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R/metamerge.R
In meta: General Package for Meta-Analysis
Defines functions
metamerge
Documented in
metamerge
#' Merge results of two meta-analyses on the same data set
#'
#' @description
#' This function can be used to merge results of two meta-analyses
#' into a single meta-analysis object if they are based on the same
#' data set. This is, for example, useful to produce a forest plot of
#' a random-effects meta-analysis with different estimates of the
#' between-study variance \eqn{\tau^2}.
#'
#' @param meta1 First meta-analysis object (see Details).
#' @param meta2 Second meta-analysis object (see Details).
#' @param common1 A logical indicating whether results of common
#' effect model should be considered for first meta-analysis.
#' @param random1 A logical indicating whether results of random
#' effects model should be considered for first meta-analysis.
#' @param prediction1 A logical indicating whether prediction interval
#' should be considered for first meta-analysis.
#' @param common2 A logical indicating whether results of common
#' effect model should be considered for second meta-analysis.
#' @param random2 A logical indicating whether results of random
#' effects model should be considered for second meta-analysis.
#' @param prediction2 A logical indicating whether prediction interval
#' should be considered for second meta-analysis.
#' @param text.pooled1 A character string used in printouts and forest
#' plot to label the results from the first meta-analysis.
#' @param text.pooled2 A character string used in printouts and forest
#' plot to label the results from the second meta-analysis.
#' @param text.w.pooled1 A character string used to label weights of
#' the first meta-analysis; can be of same length as the number of
#' pooled estimates requested in argument \code{pooled1}.
#' @param text.w.pooled2 A character string used to label weights of
#' the second meta-analysis; can be of same length as the number of
#' pooled estimates requested in argument \code{pooled1}.
#' @param label1 Default setting for arguments 'hetlabel1' and
#' 'taulabel1'.
#' @param label2 Default setting for arguments 'hetlabel2' and
#' 'taulabel2'.
#' @param hetlabel1 A character string used to label heterogeneity
#' statistics of the first meta-analysis.
#' @param hetlabel2 A character string used to label heterogeneity
#' statistics of the second meta-analysis.
#' @param taulabel1 A character string used to label estimate of
#' between-study variance of the first meta-analysis.
#' @param taulabel2 A character string used to label estimate of
#' between-study variance of the second meta-analysis.
#' @param text.common1 A character string used in printouts and forest
#' plot to label results for common effect models from the first
#' meta-analysis.
#' @param text.common2 A character string used in printouts and forest
#' plot to label results for common effect models from the second
#' meta-analysis.
#' @param text.random1 A character string used in printouts and forest
#' plot to label results for random effects models from the first
#' meta-analysis.
#' @param text.random2 A character string used in printouts and forest
#' plot to label results for random effects models from the second
#' meta-analysis.
#' @param text.predict1 A character string used in printouts and
#' forest plot to label prediction interval from the first
#' meta-analysis.
#' @param text.predict2 A character string used in printouts and
#' forest plot to label prediction interval from the second
#' meta-analysis.
#' @param text.w.common1 A character string used to label common
#' effect weights of the first meta-analysis; can be of same length
#' as the number of common effect estimates.
#' @param text.w.common2 A character string used to label common
#' effect weights of the second meta-analysis; can be of same length
#' as the number of common effect estimates.
#' @param text.w.random1 A character string used to label random
#' effects weights of the first meta-analysis; can be of same length
#' as the number of random effects estimates.
#' @param text.w.random2 A character string used to label random
#' effects weights of the second meta-analysis; can be of same
#' length as the number of random effects estimates.
#' @param keep A logical indicating whether to keep additional
#' information from second meta-analysis.
#' @param keep.Q A logical indicating whether heterogeneity statistic
#' Q of second meta-analysis should be kept or ignored.
#' @param keep.I2 A logical indicating whether heterogeneity statistic
#' I2 of second meta-analysis should be kept or ignored.
#' @param keep.w A logical indicating whether weights of the second
#' meta-analysis should be kept or ignored.
#' @param common A logical indicating whether results of common effect
#' meta-analyses should be reported.
#' @param random A logical indicating whether results of random
#' effects meta-analyses should be reported.
#' @param overall A logical indicating whether overall summaries
#' should be reported.
#' @param overall.hetstat A logical value indicating whether to print
#' heterogeneity measures for overall treatment comparisons.
#' @param prediction A logical indicating whether prediction intervals
#' should be reported.
#' @param backtransf A logical indicating whether results should be
#' back transformed in printouts and plots. If
#' \code{backtransf=TRUE} (default), results for \code{sm="OR"} are
#' printed as odds ratios rather than log odds ratios, for example.
#' @param warn.deprecated A logical indicating whether warnings should
#' be printed if deprecated arguments are used.
#' @param pooled1 Deprecated argument (replaced by 'common1',
#' 'random1', 'prediction1'). A character string indicating whether
#' results of common effect or random effects model should be
#' considered for first meta-analysis. Either \code{"both"},
#' \code{"common"} or \code{"random"}, can be abbreviated.
#' @param pooled2 Deprecated argument (replaced by 'common2',
#' 'random2', 'prediction2'). A character string indicating whether
#' results of common effect or random effects model should be
#' considered for second meta-analysis. Either \code{"both"},
#' \code{"common"} or \code{"random"}, can be abbreviated.
#'
#' @details
#' In R package \bold{meta}, objects of class \code{"meta"} contain
#' results of both common effect and random effects
#' meta-analyses. This function enables the user to merge the results
#' of two meta-analysis object if they are based on the same data set.
#'
#' Applications of this function include printing and plotting results
#' of the common effect or random effects meta-analysis and the
#' \itemize{
#' \item trim-and-fill method (\code{\link{trimfill}}),
#' \item limit meta-analyis (\code{\link[metasens]{limitmeta}} from R
#' package \bold{metasens}),
#' \item Copas selection model (\code{\link[metasens]{copas}} from R
#' package \bold{metasens}),
#' \item robust variance meta-analysis model
#' (\code{\link[robumeta]{robu}} from R package \bold{robumeta}).
#' }
#'
#' The first argument (\code{meta1}) must be an object created by a
#' meta-analysis function (see \code{\link{meta-object}}). If an
#' object created with \code{\link[metasens]{limitmeta}} or
#' \code{\link[metasens]{copas}} is provided as the first argument,
#' this object will be returned, i.e., argument \code{meta2} will be
#' ignored.
#'
#' The second meta-analysis could be an object created by a
#' meta-analysis function or with \code{\link{trimfill}},
#' \code{\link[metasens]{limitmeta}}, \code{\link[metasens]{copas}},
#' or \code{\link[robumeta]{robu}}.
#'
#' The created meta-analysis object only contains the study results,
#' i.e., estimated effects and confidence intervals, from the first
#' meta-analysis which are shown in printouts and forest plots. This
#' only makes a difference for meta-analysis methods where individual
#' study results differ, e.g., Mantel-Haenszel and Peto method for
#' binary outcomes (see \code{\link{metabin}}).
#'
#' R function \code{\link{metaadd}} can be used to add pooled results
#' from any (external) meta-analysis.
#'
#' R function \code{\link{metabind}} can be used to print and plot the
#' results of several meta-analyses without the restriction that the
#' same data set has to be used. Accordingly, individual study results
#' are ignored.
#'
#' @return
#' An object of class \code{"meta"} and \code{"metamerge"} with
#' corresponding generic functions (see \code{\link{meta-object}}).
#'
#' The following list elements have a different meaning:
#' \item{TE, seTE, studlab}{Treatment estimate, standard error, and
#' study labels (first meta-analyis).}
#' \item{lower, upper}{Lower and upper confidence interval limits for
#' individual studies (first meta-analysis).}
#' \item{statistic, pval}{Statistic and p-value for test of treatment
#' effect for individual studies (first meta-analysis.}
#' \item{w.common}{Vector or matrix with common effect weights.}
#' \item{w.random}{Vector or matrix with random effects weights.}
#' \item{k}{Vector with number of estimates (same length as number of
#' common effect and random effects estimates).}
#' \item{k.study}{Vector with number of studies (same length as
#' number of common effect and random effects estimates).}
#' \item{k.all}{Vector with total number of studies (same length as
#' number of common effect and random effects estimates).}
#' \item{k.TE}{Vector with number of studies with estimable effects
#' (same length as number of common effect and random effects
#' estimates).}
#' \item{k.MH}{Vector with number of studies combined with
#' Mantel-Haenszel method (same length as number of common effect
#' and random effects estimates).}
#' \item{TE.common}{Vector with common effect estimates.}
#' \item{seTE.common}{Vector with standard errors of common effect
#' estimates.}
#' \item{lower.common}{Vector with lower confidence limits (common
#' effect model).}
#' \item{upper.common}{Vector with upper confidence limits (common
#' effect model).}
#' \item{statistic.common}{Vector with test statistics for test of
#' overall effect (common effect model).}
#' \item{pval.common}{Vector with p-value of test for overall effect
#' (common effect model).}
#' \item{TE.random}{Vector with random effects estimates.}
#' \item{seTE.random}{Vector with standard errors of random effects
#' estimates.}
#' \item{lower.random}{Vector with lower confidence limits (random
#' effects model).}
#' \item{upper.random}{Vector with upper confidence limits (random
#' effects model).}
#' \item{statistic.random}{Vector with test statistics for test of
#' overall effect (random effects model).}
#' \item{pval.random}{Vector with p-value of test for overall effect
#' (random effects model).}
#'
#' Furthermore, meta-analysis results of common effect or random
#' effects model are taken from first meta-analysis if only random
#' effects or common effects models are selected from both
#' meta-analyses (arguments \code{pooled1} and \code{pooled2}).
