Description Usage Arguments Details Value Author(s) References See Also Examples
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.
1 
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 submodels and the model chosen, see

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 
gpt 
the number of quadrature points across which the integration with
respect to the random effects will be performed. Defaults to 
lgpt 
the number of quadrature points which the loglikelihood is
evaluated over following a model fit. This defaults to 
max.it 
the maximum number of iterations of the EM algorithm that the
function will perform. Defaults to 
tol 
the tolerance level before convergence of the algorithm is deemed
to have occurred. Default value is 
print.detail 
This argument determines the level of printing that is
done during the bootstrapping. If 
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.
An object of class data.frame
.
Ruwanthi KolamunnageDona ([email protected]) and Pete Philipson ([email protected])
Wulfsohn MS, Tsiatis AA. A joint model for survival and longitudinal data measured with error. Biometrics. 1997; 53(1): 330339.
Efron B, Tibshirani R. An Introduction to the Bootstrap. 2000; Boca Raton, FL: Chapman & Hall/CRC.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18  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)

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