mvjmcs {FastJM}R Documentation

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
)

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.

Details

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

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.

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.

Examples



  require(FastJM)
  require(survival)
  
  data(mvcdata)
  data(mvydata)

  
  # 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))
  
  

[Package FastJM version 1.5.1 Index]