an error occurred. 
ni 
number of iterations 
loglik 
loglikelihood of the model 
b 
vector of estimated parameters 
v 
estimated variance matrix of the estimated parameters 
convcrit 
convergence criteria at stop point 
time 
estimation time 
nproc 
number of processors 
bopt 
total vector of estimated and fixed parameters 
nef 
number of fixed effects parameters 
ncontr 
number of contrasts parameters 
nea 
number of random effects variables 
nvc 
number of random effects parameters 
idiag 
indicator of intra dimension correlation between random effects 
ntr 
number of link function parameters 
ntrtot 
number of link function parameters 
ncor 
number of Brownian motion parameters 
nalea 
number of outcomespecific random effects parameters 
ny 
number of longitudinal outcomes 
link 
type of link functions 
nodes 
nodes for the link functions 
nRE 
number of correlation parameters for the random effects between dimensions 
nRM 
number of correlation for the Brownian motions between dimensions 
varD 
time independent covariates in the event model 
vardept 
time dependent covariates in the event model 
nvarD 
number of time independent covariates in the event model 
ndept 
number of time dependent covariates in the event model 
idcause 
indicator of presence of the covariates in the event model 
call 
the model's call 
fix 
indicator of fixed parameters 
chol 
indicator of Cholesky transformation 
Ynames 
name of the longitudinal outcomes 
Xnames 
name of the covariates in the longitudinal part 
ns 
number of subjects 
nbevt 
number of observed events 
nbmes 
mean number of measurement 
entreRetard 
indicator of left truncation 
discretise 
indicator of dicretization 
breaks 
list of break points 
VRE 
variancecovariance matrix of the random effects 
corRE 
correlation matrix of the random effects 
mod 
list of updated 
Cecile ProustLima and Viviane Philipps
ProustLima C, Philipps V, Dartigues JF (2018). A joint model for multiple dynamic processes and clinical endpoints: application to Alzheimer’s disease. https://arxiv.org/abs/1803.10043
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