JMMLSM {JMH}

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)