Description Usage Format Value Post model fit statistics Author(s) See Also

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`

.

1 |

An object of class `NULL`

of length 0.

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.

`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*.

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.

Graeme L. Hickey (graemeleehickey@gmail.com)

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