Nothing
#' Description of summary measures available in R package \bold{meta}
#'
#' @description
#' Description of summary measures available in R package \bold{meta}
#'
#' @details
#' The following summary measures (argument \code{sm}) are recognized
#' in R package \bold{meta}.
#'
#' \subsection{Meta-analysis of binary outcome data (\code{\link{metabin})}}{
#' \tabular{ll}{
#' \bold{Argument} \tab \bold{Summary measure} \cr
#' \code{sm = "OR"} \tab Odds ratio (Fleiss, 1993)\cr
#' \code{sm = "RR"} \tab Risk ratio (Fleiss, 1993) \cr
#' \code{sm = "RD"} \tab Risk difference (Fleiss, 1993) \cr
#' \code{sm = "ASD"} \tab Arcsine difference (Rücker et al., 2009) \cr
#' \code{sm = "DOR"} \tab Diagnostic odds ratio (Moses et al., 1993)
#' \cr
#' \code{sm = "VE"} \tab Vaccine efficacy or vaccine effectiveness
#' }
#'
#' Note, mathematically, odds ratios and diagnostic odds ratios are
#' identical, however, the labels in printouts and figures
#' differ. Furthermore, log risk ratio (logRR) and log vaccine ratio
#' (logVR) are mathematical identical, however, back-transformed
#' results differ as vaccine efficacy or efficacy is defined as
#' \code{VE = 100 * (1 - RR)}.
#'
#' List elements \code{TE}, \code{TE.common}, \code{TE.random}, etc.,
#' contain transformed values, e.g., log odds ratios and log risk
#' ratios. In printouts and plots these values are back transformed if
#' argument \code{backtransf = TRUE} (default), with exception of the
#' arcsine difference where no backtransformation exists.
#'
#' A continuity correction is used for some summary measures in the
#' case of a zero cell count (see \code{\link{metabin}}).
#' }
#'
#' \subsection{Meta-analysis of continuous outcome data (\code{\link{metacont})}}{
#' \tabular{ll}{
#' \bold{Argument} \tab \bold{Summary measure} \cr
#' \code{sm = "MD"} \tab Mean difference \cr
#' \code{sm = "SMD"} \tab Standardised mean difference \cr
#' \code{sm = "ROM"} \tab Ratio of means
#' }
#'
#' Three variants to calculate the standardised mean difference are
#' available (see \code{\link{metacont}}).
#'
#' For the ratio of means, list elements \code{TE}, \code{TE.common},
#' \code{TE.random}, etc., contain the log transformed ratio of
#' means. In printouts and plots these values are back transformed if
#' argument \code{backtransf = TRUE} (default).
#' }
#'
#' \subsection{Meta-analysis of correlations (\code{\link{metacor})}}{
#' \tabular{ll}{
#' \bold{Argument} \tab \bold{Summary measure} \cr
#' \code{sm = "ZCOR"} \tab Fisher's z transformed correlation \cr
#' \code{sm = "COR"} \tab Untransformed correlations
#' }
#'
#' For Fisher's z transformed correlations, list elements \code{TE},
#' \code{TE.common}, \code{TE.random}, etc., contain the transformed
#' correlations. In printouts and plots these values are back
#' transformed if argument \code{backtransf = TRUE} (default).
#' }
#'
#' \subsection{Meta-analysis of incidence rates (\code{\link{metainc})}}{
#' \tabular{ll}{
#' \bold{Argument} \tab \bold{Summary measure} \cr
#' \code{sm = "IRR"} \tab Incidence rate ratio \cr
#' \code{sm = "IRD"} \tab Incidence rate difference \cr
#' \code{sm = "IRSD"} \tab Square root transformed incidence rate
#' difference \cr
#' \code{sm = "VE"} \tab Vaccine efficacy or vaccine effectiveness
#' }
#'
#' Note, log incidence rate ratio (logIRR) and log vaccine ratio
#' (logVR) are mathematical identical, however, back-transformed
#' results differ as vaccine efficacy or effectiveness is defined as
#' \code{VE = 100 * (1 - IRR)}.
#'
#' List elements \code{TE}, \code{TE.common}, \code{TE.random}, etc.,
#' contain the transformed incidence rates. In printouts and plots
#' these values are back transformed if argument \code{backtransf =
#' TRUE} (default).
