sep: Function to determine if estimates from separate models were...
In joineRmeta: Joint Modelling for Meta-Analytic (Multi-Study) Data

This function assesses during the jointmeta1 fit whether results from separate longitudinal and time-to-event models were requested, and supplies their results if they were.

1	sep(ests, logical)

`ests`	estimates from initial longitudinal or survival analyses
`logical`	a logical value indicating whether or not results from separate longitudinal and survival analyses were requested.

A list of results from the separate longitudinal and survival fits. The components of this list are:

longests

a list containing estimates from the initial longitudinal fit. The components of this list are:

beta1: a data frame of the estimates of the fixed effects from the longitudinal sub-model
sigma.e: the value of the variance of the measurement error from the longitudinal sub-model
D: the estimate of the covariance matrix for the individual level random effects. Individual level random effects are always included in the joint model
A: the estimate of the covariance matrix for the study level random effects. This is only present if study level random effects are specified in the jointmeta1 function call.
log.like.long: the numeric value of the log likelihood for the initial longitudinal model.
randstart.ind: a list of the conditional modes of the individual level random effects in each study given the data and the estimates of the separate longitudinal model parameters
randstart.ind.cov: a list of the conditional covariance matrices for each individual for the individual level random effects given the data and the estimates of the separate longitudinal model parameters
randstart.stud: a data frame containing the conditional modes of the study level random effects given the data and the estimates of the separate longitudinal model parameters. This is only present if study level random effects were specified in the jointmeta1 function call.
randstart.stud.cov: a list of conditional covariance matrices for each study for the study level random effects given the data and the estimates of the separate longitudinal model parameters. This is only present if study level random effects were specified in the jointmeta1 function call.
modelfit: the initial longitudinal model fit. The model has the same specification as the longitudinal sub-model for the joint model, fitted using the lmer function from package lme4

survests

a list containing estimates from the initial survival fit. The components of this list are:

beta2: vector of the estimates of the fixed effects included in the survival model.
haz: if strat = TRUE then this is a list of numeric vectors of length equal to the number of studies in the dataset, giving the study specific baseline hazard. If strat = FALSE then the baseline is not stratified by study, and this is one numeric vector giving the common baseline across studies.
rs: a counter to indicate the last how many unique event times had occured by the individual's survival time - this is for use during further calculation in the joint model EM algorithm. If a stratified baseline this is a list of numerical vectors, whereas if the baseline is not stratified this is a single numeric vector.
sf: the unique event times observed in the dataset. If a stratified baseline this is a list of numerical vectors, whereas if the baseline is not stratified this is a single numeric vector.
nev: a counter of the number of events that occur at each event time.If a stratified baseline this is a list of numerical vectors, whereas if the baseline is not stratified this is a single numeric vector.
log.like.surv: a numeric containing two values, the log-likelihood with the initial values and the log-likelihood with the final values, see coxph.object
modelfit: the initial survival model fit. The model has the same specification as the survival sub-model for the joint model, fitted using the coxph function from package survival