# JMMLSM: Joint Modeling for Continuous outcomes In JMH: Joint Model of Heterogeneous Repeated Measures and Survival Data

 JMMLSM R Documentation

## 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,
print.para = FALSE,
survinitial = TRUE,
initial.para = NULL,
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.