R/sep.R

In joineRmeta: Joint Modelling for Meta-Analytic (Multi-Study) Data

#' Function to determine if estimates from separate models were requested
#'
#' This function assesses during the \code{\link{jointmeta1}} fit whether
#' results from separate longitudinal and time-to-event models were requested,
#' and supplies their results if they were.
#'
#' @param ests estimates from initial longitudinal or survival analyses
#' @param logical a logical value indicating whether or not results from
#'   separate longitudinal and survival analyses were requested.
#'
#' @return A list of results from the separate longitudinal and survival fits.
#'   The components of this list are: \describe{
#'
#'   \item{\code{longests}}{a list containing estimates from the initial
#'   longitudinal fit.  The components of this list are: \describe{
#'
#'   \item{\code{beta1}}{a data frame of the estimates of the fixed effects from
#'   the longitudinal sub-model}
#'
#'   \item{\code{sigma.e}}{the value of the variance of the measurement error
#'   from the longitudinal sub-model}
#'
#'   \item{\code{D}}{the estimate of the covariance matrix for the individual
#'   level random effects.  Individual level random effects are always included
#'   in the joint model}
#'
#'   \item{\code{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 \code{jointmeta1} function call.}
#'
#'   \item{\code{log.like.long}}{the numeric value of the log likelihood for the
#'   initial longitudinal model.}
#'
#'   \item{\code{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}
#'
#'   \item{\code{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}
#'
#'   \item{\code{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 \code{jointmeta1} function call.
#'   }
#'
#'   \item{\code{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
#'   \code{jointmeta1} function call.}
#'
#'   \item{\code{modelfit}}{the initial longitudinal model fit.  The model has
#'   the same specification as the longitudinal sub-model for the joint model,
#'   fitted using the \code{\link[lme4]{lmer}} function from package
#'   \code{lme4}}
#'
#'   }}
#'
#'   \item{\code{survests}}{a list containing estimates from the initial
#'   survival fit.  The components of this list are: \describe{
#'
#'   \item{\code{beta2}}{vector of the estimates of the fixed effects included
#'   in the survival model.}
#'
#'   \item{\code{haz}}{if \code{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 \code{strat = FALSE} then the baseline
#'   is not stratified by study, and this is one numeric vector giving the
#'   common baseline across studies.}
#'
#'   \item{\code{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.}
#'
#'   \item{\code{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. }
#'
#'   \item{\code{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.}
#'
#'   \item{\code{log.like.surv}}{a numeric containing two values, the
#'   log-likelihood with the initial values and the log-likelihood with the
#'   final values, see \code{\link[survival]{coxph.object}}}
#'
#'   \item{\code{modelfit}}{the initial survival model fit.  The model has the
#'   same specification as the survival sub-model for the joint model, fitted
#'   using the \code{\link[survival]{coxph}} function from package
#'   \code{survival}}
#'
#'   }}
#'
#'   }
#'
#'
#' @keywords internal
sep <- function(ests, logical) {
  if (logical == FALSE) {
    ests <- "No separate results requested"
  }
  ests
}