#'
#' @author Guido Schwarzer \email{guido.schwarzer@@uniklinik-freiburg.de}
#'
#' @seealso \code{\link{metagen}}, \code{\link{metabind}},
#' \code{\link{metaadd}}
#'
#' @examples
#' # Print results with more significant digits
#' oldset <- settings.meta(digits = 6, digits.stat = 4, digits.pval = 6,
#' digits.Q = 6, digits.I2 = 4, digits.H = 4)
#' oldopts <- options(width = 120)
#'
#' data(Fleiss1993bin)
#'
#' # Mantel-Haenszel method
#' m1 <- metabin(d.asp, n.asp, d.plac, n.plac, data = Fleiss1993bin,
#' studlab = paste(study, year), sm = "OR",
#' text.common = "Mantel-Haenszel method", text.w.common = "MH",
#' text.random = "Random effects model (IV)", text.w.random = "RE-IV")
#'
#' # Peto method
#' m2 <- update(m1, method = "Peto",
#' text.common = "Peto method", text.w.common = "Peto",
#' text.random = "Random effects model (Peto)", text.w.random = "RE-Peto")
#'
#' # Inverse variance method
#' m3 <- update(m2, method = "Inverse", random = FALSE,
#' text.common = "Inverse-variance method", text.w.common = "IV")
#'
#' # Merge results from MH and Peto method
#' # - show individual results for MH method (as this is the first meta-analysis)
#' # - keep all additional information from Peto meta-analysis (i.e.,
#' # weights, Q statistic and I2 statistic)
#' m12 <- metamerge(m1, m2,
#' taulabel1 = "REML-IV", taulabel2 = "REML-Peto",
#' hetlabel1 = "MH/IV", hetlabel2 = "Peto",
#' keep = TRUE)
#' m12
#'
#' # Add results from inverse variance method
#' # - keep weights from IV meta-analysis
#' # - Q and I2 statistic are identical for sm = "MH" and sm = "Inverse"
#' # as inverse variance method is used for sm = "MH" under random
#' # effects model
#' m123 <- metamerge(m12, m3, keep.w = TRUE)
#' summary(m123)
#' \dontrun{
#' forest(m123, digits = 6)
#'
#' # Merge results (show individual results for Peto method)
#' m21 <- metamerge(m2, m1,
#' taulabel1 = "REML-Peto", taulabel2 = "REML-IV",
#' hetlabel1 = "Peto", hetlabel2 = "MH/IV",
#' keep = TRUE)
#' m213 <- metamerge(m21, m3, keep.w = TRUE)
#' summary(m213)
#'
#' # Random effects method using ML estimator for between-study variance tau2
#' m4 <- update(m1, common = FALSE, method.tau = "ML",
#' text.random = "Random effects model (ML)", text.w.random = "RE-ML")
#'
#' # Use DerSimonian-Laird estimator for tau2
#' m5 <- update(m4, method.tau = "DL",
#' text.random = "Random effects model (DL)", text.w.random = "RE-DL")
#'
#' # Use Paule-Mandel estimator for tau2
#' m6 <- update(m4, method.tau = "PM",
#' text.random = "Random effects model (PM)", text.w.random = "RE-PM")
#'
#' # Merge random effects results for ML and DL estimators
#' # - keep weights for DL estimator (which are different from ML)
#' m45 <- metamerge(m4, m5, taulabel1 = "ML", taulabel2 = "DL",
#' keep.w = TRUE)
#' summary(m45)
#'
#' # Add results for PM estimator
#' # - keep weights
#' m456 <- metamerge(m45, m6, taulabel2 = "PM", keep.w = TRUE)
#' summary(m456)
#'
#' m123456 <- metamerge(m123, m456)
#' m123456
#'
#' # Use Hartung-Knapp confidence intervals
#' # - do not keep information on Q, I2 and weights
#' m7 <- update(m4, method.random.ci = "HK",
#' text.random = "Hartung-Knapp method (REML)")
#' m8 <- update(m5, method.random.ci = "HK",
#' text.random = "Hartung-Knapp method (DL)")
#' m9 <- update(m6, method.random.ci = "HK",
#' text.random = "Hartung-Knapp method (PM)")
#'
#' # Merge results for Hartung-Knapp method (with REML and DL estimator)
#' # - RE weights for REML estimator are shown
#' m78 <- metamerge(m7, m8)
#' summary(m78)
#'
#' m789 <- metamerge(m78, m9)
#' summary(m789)
#'
#' # Merge everything
#' m1to9 <- metamerge(metamerge(m123, m456, keep.w = TRUE), m789)
#' summary(m1to9)
#'
#' m10 <- metabin(d.asp, n.asp, d.plac, n.plac, data = Fleiss1993bin,
#' studlab = paste(study, year), sm = "OR", method = "GLMM",
#' text.common = "Common effect model (GLMM)",
#' text.random = "Random effects model (GLMM)")
#'
#' m.all <- metamerge(m1to9, m10, keep.Q = TRUE,
#' taulabel2 = "ML-GLMM", hetlabel2 = "GLMM")
#' summary(m.all)
#'
#' forest(m.all, layout = "JAMA")
#' forest(m.all)
#' }
#'
#' settings.meta(oldset)
#' options(oldopts)
#'
#' @export metamerge
metamerge <- function(meta1, meta2,
##
common1 = meta1$common,
random1 = meta1$random,
prediction1 = meta1$prediction,
common2 = meta2$common,
random2 = meta2$random,
prediction2 = meta2$prediction,
##
text.pooled1 = "", text.pooled2 = "",
text.w.pooled1 = "", text.w.pooled2 = "",
##
label1 = "", label2 = "",
hetlabel1 = label1, hetlabel2 = label2,
taulabel1 = label1, taulabel2 = label2,
##
text.common1 = text.pooled1,
text.common2 = text.pooled2,
text.random1 = text.pooled1,
text.random2 = text.pooled2,
text.predict1 = text.pooled1,
text.predict2 = text.pooled2,
##
text.w.common1 = text.w.pooled1,
text.w.common2 = text.w.pooled2,
text.w.random1 = text.w.pooled1,
text.w.random2 = text.w.pooled2,
##
keep = FALSE,
keep.Q = keep, keep.I2 = keep.Q,
keep.w = keep,
##
common = common1 | common2,
random = random1 | random2,
overall = common | random,
overall.hetstat = common | random,
prediction = prediction1 | prediction2,
##
backtransf,
##
warn.deprecated = gs("warn.deprecated"),
pooled1, pooled2) {
##
##
## (1) Check arguments
##
##
if (missing(meta1))
stop("Argument 'meta1' must be provided.",
call. = FALSE)
if (missing(meta2)) {
warning("Argument 'meta2' not provided.",
call. = FALSE)
return(meta1)
}
##
if (inherits(meta1, "copas") |
inherits(meta1, "limitmeta") |
inherits(meta1, "robu"))
stop("Argument 'meta1' cannot be of class '",
class(meta1), "' (use argument 'meta2').",
call. = FALSE)
##
chkclass(meta1, "meta")
meta1 <- updateversion(meta1)
##
chkclass(meta2, c("meta", "limitmeta", "copas", "robu"))
##
if (inherits(meta1, "netpairwise") | inherits(meta2, "netpairwise"))
stop("R objects of class 'netpairwise' cannot be merged.",
call. = FALSE)
##
if (inherits(meta2, "meta"))
meta2 <- updateversion(meta2)
##
is.copas <- inherits(meta2, "copas")
is.limit <- inherits(meta2, "limitmeta")
is.robu <- inherits(meta2, "robu")
##
if (is.copas | is.limit | is.robu) {
common2 <- FALSE
random2 <- TRUE
prediction2 <- FALSE
meta2$three.level <- FALSE
##
keep <- FALSE
keep.Q <- FALSE
keep.I2 <- FALSE
keep.w <- FALSE
}
##
chklogical(warn.deprecated)
##
missing.pooled1 <- missing(pooled1)
missing.pooled2 <- missing(pooled2)
##
missing.common1 <- missing(common1)
missing.random1 <- missing(random1)
##
missing.common2 <- missing(common2)
missing.random2 <- missing(random2)
##
deprecated2(common1, missing(common1),
pooled1, missing.pooled1, warn.deprecated)
deprecated2(random1, missing(random1),
pooled1, missing.pooled1, warn.deprecated)
##
if (missing.common1 & !missing(pooled1)) {
pooled1 <- setchar(pooled1, c("both", "common", "random", "fixed"))
pooled1[pooled1 == "fixed"] <- "common"
common1 <- pooled1 == "common"
}
##
if (missing.random1 & !missing(pooled1)) {
pooled1 <- setchar(pooled1, c("both", "common", "random", "fixed"))
random1 <- pooled1 %in% c("common", "both")
}
##
chklogical(common1)
chklogical(random1)
##
deprecated2(common2, missing(common2),
pooled2, missing.pooled2, warn.deprecated)
deprecated2(random2, missing(random2),
pooled2, missing.pooled2, warn.deprecated)
##
if (missing.common2 & !missing(pooled2)) {
pooled2 <- setchar(pooled2, c("both", "common", "random", "fixed"))
pooled2[pooled2 == "fixed"] <- "common"
common2 <- pooled2 == "common"
}
##
if (missing.random2 & !missing(pooled2)) {
pooled2 <- setchar(pooled2, c("both", "common", "random", "fixed"))
random2 <- pooled2 %in% c("common", "both")
}
##
chklogical(common2)
chklogical(random2)
##
chkchar(text.pooled1, length = 1)
chkchar(text.pooled2, length = 1)
chkchar(text.common1, length = 1)
chkchar(text.common2, length = 1)
chkchar(text.random1, length = 1)
chkchar(text.random2, length = 1)
chkchar(text.predict1, length = 1)
chkchar(text.predict2, length = 1)
##
chkchar(text.w.pooled1)
chkchar(text.w.pooled2)
