jlctree: Fits Joint Latent Class Tree (JLCT) model.

In jlctree: Joint Latent Class Trees for Joint Modeling of Time-to-Event and Longitudinal Data

Description

Fits Joint Latent Class Tree model. This is the main function that is normally called by the user. See jlctree-package for more details.

Usage

jlctree(survival, classmb, fixed, random, subject, data, parms = list(),
  control = list())

Arguments

survival	a two-sided formula object; required. The left side of the formula corresponds to a Surv() object of type “counting” for left-truncated right-censored (LTRC) data, or of type “right” for right-censored data. The right side of the formula specifies the names of covariates to include in the survival model, excluding the longitudinal outcome.
classmb	one-sided formula describing the covariates in the class-membership tree construction; required. Covariates used for tree construction are separated by + on the right of ~.
fixed	two-sided linear formula object for the fixed-effects in the linear mixed-effects model for longitudinal outcomes; required. The longitudinal outcome is on the left of ~ and the covariates are separated by + on the right of ~.
random	one-sided formula for the node-specific random effects in the linear mixed-effects model for longitudinal outcomes; optional. If missing, there are no node-specific random effects in the fitted linear mixed-effects model. Covariates with a random effect are separated by + on the right of ~.
subject	name of the covariate representing the subject identifier; optional. If missing, there are no subject-specific random intercepts in the fitted linear mixed-effects model for longitudinal outcomes.
data	the dataset; required.
parms	parameter list of Joint Latent Class Tree model parameters. See also jlctree.control.
control	rpart control parameters. See also rpart.control.

Value

A list with components:

tree	an rpart object, containing the constructed Joint Latent Class tree.
control	the rpart.control parameters.
parms	the jlctree.control parameters.
lmmmodel	an lme4 object, containing the linear mixed-effects effects model with fixed effects, node-specific random effects (if valid), and subject-specific random intercepts (if valid). Returned when fity is TRUE.
coxphmodel_diffh_diffs	a coxph object, containing a Cox PH model with different hazards and different slopes across terminal nodes. Returned when fits is TRUE.
coxphmodel_diffh	a coxph object, containing a Cox PH model with different hazards but same slopes across terminal nodes. Returned when fits is TRUE.
coxphmodel_diffs	a coxph object, containing a Cox PH model with same hazards but different slopes across terminal nodes. Returned when fits is TRUE.

See Also

jlctree-package, jlctree.control, rpart.control

Examples

 # Time-to-event in LTRC format:
 data(data_timevar)
 tree <- jlctree(survival=Surv(time_L, time_Y, delta)~X3+X4+X5,
                 classmb=~X1+X2, fixed=y~X1+X2+X3+X4+X5, random=~1,
                 subject='ID',data=subset(data_timevar, ID<=30),
                 parms=list(maxng=4, fity=FALSE, fits=FALSE))

 # Time-to-event in right-censored format:
 data(data_timeinv)
 tree <- jlctree(survival=Surv(time_Y, delta)~X3+X4+X5,
                 classmb=~X1+X2, fixed=y~X1+X2+X3+X4+X5, random=~1,
                 subject='ID', data=subset(data_timeinv, ID<=30),
                 parms=list(maxng=4, fity=FALSE, fits=FALSE))

jlctree documentation built on April 15, 2021, 5:06 p.m.