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#' multinma: A Package for Network Meta-Analysis of Individual and Aggregate
#' Data in Stan
#'
#' @description
#' \if{html}{\figure{logo.svg}{options: width="120" style="float: right;" alt="multinma logo"}}
#' An R package for performing network meta-analysis and network meta-regression
#' with aggregate data, individual patient data, or mixtures of both.
#'
#' @details Network meta-analysis (NMA) combines (aggregate) data from multiple
#' studies on multiple treatments in order to produce consistent estimates of
#' relative treatment effects between each pair of treatments in the network
#' \insertCite{TSD2}{multinma}.
#'
#' Network meta-regression (NMR) extends NMA to include covariates, allowing
#' adjustment for differences in effect-modifying variables between studies
#' \insertCite{TSD3}{multinma}. NMR is typically performed using aggregate
#' data (AgD), which lacks power and is prone to ecological bias. NMR with
#' individual patient data (IPD) is the gold standard, if data are available.
#'
#' Multilevel network meta-regression (ML-NMR) allows IPD and AgD to be
#' incorporated together in a network meta-regression
#' \insertCite{methods_paper,Phillippo_thesis}{multinma}. As in IPD NMR, an
#' individual-level regression model is defined. AgD studies are then fitted
#' by integrating the individual-level model over the respective covariate
#' distributions. This correctly links the two levels of the model (instead of
#' "plugging in" mean covariate values), avoiding aggregation bias.
#' Population-adjusted treatment effects \insertCite{TSD18}{multinma} can be
#' produced for any study population in the network, or for an external target
#' population.
#'
#' Models are estimated in a Bayesian framework using Stan
#' \insertCite{Carpenter2017}{multinma}. Quasi-Monte Carlo numerical
#' integration based on Sobol' sequences is used for the integration in ML-NMR
#' models, with a Gaussian copula to account for correlations between
#' covariates \insertCite{methods_paper,Phillippo_thesis}{multinma}.
#'
#' @section Getting Started:
#' A good place to start is with the package vignettes which walk through
#' example analyses, see `vignette("vignette_overview")` for an overview.
#' The series of NICE Technical Support Documents on evidence synthesis gives
#' a detailed introduction to network meta-analysis:
#'
#' \insertRef{TSD_evsynth}{multinma}
#'
#' Multilevel network meta-regression is set out in the following methods paper:
#'
#' \insertRef{methods_paper}{multinma}
#'
#' @name multinma-package
#' @aliases multinma
#' @useDynLib multinma, .registration = TRUE
#' @import methods
#' @import Rcpp
#' @importFrom dplyr %>%
#' @importFrom rlang abort warn inform enquo .data :=
#' @importFrom rstan sampling
#' @importFrom Rdpack reprompt
#' @importFrom graphics plot pairs
#' @importFrom grDevices nclass.Sturges
#' @importFrom stats complete.cases sd median quantile model.frame model.matrix
#' model.offset terms optim pbinom dbinom qbinom as.formula update.formula
#' weighted.mean runif dunif plogis pnorm qlogis qnorm uniroot update var
#' setNames
#' @importFrom utils packageVersion head
#' @importFrom RcppParallel CxxFlags
#' @importFrom rstantools rstan_config
#'
#' @references
#' \insertAllCited{}
#'
"_PACKAGE"
# Stop R CMD check thinking . used in pipes is an undeclared global variable
if (getRversion() >= "2.15.1") utils::globalVariables(c("."))
# Reexport survival::Surv()
#' @importFrom survival Surv
#' @export
survival::Surv
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