chkchar(text.w.common1)
chkchar(text.w.common2)
chkchar(text.w.random1)
chkchar(text.w.random2)
##
chklogical(keep)
chklogical(keep.Q)
chklogical(keep.I2)
chklogical(keep.w)
##
missing.hetlabel1 <- missing(label1) & missing(hetlabel1)
missing.taulabel1 <- missing(label1) & missing(taulabel1)
missing.hetlabel2 <- missing(label2) & missing(hetlabel2)
missing.taulabel2 <- missing(label2) & missing(taulabel2)
##
chkchar(label1, length = 1)
chkchar(label2, length = 1)
chkchar(hetlabel1, length = 1)
chkchar(taulabel1, length = 1)
chkchar(hetlabel2, length = 1)
chkchar(taulabel2, length = 1)
##
chklogical(common)
chklogical(random)
chklogical(overall)
chklogical(overall.hetstat)
chklogical(prediction)
##
if (!missing(backtransf))
chklogical(backtransf)
else {
if (!is.null(meta1$backtransf) & !is.null(meta2$backtransf))
backtransf <- meta1$backtransf | meta2$backtransf
else if (!is.null(meta1$backtransf))
backtransf <- meta1$backtransf
else if (!is.null(meta2$backtransf))
backtransf <- meta2$backtransf
else
backtransf <- FALSE
}
##
## Check summary measures
##
samesm(meta1, meta2)
##
## Check original data (if available)
##
samedata(meta1, meta2)
##
## Check subgroup levels
##
samesubgroups(meta1, meta2)
##
## Check levels of confidence intervals
##
lvls <- sort(unique(c(meta1$level, meta2$level)))
lvls.ma <- sort(unique(c(meta1$level.ma, meta2$level.ma)))
##
if (length(lvls) != 1)
stop("Different level for confidence intervals of individual studies: ",
paste0(lvls, collapse = ", "),
call. = FALSE)
if (length(lvls.ma) != 1)
stop("Different level for meta-analysis confidence intervals: ",
paste0(lvls.ma, collapse = ", "),
call. = FALSE)
##
##
## (2) Some assignments for trim-and-fill method (meta1 / meta2),
## Copas selection model, limit meta-analysis and robust
## variance meta-analysis (meta2)
##
meta1 <- updateobj(meta1, text.common1, text.random1, text.predict1,
text.w.common1, text.w.random1,
hetlabel1, taulabel1)
##
meta2 <- updateobj(meta2, text.common2, text.random2, text.predict2,
text.w.common2, text.w.random2,
hetlabel2, taulabel2)
##
##
## (3) Some more assignments
##
##
meta1$detail.tau <- replaceNULL(meta1$detail.tau, "")
meta2$detail.tau <- replaceNULL(meta2$detail.tau, "")
##
meta1$method.tau <- replaceNULL(meta1$method.tau, "")
meta2$method.tau <- replaceNULL(meta2$method.tau, "")
##
meta1$method.tau.ci <- replaceNULL(meta1$method.tau.ci, "")
meta2$method.tau.ci <- replaceNULL(meta2$method.tau.ci, "")
##
meta1$method.random.ci <- replaceNULL(meta1$method.random.ci, "")
meta2$method.random.ci <- replaceNULL(meta2$method.random.ci, "")
##
meta1$df.random <- replaceNULL(meta1$df.random, NA)
meta2$df.random <- replaceNULL(meta2$df.random, NA)
##
##
## (4) Remove results from first meta-analysis (if necessary)
##
##
if (!common1 & common2)
meta1 <- dropcommon(meta1)
##
if (!random1 & random2)
meta1 <- droprandom(meta1)
##
if (!prediction1 & prediction2)
meta1 <- droppredict(meta1)
##
##
## (5) Remove results from second meta-analysis (if necessary)
##
##
if (!common2)
meta2 <- dropcommon(meta2)
##
if (!random2)
meta2 <- droprandom(meta2)
##
if (!prediction2)
meta2 <- droppredict(meta2)
##
##
## (6) Merge results
##
##
res <- meta1
##
ncom1 <- length(meta1$TE.common)
nran1 <- length(meta1$TE.random)
ncom2 <- length(meta2$TE.common)
nran2 <- length(meta2$TE.random)
##
## Individual study weights
##
if (!common1 & common2) {
res$w.common <- meta2$w.common
res$text.w.common <- meta2$text.w.common
}
else if (!is.null(meta1$w.common) & !is.null(meta2$w.common) & keep.w) {
res$w.common <- cbind(meta1$w.common, meta2$w.common)
colnames(res$w.common) <- c(meta1$text.w.common, meta2$text.w.common)
rownames(res$w.common) <- meta1$studlab
##
res$text.w.common <- c(meta1$text.w.common, meta2$text.w.common)
}
##
if (is.null(res$w.random) & !is.null(meta2$w.random)) {
res$w.random <- meta2$w.random
res$text.w.random <- meta2$text.w.random
}
else if (!is.null(res$w.random) & !is.null(meta2$w.random) & keep.w) {
res$w.random <- cbind(meta1$w.random, meta2$w.random)
colnames(res$w.random) <- c(meta1$text.w.random, meta2$text.w.random)
rownames(res$w.random) <- meta1$studlab
##
res$text.w.random <- c(meta1$text.w.random, meta2$text.w.random)
}
##
res$hetlabel <- c(meta1$hetlabel, meta2$hetlabel)
##
res$method <-
expandmerge(meta1$method, meta2$method,
nc1 = ncom1, nc2 = ncom2)
res$method.random <-
expandmerge(meta1$method.random, meta2$method.random,
nr1 = nran1, nr2 = nran2)
##
## Number of studies
##
res$k <-
expandmerge(meta1$k, meta2$k,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$k.all <-
expandmerge(meta1$k.all, meta2$k.all,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$k.MH <-
expandmerge(meta1$k.MH, meta2$k.MH,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$k.study <-
expandmerge(meta1$k.study, meta2$k.study,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$k.TE <-
expandmerge(meta1$k.TE, meta2$k.TE,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$k0 <-
expandmerge(meta1$k0, meta2$k0,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
##
## Common effect model
##
res$TE.common <- c(meta1$TE.common, meta2$TE.common)
res$seTE.common <- c(meta1$seTE.common, meta2$seTE.common)
res$statistic.common <- c(meta1$statistic.common, meta2$statistic.common)
res$pval.common <- c(meta1$pval.common, meta2$pval.common)
res$lower.common <- c(meta1$lower.common, meta2$lower.common)
res$upper.common <- c(meta1$upper.common, meta2$upper.common)
res$zval.common <- c(meta1$zval.common, meta2$zval.common)
res$text.common <- c(meta1$text.common, meta2$text.common)
##
## Random effects model
##
res$TE.random <- c(meta1$TE.random, meta2$TE.random)
res$seTE.random <- c(meta1$seTE.random, meta2$seTE.random)
res$statistic.random <- c(meta1$statistic.random, meta2$statistic.random)
res$pval.random <- c(meta1$pval.random, meta2$pval.random)
res$method.random.ci <- c(meta1$method.random.ci, meta2$method.random.ci)
res$df.random <- c(meta1$df.random, meta2$df.random)
res$lower.random <- c(meta1$lower.random, meta2$lower.random)
res$upper.random <- c(meta1$upper.random, meta2$upper.random)
res$zval.random <- c(meta1$zval.random, meta2$zval.random)
res$seTE.classic <- c(meta1$seTE.classic, meta2$seTE.classic)
res$adhoc.hakn.ci <- c(meta1$adhoc.hakn.ci, meta2$adhoc.hakn.ci)
res$df.hakn <- c(meta1$df.hakn, meta2$df.hakn)
res$seTE.hakn.ci <- c(meta1$seTE.hakn.ci, meta2$seTE.hakn.ci)
res$seTE.hakn.adhoc.ci <-
c(meta1$seTE.hakn.adhoc.ci, meta2$seTE.hakn.adhoc.ci)
res$df.kero <- c(meta1$df.kero, meta2$df.kero)
res$seTE.kero <- c(meta1$seTE.kero, meta2$seTE.kero)
res$text.random <- c(meta1$text.random, meta2$text.random)
##
## Prediction interval
##
res$method.predict <- c(meta1$method.predict, meta2$method.predict)
res$adhoc.hakn.pi <- c(meta1$adhoc.hakn.pi, meta2$adhoc.hakn.pi)
res$seTE.predict <- c(meta1$seTE.predict, meta2$seTE.predict)
res$df.predict <- c(meta1$df.predict, meta2$df.predict)
res$lower.predict <- c(meta1$lower.predict, meta2$lower.predict)
res$upper.predict <- c(meta1$upper.predict, meta2$upper.predict)
res$seTE.hakn.pi <- c(meta1$seTE.hakn.pi, meta2$seTE.hakn.pi)
res$seTE.hakn.adhoc.pi <-
c(meta1$seTE.hakn.adhoc.pi, meta2$seTE.hakn.adhoc.pi)
res$text.predict <- c(meta1$text.predict, meta2$text.predict)
##
## Heterogeneity statistics
##
Qlabel1 <- if (missing.hetlabel1) names(meta1$Q) else hetlabel1
Qlabel2 <- if (missing.hetlabel2) names(meta2$Q) else hetlabel2
##
if (keep.Q) {
res$Q <- mergevars(meta1$Q, meta2$Q, Qlabel1, Qlabel2)
res$df.Q <- mergevars(meta1$df.Q, meta2$df.Q)
res$pval.Q <- mergevars(meta1$pval.Q, meta2$pval.Q)
}
##
if (keep.I2) {
res$I2 <- mergevars(meta1$I2, meta2$I2, Qlabel1, Qlabel2)
res$lower.I2 <- mergevars(meta1$lower.I2, meta2$upper.I2)
res$upper.I2 <- mergevars(meta1$lower.I2, meta2$upper.I2)
##
res$H <- mergevars(meta1$H, meta2$H, Qlabel1, Qlabel2)
res$lower.H <- mergevars(meta1$lower.H, meta2$upper.H)
