R/Schneider2017.R

In guido-s/diagmeta: Meta-Analysis of Diagnostic Accuracy Studies with Several Cutpoints

#' Meta-analysis of studies of the diagnostic test accuracy of FENO
#' for diagnosis of asthma
#'
#' @description
#' Meta-analysis of studies of the diagnostic test accuracy of
#' fractional exhaled nitric oxide (FENO) for diagnosis of asthma.
#' 
#' The data were collected for a systematic review by Karrasch et
#' al. (2017) and are published as a supplement (Appendix 1) to
#' Schneider et al. (2017). They have been preprocessed for use in R
#' package \bold{diagmeta}.
#' 
#' @name Schneider2017
#'
#' @docType data
#'
#' @format
#' A data frame with the following columns:
#' \tabular{rl}{
#' \bold{\emph{study_id}}\tab numeric study ID \cr
#' \bold{\emph{author}}\tab first author \cr
#' \bold{\emph{year}}\tab year of publication \cr
#' \bold{\emph{group}}\tab information on subgroup \cr
#' \bold{\emph{cutpoint}}\tab cutpoint (FeNO [ppb]) \cr
#' \bold{\emph{tpos}}\tab number of true positives  \cr
#' \bold{\emph{fneg}}\tab number of false negatives \cr
#' \bold{\emph{fpos}}\tab number of false positives \cr
#' \bold{\emph{tneg}}\tab number of true negatives  \cr
#' }
#' 
#' @source
#' Karrasch S, Linde K, Rücker G, Sommer H, Karsch-Volk M,
#' Kleijnen J, Jörres RA, Schneider A (2017):
#' Accuracy of FENO for diagnosing asthma: a systematic review.
#' \emph{Thorax}, \bold{72}, 109e16
#' 
#' Schneider A, Linde K, Reitsma JB, Steinhauser S, Rücker G (2017):
#' A novel statistical model for analyzing data of a systematic review
#' generates optimal cutoff values for fractional exhaled nitric oxide
#' for asthma diagnosis.
#' \emph{Journal of Clinical Epidemiology},
#' \bold{92}, 69--78
#'
#' @keywords datasets
#'
#' @examples
#' # FENO dataset
#' #
#' data(Schneider2017)
#' 
#' diag1 <- diagmeta(tpos, fpos, tneg, fneg, cutpoint,
#'                   studlab = paste(author, year, group),
#'                   data = Schneider2017,
#'                   log.cutoff = TRUE)
#' 
#' plot(diag1)


NULL