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joineRML (version 0.4.6)

summary.mjoint: Summary of an mjoint object

Description

This function provides a summary of an mjoint object.

Usage

# S3 method for mjoint
summary(object, bootSE = NULL, ...)

Value

A list containing the coefficient matrices for the longitudinal and time-to-event sub-models; variance-covariance matrix for the random effects; residual error variances; log-likelihood of joint model; AIC and BIC statistics; and model fit objects.

Arguments

object

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

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 (graemeleehickey@gmail.com)

References

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

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.

See Also

mjoint, mjoint.object, and summary for the generic method description.

Examples

Run this code
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)
summary(fit2)
}

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