res$upper.H <- mergevars(meta1$lower.H, meta2$upper.H)
##
res$Rb <- mergevars(meta1$Rb, meta2$Rb, Qlabel1, Qlabel2)
res$lower.Rb <- mergevars(meta1$lower.Rb, meta2$upper.Rb)
res$upper.Rb <- mergevars(meta1$lower.Rb, meta2$upper.Rb)
}
##
## Trim-and-fill method
##
expandmerge(meta1$method.tau, meta2$method.tau,
nr1 = nran1, nr2 = nran2)
res$left <-
expandmerge(meta1$left, meta2$left,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$ma.common <-
expandmerge(meta1$ma.common, meta2$ma.common,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$type <-
expandmerge(meta1$type, meta2$type,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$n.iter.max <-
expandmerge(meta1$n.iter.max, meta2$n.iter.max,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$n.iter <-
expandmerge(meta1$n.iter, meta2$n.iter,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
##
## Subgroup analyses
##
res$TE.common.w <- mergevars(meta1$TE.common.w, meta2$TE.common.w)
res$seTE.common.w <- mergevars(meta1$seTE.common.w, meta2$seTE.common.w)
res$statistic.common.w <-
mergevars(meta1$statistic.common.w, meta2$statistic.common.w)
res$pval.common.w <- mergevars(meta1$pval.common.w, meta2$pval.common.w)
res$lower.common.w <- mergevars(meta1$lower.common.w, meta2$lower.common.w)
res$upper.common.w <- mergevars(meta1$upper.common.w, meta2$upper.common.w)
##
res$w.common.w <- mergevars(meta1$w.common.w, meta2$w.common.w)
##
res$Q.w.common <- mergevars(meta1$Q.w.common, meta2$Q.w.common)
res$pval.Q.w.common <-
mergevars(meta1$pval.Q.w.common, meta2$pval.Q.w.common)
##
res$Q.b.common <- mergevars(meta1$Q.b.common, meta2$Q.b.common)
res$df.Q.b.common <- mergevars(meta1$df.Q.b.common, meta2$df.Q.b.common)
res$pval.Q.b.common <-
mergevars(meta1$pval.Q.b.common, meta2$pval.Q.b.common)
##
res$TE.random.w <- mergevars(meta1$TE.random.w, meta2$TE.random.w)
res$seTE.random.w <- mergevars(meta1$seTE.random.w, meta2$seTE.random.w)
res$statistic.random.w <-
mergevars(meta1$statistic.random.w, meta2$statistic.random.w)
res$pval.random.w <- mergevars(meta1$pval.random.w, meta2$pval.random.w)
res$df.random.w <- list(meta1$df.random.w, meta2$df.random.w)
res$lower.random.w <- mergevars(meta1$lower.random.w, meta2$lower.random.w)
res$upper.random.w <- mergevars(meta1$upper.random.w, meta2$upper.random.w)
res$df.hakn.w <- list(meta1$df.hakn.w, meta2$df.hakn.w)
res$df.kero.w <- list(meta1$df.kero.w, meta2$df.kero.w)
##
res$w.random.w <- mergevars(meta1$w.random.w, meta2$w.random.w)
##
res$Q.w.random <- mergevars(meta1$Q.w.random, meta2$Q.w.random)
res$pval.Q.w.random <-
mergevars(meta1$pval.Q.w.random, meta2$pval.Q.w.random)
##
res$Q.b.random <- list(meta1$Q.b.random, meta2$Q.b.random)
res$df.Q.b.random <- list(meta1$df.Q.b.random, meta2$df.Q.b.random)
res$pval.Q.b.random <- mergevars(meta1$pval.Q.b.random, meta2$pval.Q.b.random)
##
res$seTE.predict.w <- mergevars(meta1$seTE.predict.w, meta2$seTE.predict.w)
res$df.predict.w <- list(meta1$df.predict.w, meta2$df.predict.w)
res$lower.predict.w <- mergevars(meta1$lower.predict.w, meta2$lower.predict.w)
res$upper.predict.w <- mergevars(meta1$upper.predict.w, meta2$upper.predict.w)
##
##
## (7) More settings
##
##
taulabel1 <- if (missing.taulabel1) names(meta1$tau) else taulabel1
taulabel2 <- if (missing.taulabel2) names(meta2$tau) else taulabel2
##
names(meta1$tau) <- names(meta1$tau2) <- taulabel1
names(meta2$tau) <- names(meta2$tau2) <- taulabel2
##
if (!random1 & random2) {
res$method.tau <- meta2$method.tau
res$method.tau.ci <- meta2$method.tau.ci
res$detail.tau <- meta2$detail.tau
##
res$tau <- meta2$tau
names(res$tau) <- taulabel2
res$lower.tau <- meta2$lower.tau
res$upper.tau <- meta2$upper.tau
##
res$tau2 <- meta2$tau2
names(res$tau2) <- taulabel2
res$lower.tau2 <- meta2$lower.tau2
res$upper.tau2 <- meta2$upper.tau2
##
res$se.tau <- meta2$se.tau
##
res$tau.preset <- meta2$tau.preset
##
res$Q.Cochrane <- meta2$Q.Cochrane
}
else if (random1 & random2) {
res$method.tau <-
expandmerge(meta1$method.tau, meta2$method.tau,
nr1 = nran1, nr2 = nran2)
res$method.tau.ci <-
expandmerge(meta1$method.tau.ci, meta2$method.tau.ci,
nr1 = nran1, nr2 = nran2)
##
res$detail.tau <- c(meta1$detail.tau, meta2$detail.tau)
##
if (!inherits(meta1, "metamerge") && meta1$three.level) {
meta1$tau2 <- sum(meta1$tau2)
meta1$tau <- sqrt(meta1$tau2)
meta1$lower.tau <- meta1$upper.tau <-
meta1$lower.tau2 <- meta1$upper.tau2 <- NA
meta1$sign.lower.tau <- meta1$sign.upper.tau <- ""
}
##
if (!inherits(meta2, "metamerge") && meta2$three.level) {
meta2$tau2 <- sum(meta2$tau2)
meta2$tau <- sqrt(meta2$tau2)
meta2$lower.tau <- meta2$upper.tau <-
meta2$lower.tau2 <- meta2$upper.tau2 <- NA
meta2$sign.lower.tau <- meta2$sign.upper.tau <- ""
}
##
res$tau <- expandmerge(meta1$tau, meta2$tau, nr1 = nran1, nr2 = nran2)
res$lower.tau <-
expandmerge(meta1$lower.tau, meta2$lower.tau, nr1 = nran1, nr2 = nran2)
res$upper.tau <-
expandmerge(meta1$upper.tau, meta2$upper.tau, nr1 = nran1, nr2 = nran2)
##
res$tau2 <- expandmerge(meta1$tau2, meta2$tau2, nr1 = nran1, nr2 = nran2)
res$lower.tau2 <- expandmerge(meta1$lower.tau2, meta2$lower.tau2,
nr1 = nran1, nr2 = nran2)
res$upper.tau2 <- expandmerge(meta1$upper.tau2, meta2$upper.tau2,
nr1 = nran1, nr2 = nran2)
##
res$se.tau <-
expandmerge(meta1$se.tau, meta2$se.tau, nr1 = nran1, nr2 = nran2)
##
res$tau.preset <-
expandmerge(meta1$tau.preset, meta2$tau.preset, nr1 = nran1, nr2 = nran2)
##
res$Q.Cochrane <-
expandmerge(meta1$Q.Cochrane, meta2$Q.Cochrane, nr1 = nran1, nr2 = nran2)
}
##
res$sm <- if (meta1$sm != meta2$sm) "" else meta1$sm
##
res$common <- common
res$random <- random
res$overall <- overall
res$overall.hetstat <- overall.hetstat
res$prediction <- prediction
##
res$backtransf <- backtransf
##
## Three-level model
##
res$three.level <-
expandmerge(meta1$three.level, meta2$three.level,
ncom1, nran1, ncom2, nran2)
if (!random1 & random2)
res$cluster <- meta2$cluster
##
## Additional arguments from metabin(), metainc(), metaprop()
##
res$incr <- expandmerge(meta1$incr, meta2$incr,
ncom1, nran1, ncom2, nran2)
res$method.incr <-
expandmerge(meta1$method.incr, meta2$method.incr,
ncom1, nran1, ncom2, nran2)
res$sparse <-
expandmerge(meta1$sparse, meta2$sparse,
ncom1, nran1, ncom2, nran2)
##
## Additional arguments from metabin()
##
res$allstudies <-
expandmerge(meta1$allstudies, meta2$allstudies,
ncom1, nran1, ncom2, nran2)
res$doublezeros <-
expandmerge(meta1$doublezeros, meta2$doublezeros,
ncom1, nran1, ncom2, nran2)
res$MH.exact <-
expandmerge(meta1$MH.exact, meta2$MH.exact,
ncom1, nran1, ncom2, nran2)
res$RR.Cochrane <-
expandmerge(meta1$RR.Cochrane, meta2$RR.Cochrane,
ncom1, nran1, ncom2, nran2)
res$model.glmm <-
expandmerge(meta1$model.glmm, meta2$model.glmm,
ncom1, nran1, ncom2, nran2)
##
## Additional arguments from metacont()
##
res$pooledvar <- expandmerge(meta1$pooledvar, meta2$pooledvar,
ncom1, nran1, ncom2, nran2)
res$method.smd <- expandmerge(meta1$method.smd, meta2$method.smd,
ncom1, nran1, ncom2, nran2)
res$sd.glass <- expandmerge(meta1$sd.glass, meta2$sd.glass,
ncom1, nran1, ncom2, nran2)
res$exact.smd <- expandmerge(meta1$exact.smd, meta2$exact.smd,
ncom1, nran1, ncom2, nran2)
res$method.mean <- expandmerge(meta1$method.mean, meta2$method.mean,
ncom1, nran1, ncom2, nran2)
res$method.sd <- expandmerge(meta1$method.sd, meta2$method.sd,
ncom1, nran1, ncom2, nran2)
##
##
## (8) Backward compatibility
##
##
res <- backward(res)
##
class(res) <- c(class(res), "metamerge")
res
}
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Defines functions metamerge
Documented in metamerge
#' Merge results of two meta-analyses on the same data set
#'
#' @description
#' This function can be used to merge results of two meta-analyses
#' into a single meta-analysis object if they are based on the same
#' data set. This is, for example, useful to produce a forest plot of
#' a random-effects meta-analysis with different estimates of the
#' between-study variance \eqn{\tau^2}.
