mvjmcs: Joint modeling of multivariate longitudinal and competing risks data

Description

Joint modeling of multivariate longitudinal continuous data and competing risks

Usage

mvjmcs(
  ydata,
  cdata,
  long.formula,
  random = NULL,
  surv.formula,
  maxiter = 10000,
  opt = "nlminb",
  tol = 0.005,
  print.para = TRUE,
  initial.para = NULL,
  cpu.cores = NULL
)

Value

Object of class mvjmcs with elements

beta: the vector of all biomarker-specific fixed effects for the linear mixed effects sub-models.
betaList: the list of biomarker-specific fixed effects for the linear mixed effects sub-model.
gamma1: the vector of fixed effects for type 1 failure for the survival model.
gamma2: the vector of fixed effects for type 2 failure for the survival model. Valid only if CompetingRisk = TRUE.
alpha1: the vector of association parameter(s) for type 1 failure.
alpha2: the vector of association parameter(s) for type 2 failure. Valid only if CompetingRisk = TRUE.
H01: the matrix that collects baseline hazards evaluated at each uncensored event time for type 1 failure. The first column denotes uncensored event times, the second column the number of events, and the third columns the hazards obtained by Breslow estimator.
H02: the matrix that collects baseline hazards evaluated at each uncensored event time for type 2 failure. The data structure is the same as H01. Valid only if CompetingRisk = TRUE.
Sig: the variance-covariance matrix of the random effects.
sigma: the vector of the variance of the biomarker-specific measurement error for the linear mixed effects sub-models.
iter: the total number of iterations until convergence.
convergence: convergence identifier: 1 corresponds to successful convergence, whereas 0 to a problem (i.e., when 0, usually more iterations are required).
vcov: the variance-covariance matrix of all the fixed effects for both models.
FisherInfo: the Empirical Fisher information matrix.
Score: a matrix of the score function for all subjects.
sebeta: the standard error of beta.
segamma1: the standard error of gamma1.
segamma2: the standard error of gamma2. Valid only if CompetingRisk = TRUE.
sealpha1: the standard error of nu1.
sealpha2: the standard error of nu2. Valid only if CompetingRisk = TRUE.
seSig: the vector of standard errors of covariance of random effects.
sesigma: the standard error of variance of biomarker-specific measurement error for the linear mixed effects sub-models.
pos.mode: the posterior mode of the conditional distribution of random effects.
pos.cov: the posterior covariance of the conditional distribution of random effects.
CompetingRisk: logical value; TRUE if a competing event are accounted for.
ydata: the input longitudinal dataset for fitting a joint model. It has been re-ordered in accordance with descending observation times in cdata.
cdata: the input survival dataset for fitting a joint model. It has been re-ordered in accordance with descending observation times.
PropEventType: a frequency table of number of events.
LongitudinalSubmodel: the component of the long.formula.
SurvivalSubmodel: the component of the surv.formula.
random: the component of the random.
call: the matched call.
id: the grouping vector for the longitudinal outcome.
opt: the numerical optimizer for obtaining the initial guess of the parameters in the linear mixed effects sub-models.
runtime: the total computation time.

Arguments

ydata: A longitudinal data frame in long format.
cdata: A survival data frame with competing risks or single failure. Each subject has one data entry.
long.formula: A list of formula objects specifying fixed effects for each longitudinal outcome.
random: A formula or list of formulas describing random effects structures (e.g., ~ 1|ID).
surv.formula: A formula for the survival sub-model, including survival time and event indicator.
maxiter: Maximum number of EM iterations. Default is 10000.
opt: Optimization method for mixed model. Default is "nlminb".
tol: Convergence tolerance for EM algorithm. Default is 0.0001.
print.para: Logical; if TRUE, prints parameter values at each iteration.
initial.para: Optional list of initialized parameters. Default is NULL.
cpu.cores: Number of CPU cores for parallel computation. Default is 1.

Details

Function fits a joint model for multiple longitudinal outcomes and competing risks using a fast EM algorithm.

Examples

Run this code



  require(FastJM)
  require(survival)
  require(future)
  require(future.apply)
  
  data(mvcdata)
  data(mvydata)

  # \donttest{
  # Fit joint model with two biomarkers
  fit <- mvjmcs(ydata = mvydata, cdata = mvcdata, 
                long.formula = list(Y1 ~ X11 + X12 + time, 
                                    Y2 ~ X11 + X12 + time),
                random = list(~ time | ID,
                              ~ 1 | ID),
                surv.formula = Surv(survtime, cmprsk) ~ X21 + X22, maxiter = 1000, opt = "optim", 
                tol = 1e-3, print.para = FALSE)
  fit
  
  # Extract the parameter estimates of longitudinal sub-model fixed effects
  fixef(fit, process = "Longitudinal")
  
  # Extract the parameter estimates of survival sub-model fixed effects
  fixef(fit, process = "Event")
  
  # Obtain the random effects estimates for first 6 subjects 
  head(ranef(fit))
  # }

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