#' }
#'
#' \subsection{Meta-analysis of single means (\code{\link{metamean})}}{
#' \tabular{ll}{
#' \bold{Argument} \tab \bold{Summary measure} \cr
#' \code{sm = "MRAW"} \tab Raw, i.e. untransformed, means \cr
#' \code{sm = "MLN"} \tab Log transformed means
#' }
#'
#' Calculations are conducted on the log scale if \code{sm =
#' "ROM"}. Accordingly, list elements \code{TE}, \code{TE.common}, and
#' \code{TE.random} contain the logarithm of means. In printouts and
#' plots these values are back transformed if argument
#' \code{backtransf = TRUE}.
#' }
#'
#' \subsection{Meta-analysis of single proportions (\code{\link{metaprop})}}{
#'
#' The following transformations of proportions are
#' implemented to calculate an overall proportion:
#'
#' \tabular{ll}{
#' \bold{Argument} \tab \bold{Summary measure} \cr
#' \code{sm = "PLOGIT"} \tab Logit transformation \cr
#' \code{sm = "PAS"} \tab Arcsine transformation \cr
#' \code{sm = "PFT"} \tab Freeman-Tukey Double arcsine transformation
#' \cr
#' \code{sm = "PLN"} \tab Log transformation \cr
#' \code{sm = "PRAW"} \tab No transformation
#' }
#'
#' List elements \code{TE}, \code{TE.common}, \code{TE.random}, etc.,
#' contain the transformed proportions. In printouts and plots these
#' values are back transformed if argument \code{backtransf = TRUE}
#' (default).
#' }
#'
#' \subsection{Meta-analysis of single rates (\code{\link{metarate})}}{
#'
#' The following transformations of incidence rates are implemented to
#' calculate an overall rate:
#'
#' \tabular{ll}{
#' \bold{Argument} \tab \bold{Summary measure} \cr
#' \code{sm = "IRLN"} \tab Log transformation \cr
#' \code{sm = "IRS"} \tab Square root transformation \cr
#' \code{sm = "IRFT"} \tab Freeman-Tukey Double arcsine transformation
#' \cr
#' \code{sm = "IR"} \tab No transformation
#' }
#'
#' List elements \code{TE}, \code{TE.common}, \code{TE.random}, etc.,
#' contain the transformed incidence rates. In printouts and plots
#' these values are back transformed if argument \code{backtransf =
#' TRUE} (default).
#' }
#'
#' \subsection{Generic inverse variance method (\code{\link{metagen})}}{
#'
#' The following summary measures are recognised in addition to the
#' above mentioned summary measures:
#'
#' \tabular{ll}{
#' \bold{Argument} \tab \bold{Summary measure} \cr
#' \code{sm = "HR"} \tab Hazard ratio \cr
#' \code{sm = "VE"} \tab Vaccine efficacy or vaccine effectiveness
#' }
#'
#' List elements \code{TE}, \code{TE.common}, \code{TE.random}, etc.,
#' contain transformed values, i.e., log hazard ratios and log vaccine
#' ratios. In printouts and plots these values are back transformed if
#' argument \code{backtransf = TRUE} (default).
#' }
#'
#' @name meta-sm
#'
#' @aliases meta-sm
#'
#' @author Guido Schwarzer \email{guido.schwarzer@@uniklinik-freiburg.de}
#'
#' @seealso \code{\link{meta-package}}, \code{\link{meta-object}},
#' \code{\link{print.meta}}, \code{\link{summary.meta}},
#' \code{\link{forest.meta}}
#'
#' @references
#' Borenstein M, Hedges LV, Higgins JP, Rothstein HR (2010):
#' A basic introduction to fixed-effect and random-effects models for
#' meta-analysis.
#' \emph{Research Synthesis Methods},
#' \bold{1}, 97--111
#'
#' Fleiss JL (1993):
#' The statistical basis of meta-analysis.
#' \emph{Statistical Methods in Medical Research},
#' \bold{2}, 121--45
#'
#' Moses LE, Shapiro D, Littenberg B (1993):
#' Combining Independent Studies of a Diagnostic Test into a Summary
#' Roc Curve: Data-Analytic Approaches and Some Additional
#' Considerations.
#' \emph{Statistics in Medicine},
#' \bold{12}, 1293--1316
#'
#' Rücker G, Schwarzer G, Carpenter J, Olkin I (2009):
#' Why add anything to nothing? The arcsine difference as a measure of
#' treatment effect in meta-analysis with zero cells.
#' \emph{Statistics in Medicine},
#' \bold{28}, 721--38
#'
#' Stijnen T, Hamza TH, Ozdemir P (2010):
#' Random effects meta-analysis of event outcome in the framework of
#' the generalized linear mixed model with applications in sparse
#' data.
#' \emph{Statistics in Medicine},
#' \bold{29}, 3046--67
NULL
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.