jointmetaSE: Bootstrapping function to obtain standard errors for...
In mesudell/joineRmeta: Joint Modelling for Meta-Analytic (Multi-Study) Data

Description Usage Arguments Details Value Examples

This function takes the results of a jointmeta1 fit and bootstraps it to find the standard errors of the parameter estimates.

1 2	jointmetaSE(fitted, n.boot, gpt, max.it, tol, print.detail = FALSE, overalleffects = NULL)

`fitted`	a `jointmeta1` object
`n.boot`	the number of bootstraps to conduct. Note that confidence intervals will only be calculated if `n.boot` is greater than 100.
`gpt`	the number of quadrature points over which the integration with respect to the random effects will be performed. Will default to `gpt = 3`.
`max.it`	the maximum number of iterations that the EM algorithm will perform for each bootstrap fit. Will default to 350.
`tol`	the tolerance level before convergence of the algorithm is considered 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. Otherwise a progress bar is printed to indicated the proportion of bootstraps currently completed.
`overalleffects`	this argument indicates what if any overall effects will have their standard errors and confidence intervals calculated during the bootstrap procedure. An example of an overall effect would be the combined value of a treatment effect, and a treatment by study membership interaction. The overall treatment effect (the sum of these two values) could be of interest in an investigation. This argument is a list containing two elements, `long` and `surv`. Each of these elements contains a list of vectors, each of which contains the names of the parameters that make up the required overall effects

This function takes the results of a one stage joint model fit to data from multiple studies using the function jointmeta1 and performs n.boot bootstraps to determine the standard errors of the parameter estimates, and their confidence intervals if n.boot > 100.

The parameter overalleffects is designed for use in cases where interaction terms are included in the model specification, for example a model fitted using jointmeta1 which includes both treat and treat:study where treat is a binary treatment indicator variable and study is a study indicator. In this case it may be of interest to calculate the confidence interval for the value of treat + treat:study for a given study. This is done by calculating the value of the expression for each bootstrap, and calculating the standard errors for the expression in the same way as for the other parameters. Any overall effects to be calculated for the longitudinal sub-model are supplied as a list named long in the list overalleffects, with each element of this list containing a vector of the character names of the fixed effects to be summed to form an overall effect. Overall effects from the survival model are specified in a similar way to an element named surv

a list containing three elements:

results: a data frame containing the estimates, standard errors and 95 and any overall effects requested.
covmat: the covariance matrix for the model parameters
bootstraps: a data frame containing the results of each bootstrap

## Not run: 
jointdat<-tojointdata(longitudinal = simdat2$longitudinal,
                      survival = simdat2$survival, id = "id",
                      longoutcome = "Y",
                      timevarying = c("time","ltime"),
                      survtime = "survtime", cens = "cens",
                      time = "time")

onestagefit4 <- jointmeta1(data = jointdat,
                           long.formula = Y ~ 1 + time + treat + study,
                           long.rand.ind = c("int", "time"),
                           long.rand.stud = c("treat"),
                           sharingstrct = "randprop",
                           surv.formula = Surv(survtime, cens) ~ treat,
                           study.name = "study", strat = TRUE)

onestagefit4SE <- jointmetaSE(fitted = onestagefit4, n.boot = 200)
## End(Not run)