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 non-Gaussian, 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 time-to-event 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 : Proust-Lima 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 two-sided formula defining the event part of the
model. The left side should be either |
data |
data.frame containing the observations and variables |
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 "block-diag" 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 log-likelihood 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 subject-specific 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 |
log-likelihood 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 outcome-specific 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 |
variance-covariance matrix of the random effects |
corRE |
correlation matrix of the random effects |
mod |
list of updated |
Cecile Proust-Lima and Viviane Philipps
Proust-Lima 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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