#'
#' @param meta1 First meta-analysis object (see Details).
#' @param meta2 Second meta-analysis object (see Details).
#' @param common1 A logical indicating whether results of common
#' effect model should be considered for first meta-analysis.
#' @param random1 A logical indicating whether results of random
#' effects model should be considered for first meta-analysis.
#' @param prediction1 A logical indicating whether prediction interval
#' should be considered for first meta-analysis.
#' @param common2 A logical indicating whether results of common
#' effect model should be considered for second meta-analysis.
#' @param random2 A logical indicating whether results of random
#' effects model should be considered for second meta-analysis.
#' @param prediction2 A logical indicating whether prediction interval
#' should be considered for second meta-analysis.
#' @param text.pooled1 A character string used in printouts and forest
#' plot to label the results from the first meta-analysis.
#' @param text.pooled2 A character string used in printouts and forest
#' plot to label the results from the second meta-analysis.
#' @param text.w.pooled1 A character string used to label weights of
#' the first meta-analysis; can be of same length as the number of
#' pooled estimates requested in argument \code{pooled1}.
#' @param text.w.pooled2 A character string used to label weights of
#' the second meta-analysis; can be of same length as the number of
#' pooled estimates requested in argument \code{pooled1}.
#' @param label1 Default setting for arguments 'hetlabel1' and
#' 'taulabel1'.
#' @param label2 Default setting for arguments 'hetlabel2' and
#' 'taulabel2'.
#' @param hetlabel1 A character string used to label heterogeneity
#' statistics of the first meta-analysis.
#' @param hetlabel2 A character string used to label heterogeneity
#' statistics of the second meta-analysis.
#' @param taulabel1 A character string used to label estimate of
#' between-study variance of the first meta-analysis.
#' @param taulabel2 A character string used to label estimate of
#' between-study variance of the second meta-analysis.
#' @param text.common1 A character string used in printouts and forest
#' plot to label results for common effect models from the first
#' meta-analysis.
#' @param text.common2 A character string used in printouts and forest
#' plot to label results for common effect models from the second
#' meta-analysis.
#' @param text.random1 A character string used in printouts and forest
#' plot to label results for random effects models from the first
#' meta-analysis.
#' @param text.random2 A character string used in printouts and forest
#' plot to label results for random effects models from the second
#' meta-analysis.
#' @param text.predict1 A character string used in printouts and
#' forest plot to label prediction interval from the first
#' meta-analysis.
#' @param text.predict2 A character string used in printouts and
#' forest plot to label prediction interval from the second
#' meta-analysis.
#' @param text.w.common1 A character string used to label common
#' effect weights of the first meta-analysis; can be of same length
#' as the number of common effect estimates.
#' @param text.w.common2 A character string used to label common
#' effect weights of the second meta-analysis; can be of same length
#' as the number of common effect estimates.
#' @param text.w.random1 A character string used to label random
#' effects weights of the first meta-analysis; can be of same length
#' as the number of random effects estimates.
#' @param text.w.random2 A character string used to label random
#' effects weights of the second meta-analysis; can be of same
#' length as the number of random effects estimates.
#' @param keep A logical indicating whether to keep additional
#' information from second meta-analysis.
#' @param keep.Q A logical indicating whether heterogeneity statistic
#' Q of second meta-analysis should be kept or ignored.
#' @param keep.I2 A logical indicating whether heterogeneity statistic
#' I2 of second meta-analysis should be kept or ignored.
#' @param keep.w A logical indicating whether weights of the second
#' meta-analysis should be kept or ignored.
#' @param common A logical indicating whether results of common effect
#' meta-analyses should be reported.
#' @param random A logical indicating whether results of random
#' effects meta-analyses should be reported.
#' @param overall A logical indicating whether overall summaries
#' should be reported.
#' @param overall.hetstat A logical value indicating whether to print
#' heterogeneity measures for overall treatment comparisons.
#' @param prediction A logical indicating whether prediction intervals
#' should be reported.
#' @param backtransf A logical indicating whether results should be
#' back transformed in printouts and plots. If
#' \code{backtransf=TRUE} (default), results for \code{sm="OR"} are
#' printed as odds ratios rather than log odds ratios, for example.
#' @param warn.deprecated A logical indicating whether warnings should
#' be printed if deprecated arguments are used.
#' @param pooled1 Deprecated argument (replaced by 'common1',
#' 'random1', 'prediction1'). A character string indicating whether
#' results of common effect or random effects model should be
#' considered for first meta-analysis. Either \code{"both"},
#' \code{"common"} or \code{"random"}, can be abbreviated.
#' @param pooled2 Deprecated argument (replaced by 'common2',
#' 'random2', 'prediction2'). A character string indicating whether
#' results of common effect or random effects model should be
#' considered for second meta-analysis. Either \code{"both"},
#' \code{"common"} or \code{"random"}, can be abbreviated.
#'
#' @details
#' In R package \bold{meta}, objects of class \code{"meta"} contain
#' results of both common effect and random effects
#' meta-analyses. This function enables the user to merge the results
#' of two meta-analysis object if they are based on the same data set.
#'
#' Applications of this function include printing and plotting results
#' of the common effect or random effects meta-analysis and the
#' \itemize{
#' \item trim-and-fill method (\code{\link{trimfill}}),
#' \item limit meta-analyis (\code{\link[metasens]{limitmeta}} from R
#' package \bold{metasens}),
#' \item Copas selection model (\code{\link[metasens]{copas}} from R
#' package \bold{metasens}),
#' \item robust variance meta-analysis model
#' (\code{\link[robumeta]{robu}} from R package \bold{robumeta}).
#' }
#'
#' The first argument (\code{meta1}) must be an object created by a
#' meta-analysis function (see \code{\link{meta-object}}). If an
#' object created with \code{\link[metasens]{limitmeta}} or
#' \code{\link[metasens]{copas}} is provided as the first argument,
#' this object will be returned, i.e., argument \code{meta2} will be
#' ignored.
#'
#' The second meta-analysis could be an object created by a
#' meta-analysis function or with \code{\link{trimfill}},
#' \code{\link[metasens]{limitmeta}}, \code{\link[metasens]{copas}},
#' or \code{\link[robumeta]{robu}}.
#'
#' The created meta-analysis object only contains the study results,
#' i.e., estimated effects and confidence intervals, from the first
#' meta-analysis which are shown in printouts and forest plots. This
#' only makes a difference for meta-analysis methods where individual
#' study results differ, e.g., Mantel-Haenszel and Peto method for
#' binary outcomes (see \code{\link{metabin}}).
#'
#' R function \code{\link{metaadd}} can be used to add pooled results
#' from any (external) meta-analysis.
#'
#' R function \code{\link{metabind}} can be used to print and plot the
#' results of several meta-analyses without the restriction that the
#' same data set has to be used. Accordingly, individual study results
#' are ignored.
#'
#' @return
#' An object of class \code{"meta"} and \code{"metamerge"} with
#' corresponding generic functions (see \code{\link{meta-object}}).
