JointMult: Estimation of a joint model for multivariate latent processes...

In VivianePhilipps/multLPM: Joint model for multivariate latent processes and clinical endpoints using an exact likelihood inference method

Description

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

Usage

JointMult(Y, D, data, var.time, RE = "block-diag", BM = "diag", 
    B, posfix, maxiter = 0, nproc = 1, verbose = FALSE, 
    file = "", pred = FALSE, breaks = NULL, delayed = TRUE,  
    eps = c(1e-04, 1e-04, 0.001)) 

Arguments

Y	a list of multlcmm objects. Each multlcmm object defines the outcomes and longitudinal structure of one dimension of the model. The number of outcomes, covariates or random effects can differ between dimensions.
D	a list of two-sided formula defining the event part of the model. The left side should be either longDiag(time0, time, indicator) with time0 the variable indicating entry time (which will only be used for delayed entry), time the repeated measurement times, indicator the repeated event indicator at each time or survBreak(T0, T, Event) with T0 the entry time, T the event time and Event the event indicator.
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 multlcmm object) or "full" for correlated random effects between dimensions.
BM	in the case where Brownian motions are included in the multlcmm objects, an indicator of the correlation of the Brownian motions. Should be "diag" for independence, or "full" for correlated Brownian motions between dimensions.
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 verbose=TRUE).
pred	logical indicating if subject-specific predictions should be computed. Default is set to FALSE.

Value

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 multlcmm objects

Author(s)

Cecile Proust-Lima and Viviane Philipps

References

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

VivianePhilipps/multLPM documentation built on March 13, 2021, 6:35 a.m.