mjoint.object {joineRML}

Fitted mjoint object

Description

An object returned by the mjoint function, inheriting from class mjoint and representing a fitted joint model for multivariate longitudinal and time-to-event data. Objects of this class have methods for the generic functions coef, logLik, plot, print, ranef, fixef, summary, AIC, getVarCov, vcov, confint, sigma, fitted, residuals, and formula.

Usage

mjoint.object

Format

An object of class NULL of length 0.

Value

A list with the following components.

coefficients

a list with the estimated coefficients. The components of this list are:

beta

the vector of fixed effects for the linear mixed effects sub-model.

D

the variance-covariance matrix of the random effects.

sigma2

the measurement error standard deviations for the linear mixed effects sub-model.

haz

the estimated baseline hazard values for each unique failure time. Note that this is the centered hazard, equivalent to that returned by coxph.detail.

gamma

the vector of baseline covariates for the survival model and the latent association coefficient parameter estimates.

history

a matrix with parameter estimates at each iteration of the MCEM algorithm.

nMC.hx

a vector with the number of Monte Carlo samples for each MCEM algorithm iteration.

formLongFixed

a list of formulae for the fixed effects component of each longitudinal outcome.

formLongRandom

a list of formulae for the fixed effects component of each longitudinal outcome. The length of the list will be equal to formLongFixed.

formSurv

a formula specifying the proportional hazards regression model (not including the latent association structure).

data

a list of data.frames for each longitudinal outcome.

survData

a data.frame of the time-to-event dataset.

timeVar

a character string vector of length K denoting the column name(s) for time in data.

id

a character string denoting the column name for subject IDs in data and survData.

dims

a list giving the dimensions of model parameters with components:

p

a vector of the number of fixed effects for each longitudinal outcome.

r

a vector of the number of random effects for each longitudinal outcome.

K

an integer of the number of different longitudinal outcome types.

q

an integer of the number of baseline covariates in the time-to-event sub-model.

n

an integer of the total number of subjects in the study.

nk

a vector of the number of measurements for each longitudinal outcome.

sfit

an object of class coxph for the separate time-to-event model fit. See coxph for details.

lfit

a list of objects each of class lme from fitting separate linear mixed effects models; one per each longitudinal outcome type. See lme for details.

log.lik0

the combined log-likelihood from separate sub-model fits.

log.lik

the log-likelihood from the joint model fit.

ll.hx

a vector of the log-likelihood values for each MCEM algorithm interaction.

control

a list of control parameters used in the estimation of the joint model. See mjoint for details.

finalnMC

the final number of Monte Carlo samples required prior to convergence.

call

the matched call.

inits

the initial values passed as an argument in the mjoint function.

inits.long

the computed initial values from fitting a multivariate longitudinal model.

inits.surv

the computed initial values from fitting a Cox proportional hazards model with time-dependent covariates calculated from the fitted multivariate LME model.

conv

logical: did the MCEM algorithm converge within the specified maximum number of iterations?

comp.time

a vector of length 2 with each element an object of class difftime that reports the total time taken for model fitting (including all stages) and the time spent in the EM algorithm.

Post model fit statistics

If pfs=TRUE, indicating that post-fit statistics are to be returned, then the output also includes the following objects.

vcov

the variance-covariance matrix of model parameters, as approximated by the empirical information matrix, is reported. See mjoint for details.

SE.approx

the square-root of the diagonal of vcov is returned, which are estimates of the standard errors for the parameters.

Eb

a matrix with the estimated random effects values for each subject.

Vb

an array with the estimated variance-covariance matrices for the random effects values for each subject.

dmats

a list of length 3 containing the design matrices, data frames, and vectors used in the MCEM algorithm. These are required for prediction and to calculate the residuals and . The 3 items in the list are l (longitudinal data), t (time-to-event data), and z (design matrices expanded over unique failure times). These are not intended to be extracted by the user.

Author(s)

Graeme L. Hickey ([e-mail])

See Also

mjoint.