#'
#' The following list elements have a different meaning:
#' \item{TE, seTE, studlab}{Treatment estimate, standard error, and
#' study labels (first meta-analyis).}
#' \item{lower, upper}{Lower and upper confidence interval limits for
#' individual studies (first meta-analysis).}
#' \item{statistic, pval}{Statistic and p-value for test of treatment
#' effect for individual studies (first meta-analysis.}
#' \item{w.common}{Vector or matrix with common effect weights.}
#' \item{w.random}{Vector or matrix with random effects weights.}
#' \item{k}{Vector with number of estimates (same length as number of
#' common effect and random effects estimates).}
#' \item{k.study}{Vector with number of studies (same length as
#' number of common effect and random effects estimates).}
#' \item{k.all}{Vector with total number of studies (same length as
#' number of common effect and random effects estimates).}
#' \item{k.TE}{Vector with number of studies with estimable effects
#' (same length as number of common effect and random effects
#' estimates).}
#' \item{k.MH}{Vector with number of studies combined with
#' Mantel-Haenszel method (same length as number of common effect
#' and random effects estimates).}
#' \item{TE.common}{Vector with common effect estimates.}
#' \item{seTE.common}{Vector with standard errors of common effect
#' estimates.}
#' \item{lower.common}{Vector with lower confidence limits (common
#' effect model).}
#' \item{upper.common}{Vector with upper confidence limits (common
#' effect model).}
#' \item{statistic.common}{Vector with test statistics for test of
#' overall effect (common effect model).}
#' \item{pval.common}{Vector with p-value of test for overall effect
#' (common effect model).}
#' \item{TE.random}{Vector with random effects estimates.}
#' \item{seTE.random}{Vector with standard errors of random effects
#' estimates.}
#' \item{lower.random}{Vector with lower confidence limits (random
#' effects model).}
#' \item{upper.random}{Vector with upper confidence limits (random
#' effects model).}
#' \item{statistic.random}{Vector with test statistics for test of
#' overall effect (random effects model).}
#' \item{pval.random}{Vector with p-value of test for overall effect
#' (random effects model).}
#'
#' Furthermore, meta-analysis results of common effect or random
#' effects model are taken from first meta-analysis if only random
#' effects or common effects models are selected from both
#' meta-analyses (arguments \code{pooled1} and \code{pooled2}).
#'
#' @author Guido Schwarzer \email{guido.schwarzer@@uniklinik-freiburg.de}
#'
#' @seealso \code{\link{metagen}}, \code{\link{metabind}},
#' \code{\link{metaadd}}
#'
#' @examples
#' # Print results with more significant digits
#' oldset <- settings.meta(digits = 6, digits.stat = 4, digits.pval = 6,
#' digits.Q = 6, digits.I2 = 4, digits.H = 4)
#' oldopts <- options(width = 120)
#'
#' data(Fleiss1993bin)
#'
#' # Mantel-Haenszel method
#' m1 <- metabin(d.asp, n.asp, d.plac, n.plac, data = Fleiss1993bin,
#' studlab = paste(study, year), sm = "OR",
#' text.common = "Mantel-Haenszel method", text.w.common = "MH",
#' text.random = "Random effects model (IV)", text.w.random = "RE-IV")
#'
#' # Peto method
#' m2 <- update(m1, method = "Peto",
#' text.common = "Peto method", text.w.common = "Peto",
#' text.random = "Random effects model (Peto)", text.w.random = "RE-Peto")
#'
#' # Inverse variance method
#' m3 <- update(m2, method = "Inverse", random = FALSE,
#' text.common = "Inverse-variance method", text.w.common = "IV")
#'
#' # Merge results from MH and Peto method
#' # - show individual results for MH method (as this is the first meta-analysis)
#' # - keep all additional information from Peto meta-analysis (i.e.,
#' # weights, Q statistic and I2 statistic)
#' m12 <- metamerge(m1, m2,
#' taulabel1 = "REML-IV", taulabel2 = "REML-Peto",
#' hetlabel1 = "MH/IV", hetlabel2 = "Peto",
#' keep = TRUE)
#' m12
#'
#' # Add results from inverse variance method
#' # - keep weights from IV meta-analysis
#' # - Q and I2 statistic are identical for sm = "MH" and sm = "Inverse"
#' # as inverse variance method is used for sm = "MH" under random
#' # effects model
#' m123 <- metamerge(m12, m3, keep.w = TRUE)
#' summary(m123)
#' \dontrun{
#' forest(m123, digits = 6)
#'
#' # Merge results (show individual results for Peto method)
#' m21 <- metamerge(m2, m1,
#' taulabel1 = "REML-Peto", taulabel2 = "REML-IV",
#' hetlabel1 = "Peto", hetlabel2 = "MH/IV",
#' keep = TRUE)
#' m213 <- metamerge(m21, m3, keep.w = TRUE)
#' summary(m213)
#'
#' # Random effects method using ML estimator for between-study variance tau2
#' m4 <- update(m1, common = FALSE, method.tau = "ML",
#' text.random = "Random effects model (ML)", text.w.random = "RE-ML")
#'
#' # Use DerSimonian-Laird estimator for tau2
#' m5 <- update(m4, method.tau = "DL",
#' text.random = "Random effects model (DL)", text.w.random = "RE-DL")
#'
#' # Use Paule-Mandel estimator for tau2
#' m6 <- update(m4, method.tau = "PM",
#' text.random = "Random effects model (PM)", text.w.random = "RE-PM")
#'
#' # Merge random effects results for ML and DL estimators
#' # - keep weights for DL estimator (which are different from ML)
#' m45 <- metamerge(m4, m5, taulabel1 = "ML", taulabel2 = "DL",
#' keep.w = TRUE)
#' summary(m45)
#'
#' # Add results for PM estimator
#' # - keep weights
#' m456 <- metamerge(m45, m6, taulabel2 = "PM", keep.w = TRUE)
#' summary(m456)
#'
#' m123456 <- metamerge(m123, m456)
#' m123456
#'
#' # Use Hartung-Knapp confidence intervals
#' # - do not keep information on Q, I2 and weights
#' m7 <- update(m4, method.random.ci = "HK",
#' text.random = "Hartung-Knapp method (REML)")
#' m8 <- update(m5, method.random.ci = "HK",
#' text.random = "Hartung-Knapp method (DL)")
#' m9 <- update(m6, method.random.ci = "HK",
#' text.random = "Hartung-Knapp method (PM)")
#'
#' # Merge results for Hartung-Knapp method (with REML and DL estimator)
#' # - RE weights for REML estimator are shown
#' m78 <- metamerge(m7, m8)
#' summary(m78)
#'
#' m789 <- metamerge(m78, m9)
#' summary(m789)
#'
#' # Merge everything
#' m1to9 <- metamerge(metamerge(m123, m456, keep.w = TRUE), m789)
#' summary(m1to9)
#'
#' m10 <- metabin(d.asp, n.asp, d.plac, n.plac, data = Fleiss1993bin,
#' studlab = paste(study, year), sm = "OR", method = "GLMM",
#' text.common = "Common effect model (GLMM)",
#' text.random = "Random effects model (GLMM)")
#'
#' m.all <- metamerge(m1to9, m10, keep.Q = TRUE,
#' taulabel2 = "ML-GLMM", hetlabel2 = "GLMM")
#' summary(m.all)
#'
#' forest(m.all, layout = "JAMA")
#' forest(m.all)
#' }
#'
#' settings.meta(oldset)
#' options(oldopts)
#'
#' @export metamerge
metamerge <- function(meta1, meta2,
##
common1 = meta1$common,
random1 = meta1$random,
prediction1 = meta1$prediction,
common2 = meta2$common,
random2 = meta2$random,
prediction2 = meta2$prediction,
##
text.pooled1 = "", text.pooled2 = "",
text.w.pooled1 = "", text.w.pooled2 = "",
##
label1 = "", label2 = "",
hetlabel1 = label1, hetlabel2 = label2,
taulabel1 = label1, taulabel2 = label2,
##
text.common1 = text.pooled1,
text.common2 = text.pooled2,
text.random1 = text.pooled1,
text.random2 = text.pooled2,
text.predict1 = text.pooled1,
text.predict2 = text.pooled2,
##
text.w.common1 = text.w.pooled1,
text.w.common2 = text.w.pooled2,
text.w.random1 = text.w.pooled1,
text.w.random2 = text.w.pooled2,
##
keep = FALSE,
keep.Q = keep, keep.I2 = keep.Q,
keep.w = keep,
##
common = common1 | common2,
random = random1 | random2,
overall = common | random,
overall.hetstat = common | random,
prediction = prediction1 | prediction2,
##
backtransf,
##
warn.deprecated = gs("warn.deprecated"),
pooled1, pooled2) {
