vcov.mjoint: Extract an approximate variance-covariance matrix of...

In joineRML: Joint Modelling of Multivariate Longitudinal Data and Time-to-Event Outcomes

Extract an approximate variance-covariance matrix of estimated parameters from an mjoint object

Description

Returns the variance-covariance matrix of the main parameters of a fitted mjoint model object.

Usage

## S3 method for class 'mjoint'
vcov(object, correlation = FALSE, ...)

Arguments

object	an object inheriting from class mjoint for a joint model of time-to-event and multivariate longitudinal data.
correlation	logical: if TRUE returns the correlation matrix, otherwise returns the variance-covariance matrix (default).
...	additional arguments; currently none are used.

Details

This is a generic function that extracts the variance-covariance matrix of parameters from an mjoint model fit. It is based on a profile likelihood, so no estimates are given for the baseline hazard function, which is generally considered a nuisance parameter. It is based on the empirical information matrix (see Lin et al. 2002, and McLachlan and Krishnan 2008 for details), so is only approximate.

Value

A variance-covariance matrix.

Note

This function is not to be confused with getVarCov, which returns the extracted variance-covariance matrix for the random effects distribution.

Author(s)

Graeme L. Hickey ([e-mail])

References

Lin H, McCulloch CE, Mayne ST. Maximum likelihood estimation in the joint analysis of time-to-event and multiple longitudinal variables. Stat Med. 2002; 21: 2369-2382.

McLachlan GJ, Krishnan T. The EM Algorithm and Extensions. Second Edition. Wiley-Interscience; 2008.

See Also

vcov for the generic method description, and cov2cor for details of efficient scaling of a covariance matrix into the corresponding correlation matrix.

Examples

## Not run: 
# Fit a classical univariate joint model with a single longitudinal outcome
# and a single time-to-event outcome

data(heart.valve)
hvd <- heart.valve[!is.na(heart.valve$log.grad) & !is.na(heart.valve$log.lvmi), ]

set.seed(1)
fit1 <- mjoint(formLongFixed = log.lvmi ~ time + age,
    formLongRandom = ~ time | num,
    formSurv = Surv(fuyrs, status) ~ age,
    data = hvd,
    timeVar = "time",
    control = list(nMCscale = 2, burnin = 5)) # controls for illustration only

vcov(fit1)

## End(Not run)

## Not run: 
# Fit a joint model with bivariate longitudinal outcomes

data(heart.valve)
hvd <- heart.valve[!is.na(heart.valve$log.grad) & !is.na(heart.valve$log.lvmi), ]

fit2 <- mjoint(
    formLongFixed = list("grad" = log.grad ~ time + sex + hs,
                         "lvmi" = log.lvmi ~ time + sex),
    formLongRandom = list("grad" = ~ 1 | num,
                          "lvmi" = ~ time | num),
    formSurv = Surv(fuyrs, status) ~ age,
    data = list(hvd, hvd),
    inits = list("gamma" = c(0.11, 1.51, 0.80)),
    timeVar = "time",
    verbose = TRUE)

vcov(fit2)

## End(Not run)

joineRML documentation built on April 4, 2025, 1:23 a.m.