joineR (version 1.2.8)

jointSE: Standard errors via bootstrap for a joint model fit

Description

This function takes a model fit from a joint model and calculates standard errors, with optional confidence intervals, for the main longitudinal and survival covariates.

Usage

jointSE(fitted, n.boot, gpt, lgpt, max.it, tol, print.detail = FALSE)

Value

An object of class data.frame.

Arguments

fitted

a list containing as components the parameter estimates obtained by fitting a joint model along with the respective formulae for the longitudinal and survival sub-models and the model chosen, see joint for further details.

n.boot

argument specifying the number of bootstrap samples to use in order to obtain the standard error estimates and confidence intervals. Note that at least n.boot = 100 is required in order for the function to return non-zero confidence intervals.

gpt

the number of quadrature points across which the integration with respect to the random effects will be performed. Defaults to gpt = 3 which produces stable estimates in most datasets.

lgpt

the number of quadrature points which the log-likelihood is evaluated over following a model fit. This defaults to lgpt = 10, though lgpt = 3 is often sufficient.

max.it

the maximum number of iterations of the EM algorithm that the function will perform. Defaults to max.it = 200, though more iterations may be necessary for large, complex data.

tol

the tolerance level before convergence of the algorithm is deemed to have occurred. Default value is tol = 0.001.

print.detail

This argument determines the level of printing that is done during the bootstrapping. If TRUE then the parameter estimates from each bootstrap sample are output.

Author

Ruwanthi Kolamunnage-Dona and Pete Philipson

Details

Standard errors and confidence intervals are obtained by repeated fitting of the requisite joint model to bootstrap samples of the original longitudinal and survival data. It is rare that more than 200 bootstrap samples are needed for estimating a standard error. The number of bootstrap samples needed for accurate confidence intervals can be as large as 1000.

References

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

Efron B, Tibshirani R. An Introduction to the Bootstrap. 2000; Boca Raton, FL: Chapman & Hall/CRC.

See Also

lme, coxph, joint, jointdata.

Examples

data(heart.valve)
heart.surv <- UniqueVariables(heart.valve, 
                              var.col = c("fuyrs", "status"), 
                              id.col = "num")
heart.long <- heart.valve[, c("num", "time", "log.lvmi")]
heart.cov <- UniqueVariables(heart.valve, 
                             c("age", "hs", "sex"), 
                             id.col = "num")
heart.valve.jd <- jointdata(longitudinal = heart.long, 
                            baseline = heart.cov, 
                            survival = heart.surv, 
                            id.col = "num", 
                            time.col = "time")
fit <- joint(heart.valve.jd, 
             long.formula = log.lvmi ~ 1 + time + hs, 
             surv.formula = Surv(fuyrs, status) ~ hs, 
             model = "int")
jointSE(fitted = fit, n.boot = 1)