##
##
## (1) Check arguments
##
##
if (missing(meta1))
stop("Argument 'meta1' must be provided.",
call. = FALSE)
if (missing(meta2)) {
warning("Argument 'meta2' not provided.",
call. = FALSE)
return(meta1)
}
##
if (inherits(meta1, "copas") |
inherits(meta1, "limitmeta") |
inherits(meta1, "robu"))
stop("Argument 'meta1' cannot be of class '",
class(meta1), "' (use argument 'meta2').",
call. = FALSE)
##
chkclass(meta1, "meta")
meta1 <- updateversion(meta1)
##
chkclass(meta2, c("meta", "limitmeta", "copas", "robu"))
##
if (inherits(meta1, "netpairwise") | inherits(meta2, "netpairwise"))
stop("R objects of class 'netpairwise' cannot be merged.",
call. = FALSE)
##
if (inherits(meta2, "meta"))
meta2 <- updateversion(meta2)
##
is.copas <- inherits(meta2, "copas")
is.limit <- inherits(meta2, "limitmeta")
is.robu <- inherits(meta2, "robu")
##
if (is.copas | is.limit | is.robu) {
common2 <- FALSE
random2 <- TRUE
prediction2 <- FALSE
meta2$three.level <- FALSE
##
keep <- FALSE
keep.Q <- FALSE
keep.I2 <- FALSE
keep.w <- FALSE
}
##
chklogical(warn.deprecated)
##
missing.pooled1 <- missing(pooled1)
missing.pooled2 <- missing(pooled2)
##
missing.common1 <- missing(common1)
missing.random1 <- missing(random1)
##
missing.common2 <- missing(common2)
missing.random2 <- missing(random2)
##
deprecated2(common1, missing(common1),
pooled1, missing.pooled1, warn.deprecated)
deprecated2(random1, missing(random1),
pooled1, missing.pooled1, warn.deprecated)
##
if (missing.common1 & !missing(pooled1)) {
pooled1 <- setchar(pooled1, c("both", "common", "random", "fixed"))
pooled1[pooled1 == "fixed"] <- "common"
common1 <- pooled1 == "common"
}
##
if (missing.random1 & !missing(pooled1)) {
pooled1 <- setchar(pooled1, c("both", "common", "random", "fixed"))
random1 <- pooled1 %in% c("common", "both")
}
##
chklogical(common1)
chklogical(random1)
##
deprecated2(common2, missing(common2),
pooled2, missing.pooled2, warn.deprecated)
deprecated2(random2, missing(random2),
pooled2, missing.pooled2, warn.deprecated)
##
if (missing.common2 & !missing(pooled2)) {
pooled2 <- setchar(pooled2, c("both", "common", "random", "fixed"))
pooled2[pooled2 == "fixed"] <- "common"
common2 <- pooled2 == "common"
}
##
if (missing.random2 & !missing(pooled2)) {
pooled2 <- setchar(pooled2, c("both", "common", "random", "fixed"))
random2 <- pooled2 %in% c("common", "both")
}
##
chklogical(common2)
chklogical(random2)
##
chkchar(text.pooled1, length = 1)
chkchar(text.pooled2, length = 1)
chkchar(text.common1, length = 1)
chkchar(text.common2, length = 1)
chkchar(text.random1, length = 1)
chkchar(text.random2, length = 1)
chkchar(text.predict1, length = 1)
chkchar(text.predict2, length = 1)
##
chkchar(text.w.pooled1)
chkchar(text.w.pooled2)
chkchar(text.w.common1)
chkchar(text.w.common2)
chkchar(text.w.random1)
chkchar(text.w.random2)
##
chklogical(keep)
chklogical(keep.Q)
chklogical(keep.I2)
chklogical(keep.w)
##
missing.hetlabel1 <- missing(label1) & missing(hetlabel1)
missing.taulabel1 <- missing(label1) & missing(taulabel1)
missing.hetlabel2 <- missing(label2) & missing(hetlabel2)
missing.taulabel2 <- missing(label2) & missing(taulabel2)
##
chkchar(label1, length = 1)
chkchar(label2, length = 1)
chkchar(hetlabel1, length = 1)
chkchar(taulabel1, length = 1)
chkchar(hetlabel2, length = 1)
chkchar(taulabel2, length = 1)
##
chklogical(common)
chklogical(random)
chklogical(overall)
chklogical(overall.hetstat)
chklogical(prediction)
##
if (!missing(backtransf))
chklogical(backtransf)
else {
if (!is.null(meta1$backtransf) & !is.null(meta2$backtransf))
backtransf <- meta1$backtransf | meta2$backtransf
else if (!is.null(meta1$backtransf))
backtransf <- meta1$backtransf
else if (!is.null(meta2$backtransf))
backtransf <- meta2$backtransf
else
backtransf <- FALSE
}
##
## Check summary measures
##
samesm(meta1, meta2)
##
## Check original data (if available)
##
samedata(meta1, meta2)
##
## Check subgroup levels
##
samesubgroups(meta1, meta2)
##
## Check levels of confidence intervals
##
lvls <- sort(unique(c(meta1$level, meta2$level)))
lvls.ma <- sort(unique(c(meta1$level.ma, meta2$level.ma)))
##
if (length(lvls) != 1)
stop("Different level for confidence intervals of individual studies: ",
paste0(lvls, collapse = ", "),
call. = FALSE)
if (length(lvls.ma) != 1)
stop("Different level for meta-analysis confidence intervals: ",
paste0(lvls.ma, collapse = ", "),
call. = FALSE)
##
##
## (2) Some assignments for trim-and-fill method (meta1 / meta2),
## Copas selection model, limit meta-analysis and robust
## variance meta-analysis (meta2)
##
meta1 <- updateobj(meta1, text.common1, text.random1, text.predict1,
text.w.common1, text.w.random1,
hetlabel1, taulabel1)
##
meta2 <- updateobj(meta2, text.common2, text.random2, text.predict2,
text.w.common2, text.w.random2,
hetlabel2, taulabel2)
##
##
## (3) Some more assignments
##
##
meta1$detail.tau <- replaceNULL(meta1$detail.tau, "")
meta2$detail.tau <- replaceNULL(meta2$detail.tau, "")
##
meta1$method.tau <- replaceNULL(meta1$method.tau, "")
meta2$method.tau <- replaceNULL(meta2$method.tau, "")
##
meta1$method.tau.ci <- replaceNULL(meta1$method.tau.ci, "")
meta2$method.tau.ci <- replaceNULL(meta2$method.tau.ci, "")
##
meta1$method.random.ci <- replaceNULL(meta1$method.random.ci, "")
meta2$method.random.ci <- replaceNULL(meta2$method.random.ci, "")
##
meta1$df.random <- replaceNULL(meta1$df.random, NA)
meta2$df.random <- replaceNULL(meta2$df.random, NA)
##
##
## (4) Remove results from first meta-analysis (if necessary)
##
##
if (!common1 & common2)
meta1 <- dropcommon(meta1)
##
if (!random1 & random2)
meta1 <- droprandom(meta1)
##
if (!prediction1 & prediction2)
meta1 <- droppredict(meta1)
##
##
## (5) Remove results from second meta-analysis (if necessary)
##
##
if (!common2)
meta2 <- dropcommon(meta2)
##
if (!random2)
meta2 <- droprandom(meta2)
##
if (!prediction2)
meta2 <- droppredict(meta2)
##
##
## (6) Merge results
##
##
res <- meta1
##
ncom1 <- length(meta1$TE.common)
nran1 <- length(meta1$TE.random)
ncom2 <- length(meta2$TE.common)
nran2 <- length(meta2$TE.random)
##
## Individual study weights
##
if (!common1 & common2) {
res$w.common <- meta2$w.common
res$text.w.common <- meta2$text.w.common
}
else if (!is.null(meta1$w.common) & !is.null(meta2$w.common) & keep.w) {
res$w.common <- cbind(meta1$w.common, meta2$w.common)
colnames(res$w.common) <- c(meta1$text.w.common, meta2$text.w.common)
rownames(res$w.common) <- meta1$studlab
##
res$text.w.common <- c(meta1$text.w.common, meta2$text.w.common)
}
##
if (is.null(res$w.random) & !is.null(meta2$w.random)) {
res$w.random <- meta2$w.random
res$text.w.random <- meta2$text.w.random
}
else if (!is.null(res$w.random) & !is.null(meta2$w.random) & keep.w) {
res$w.random <- cbind(meta1$w.random, meta2$w.random)
colnames(res$w.random) <- c(meta1$text.w.random, meta2$text.w.random)
rownames(res$w.random) <- meta1$studlab
##
res$text.w.random <- c(meta1$text.w.random, meta2$text.w.random)
}
##
res$hetlabel <- c(meta1$hetlabel, meta2$hetlabel)
##
res$method <-
expandmerge(meta1$method, meta2$method,
nc1 = ncom1, nc2 = ncom2)
res$method.random <-
expandmerge(meta1$method.random, meta2$method.random,
nr1 = nran1, nr2 = nran2)
##
## Number of studies
##
res$k <-
expandmerge(meta1$k, meta2$k,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$k.all <-
expandmerge(meta1$k.all, meta2$k.all,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$k.MH <-
expandmerge(meta1$k.MH, meta2$k.MH,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$k.study <-
expandmerge(meta1$k.study, meta2$k.study,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$k.TE <-
expandmerge(meta1$k.TE, meta2$k.TE,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$k0 <-
