joineRML (version 0.4.6)

confint.mjoint: Confidence intervals for model parameters of an mjoint object

Description

This function computes confidence intervals for one or more parameters in a fitted mjoint object.

Usage

# S3 method for mjoint
confint(
  object,
  parm = c("Both", "Longitudinal", "Event"),
  level = 0.95,
  bootSE = NULL,
  ...
)

Value

A matrix containing the confidence intervals for either the longitudinal, time-to-event, or both sub-models.

Arguments

object

an object inheriting from class mjoint for a joint model of time-to-event and multivariate longitudinal data.

parm

a character string specifying which sub-model parameter confidence intervals should be returned for. Can be specified as parm='Longitudinal' (multivariate longitudinal sub-model), parm='Event' (time-to-event sub-model), or parm='both' (default).

level

the confidence level required. Default is level=0.95 for a 95% confidence interval.

bootSE

an object inheriting from class bootSE for the corresponding model. If bootSE=NULL, the function will attempt to utilize approximate standard error estimates (if available) calculated from the empirical information matrix.

...

additional arguments; currently none are used.

Author

Graeme L. Hickey ()

References

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

Henderson R, Diggle PJ, Dobson A. Joint modelling of longitudinal measurements and event time data. Biostatistics. 2000; 1(4): 465-480.

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.

Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics. 1997; 53(1): 330-339.

See Also

mjoint, bootSE, and confint for the generic method description.

Examples

# 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), ]

gamma <- c(0.1059417, 1.0843359)
sigma2 <- 0.03725999
beta <- c(4.9988669999, -0.0093527634, 0.0004317697)
D <- matrix(c(0.128219108, -0.006665505, -0.006665505, 0.002468688),
            nrow = 2, byrow = TRUE)

set.seed(1)
fit1 <- mjoint(formLongFixed = log.lvmi ~ time + age,
    formLongRandom = ~ time | num,
    formSurv = Surv(fuyrs, status) ~ age,
    data = hvd,
    timeVar = "time",
    inits = list(gamma = gamma, sigma2 = sigma2, beta = beta, D = D),
    control = list(nMCscale = 2, burnin = 5)) # controls for illustration only

confint(fit1, parm = "Longitudinal")

if (FALSE) {
# 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)
confint(fit2)
}