JMMLSM: Joint Modeling for Continuous outcomes

In JMH: Joint Model of Heterogeneous Repeated Measures and Survival Data

Joint Modeling for Continuous outcomes

Description

Joint modeling of longitudinal continuous data and competing risks

Usage

JMMLSM(
  cdata,
  ydata,
  long.formula,
  surv.formula,
  variance.formula,
  random,
  maxiter = 1000,
  epsilon = 1e-04,
  quadpoint = NULL,
  print.para = FALSE,
  survinitial = TRUE,
  initial.para = NULL,
  method = "adaptive",
  opt = "nlminb",
  initial.optimizer = "BFGS"
)

Arguments

cdata	a survival data frame with competing risks or single failure. Each subject has one data entry.
ydata	a longitudinal data frame in long format.
long.formula	a formula object with the response variable and fixed effects covariates to be included in the longitudinal sub-model.
surv.formula	a formula object with the survival time, event indicator, and the covariates to be included in the survival sub-model.
variance.formula	an one-sided formula object with the fixed effects covariates to model the variance of longitudinal sub-model.
random	a one-sided formula object describing the random effects part of the longitudinal sub-model. For example, fitting a random intercept model takes the form ~ 1|ID. Alternatively. Fitting a random intercept and slope model takes the form ~ x1 + ... + xn|ID.
maxiter	the maximum number of iterations of the EM algorithm that the function will perform. Default is 10000.
epsilon	Tolerance parameter. Default is 0.0001.
quadpoint	the number of Gauss-Hermite quadrature points to be chosen for numerical integration. Default is 15 which produces stable estimates in most dataframes.
print.para	Print detailed information of each iteration. Default is FALSE, i.e., not to print the iteration details.
survinitial	Fit a Cox model to obtain initial values of the parameter estimates. Default is TRUE.
initial.para	a list of initialized parameters for EM iteration. Default is NULL.
method	Method for proceeding numerical integration in the E-step. Default is adaptive.
opt	Optimization method to fit a linear mixed effects model, either nlminb (default) or optim.
initial.optimizer	Method for numerical optimization to be used. Default is BFGS.

Value

Object of class JMMLSM with elements

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.
beta	the vector of fixed effects for the mean trajectory in the mixed effects location and scale model.
tau	the vector of fixed effects for the within-subject variability in the mixed effects location and scale 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 the mean trajectory for type 1 failure.
alpha2	the vector of association parameter(s) for the mean trajectory for type 2 failure. Valid only if CompetingRisk = TRUE.
vee1	the vector of association parameter(s) for the within-subject variability for type 1 failure.
vee2	the vector of association parameter(s) for the within-subject variability 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.
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.
setau	the standard error of tau.
segamma1	the standard error of gamma1.
segamma2	the standard error of gamma2. Valid only if CompetingRisk = TRUE.
sealpha1	the standard error of alpha1.
sealpha2	the standard error of alpha2. Valid only if CompetingRisk = TRUE.
sevee1	the standard error of vee1.
sevee2	the standard error of vee2. Valid only if CompetingRisk = TRUE.
seSig	the vector of standard errors of covariance of random effects.
loglike	the log-likelihood value.
EFuntheta	a list with the expected values of all the functions of random effects.
CompetingRisk	logical value; TRUE if a competing event are accounted for.
quadpoint	the number of Gauss Hermite quadrature points used for numerical integration.
LongitudinalSubmodelmean	the component of the long.formula.
LongitudinalSubmodelvariance	the component of the variance.formula.
SurvivalSubmodel	the component of the surv.formula.
random	the component of the random.
call	the matched call.

Examples

require(JMH)
data(ydata)
data(cdata)
## fit a joint model
## Not run: 
fit <- JMMLSM(cdata = cdata, ydata = ydata, 
              long.formula = Y ~ Z1 + Z2 + Z3 + time,
              surv.formula = Surv(survtime, cmprsk) ~ var1 + var2 + var3,
              variance.formula = ~ Z1 + Z2 + Z3 + time, 
              quadpoint = 6, random = ~ 1|ID, print.para = FALSE)
              
## make dynamic prediction of two subjects
cnewdata <- cdata[cdata$ID %in% c(122, 152), ]
ynewdata <- ydata[ydata$ID %in% c(122, 152), ]
survfit <- survfitJMMLSM(fit, seed = 100, ynewdata = ynewdata, cnewdata = cnewdata, 
                         u = seq(5.2, 7.2, by = 0.5), Last.time = "survtime",
                         obs.time = "time", method = "GH")
oldpar <- par(mfrow = c(2, 2), mar = c(5, 4, 4, 4))
plot(survfit, include.y = TRUE)
par(oldpar)

## End(Not run)

JMH documentation built on June 22, 2024, 7:08 p.m.