expandmerge(meta1$k0, meta2$k0,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
##
## Common effect model
##
res$TE.common <- c(meta1$TE.common, meta2$TE.common)
res$seTE.common <- c(meta1$seTE.common, meta2$seTE.common)
res$statistic.common <- c(meta1$statistic.common, meta2$statistic.common)
res$pval.common <- c(meta1$pval.common, meta2$pval.common)
res$lower.common <- c(meta1$lower.common, meta2$lower.common)
res$upper.common <- c(meta1$upper.common, meta2$upper.common)
res$zval.common <- c(meta1$zval.common, meta2$zval.common)
res$text.common <- c(meta1$text.common, meta2$text.common)
##
## Random effects model
##
res$TE.random <- c(meta1$TE.random, meta2$TE.random)
res$seTE.random <- c(meta1$seTE.random, meta2$seTE.random)
res$statistic.random <- c(meta1$statistic.random, meta2$statistic.random)
res$pval.random <- c(meta1$pval.random, meta2$pval.random)
res$method.random.ci <- c(meta1$method.random.ci, meta2$method.random.ci)
res$df.random <- c(meta1$df.random, meta2$df.random)
res$lower.random <- c(meta1$lower.random, meta2$lower.random)
res$upper.random <- c(meta1$upper.random, meta2$upper.random)
res$zval.random <- c(meta1$zval.random, meta2$zval.random)
res$seTE.classic <- c(meta1$seTE.classic, meta2$seTE.classic)
res$adhoc.hakn.ci <- c(meta1$adhoc.hakn.ci, meta2$adhoc.hakn.ci)
res$df.hakn <- c(meta1$df.hakn, meta2$df.hakn)
res$seTE.hakn.ci <- c(meta1$seTE.hakn.ci, meta2$seTE.hakn.ci)
res$seTE.hakn.adhoc.ci <-
c(meta1$seTE.hakn.adhoc.ci, meta2$seTE.hakn.adhoc.ci)
res$df.kero <- c(meta1$df.kero, meta2$df.kero)
res$seTE.kero <- c(meta1$seTE.kero, meta2$seTE.kero)
res$text.random <- c(meta1$text.random, meta2$text.random)
##
## Prediction interval
##
res$method.predict <- c(meta1$method.predict, meta2$method.predict)
res$adhoc.hakn.pi <- c(meta1$adhoc.hakn.pi, meta2$adhoc.hakn.pi)
res$seTE.predict <- c(meta1$seTE.predict, meta2$seTE.predict)
res$df.predict <- c(meta1$df.predict, meta2$df.predict)
res$lower.predict <- c(meta1$lower.predict, meta2$lower.predict)
res$upper.predict <- c(meta1$upper.predict, meta2$upper.predict)
res$seTE.hakn.pi <- c(meta1$seTE.hakn.pi, meta2$seTE.hakn.pi)
res$seTE.hakn.adhoc.pi <-
c(meta1$seTE.hakn.adhoc.pi, meta2$seTE.hakn.adhoc.pi)
res$text.predict <- c(meta1$text.predict, meta2$text.predict)
##
## Heterogeneity statistics
##
Qlabel1 <- if (missing.hetlabel1) names(meta1$Q) else hetlabel1
Qlabel2 <- if (missing.hetlabel2) names(meta2$Q) else hetlabel2
##
if (keep.Q) {
res$Q <- mergevars(meta1$Q, meta2$Q, Qlabel1, Qlabel2)
res$df.Q <- mergevars(meta1$df.Q, meta2$df.Q)
res$pval.Q <- mergevars(meta1$pval.Q, meta2$pval.Q)
}
##
if (keep.I2) {
res$I2 <- mergevars(meta1$I2, meta2$I2, Qlabel1, Qlabel2)
res$lower.I2 <- mergevars(meta1$lower.I2, meta2$upper.I2)
res$upper.I2 <- mergevars(meta1$lower.I2, meta2$upper.I2)
##
res$H <- mergevars(meta1$H, meta2$H, Qlabel1, Qlabel2)
res$lower.H <- mergevars(meta1$lower.H, meta2$upper.H)
res$upper.H <- mergevars(meta1$lower.H, meta2$upper.H)
##
res$Rb <- mergevars(meta1$Rb, meta2$Rb, Qlabel1, Qlabel2)
res$lower.Rb <- mergevars(meta1$lower.Rb, meta2$upper.Rb)
res$upper.Rb <- mergevars(meta1$lower.Rb, meta2$upper.Rb)
}
##
## Trim-and-fill method
##
expandmerge(meta1$method.tau, meta2$method.tau,
nr1 = nran1, nr2 = nran2)
res$left <-
expandmerge(meta1$left, meta2$left,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$ma.common <-
expandmerge(meta1$ma.common, meta2$ma.common,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$type <-
expandmerge(meta1$type, meta2$type,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$n.iter.max <-
expandmerge(meta1$n.iter.max, meta2$n.iter.max,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
res$n.iter <-
expandmerge(meta1$n.iter, meta2$n.iter,
nc1 = ncom1, nc2 = ncom2, nr1 = nran1, nr2 = nran2)
##
## Subgroup analyses
##
res$TE.common.w <- mergevars(meta1$TE.common.w, meta2$TE.common.w)
res$seTE.common.w <- mergevars(meta1$seTE.common.w, meta2$seTE.common.w)
res$statistic.common.w <-
mergevars(meta1$statistic.common.w, meta2$statistic.common.w)
res$pval.common.w <- mergevars(meta1$pval.common.w, meta2$pval.common.w)
res$lower.common.w <- mergevars(meta1$lower.common.w, meta2$lower.common.w)
res$upper.common.w <- mergevars(meta1$upper.common.w, meta2$upper.common.w)
##
res$w.common.w <- mergevars(meta1$w.common.w, meta2$w.common.w)
##
res$Q.w.common <- mergevars(meta1$Q.w.common, meta2$Q.w.common)
res$pval.Q.w.common <-
mergevars(meta1$pval.Q.w.common, meta2$pval.Q.w.common)
##
res$Q.b.common <- mergevars(meta1$Q.b.common, meta2$Q.b.common)
res$df.Q.b.common <- mergevars(meta1$df.Q.b.common, meta2$df.Q.b.common)
res$pval.Q.b.common <-
mergevars(meta1$pval.Q.b.common, meta2$pval.Q.b.common)
##
res$TE.random.w <- mergevars(meta1$TE.random.w, meta2$TE.random.w)
res$seTE.random.w <- mergevars(meta1$seTE.random.w, meta2$seTE.random.w)
res$statistic.random.w <-
mergevars(meta1$statistic.random.w, meta2$statistic.random.w)
res$pval.random.w <- mergevars(meta1$pval.random.w, meta2$pval.random.w)
res$df.random.w <- list(meta1$df.random.w, meta2$df.random.w)
res$lower.random.w <- mergevars(meta1$lower.random.w, meta2$lower.random.w)
res$upper.random.w <- mergevars(meta1$upper.random.w, meta2$upper.random.w)
res$df.hakn.w <- list(meta1$df.hakn.w, meta2$df.hakn.w)
res$df.kero.w <- list(meta1$df.kero.w, meta2$df.kero.w)
##
res$w.random.w <- mergevars(meta1$w.random.w, meta2$w.random.w)
##
res$Q.w.random <- mergevars(meta1$Q.w.random, meta2$Q.w.random)
res$pval.Q.w.random <-
mergevars(meta1$pval.Q.w.random, meta2$pval.Q.w.random)
##
res$Q.b.random <- list(meta1$Q.b.random, meta2$Q.b.random)
res$df.Q.b.random <- list(meta1$df.Q.b.random, meta2$df.Q.b.random)
res$pval.Q.b.random <- mergevars(meta1$pval.Q.b.random, meta2$pval.Q.b.random)
##
res$seTE.predict.w <- mergevars(meta1$seTE.predict.w, meta2$seTE.predict.w)
res$df.predict.w <- list(meta1$df.predict.w, meta2$df.predict.w)
res$lower.predict.w <- mergevars(meta1$lower.predict.w, meta2$lower.predict.w)
res$upper.predict.w <- mergevars(meta1$upper.predict.w, meta2$upper.predict.w)
##
##
## (7) More settings
##
##
taulabel1 <- if (missing.taulabel1) names(meta1$tau) else taulabel1
taulabel2 <- if (missing.taulabel2) names(meta2$tau) else taulabel2
##
names(meta1$tau) <- names(meta1$tau2) <- taulabel1
names(meta2$tau) <- names(meta2$tau2) <- taulabel2
##
if (!random1 & random2) {
res$method.tau <- meta2$method.tau
res$method.tau.ci <- meta2$method.tau.ci
res$detail.tau <- meta2$detail.tau
##
res$tau <- meta2$tau
names(res$tau) <- taulabel2
res$lower.tau <- meta2$lower.tau
res$upper.tau <- meta2$upper.tau
##
res$tau2 <- meta2$tau2
names(res$tau2) <- taulabel2
res$lower.tau2 <- meta2$lower.tau2
res$upper.tau2 <- meta2$upper.tau2
##
res$se.tau <- meta2$se.tau
##
res$tau.preset <- meta2$tau.preset
##
res$Q.Cochrane <- meta2$Q.Cochrane
}
else if (random1 & random2) {
res$method.tau <-
expandmerge(meta1$method.tau, meta2$method.tau,
nr1 = nran1, nr2 = nran2)
res$method.tau.ci <-
expandmerge(meta1$method.tau.ci, meta2$method.tau.ci,
nr1 = nran1, nr2 = nran2)
##
res$detail.tau <- c(meta1$detail.tau, meta2$detail.tau)
##
if (!inherits(meta1, "metamerge") && meta1$three.level) {
meta1$tau2 <- sum(meta1$tau2)
meta1$tau <- sqrt(meta1$tau2)
meta1$lower.tau <- meta1$upper.tau <-
meta1$lower.tau2 <- meta1$upper.tau2 <- NA
meta1$sign.lower.tau <- meta1$sign.upper.tau <- ""
}
##
if (!inherits(meta2, "metamerge") && meta2$three.level) {
meta2$tau2 <- sum(meta2$tau2)
meta2$tau <- sqrt(meta2$tau2)
meta2$lower.tau <- meta2$upper.tau <-
meta2$lower.tau2 <- meta2$upper.tau2 <- NA
meta2$sign.lower.tau <- meta2$sign.upper.tau <- ""
}
##
res$tau <- expandmerge(meta1$tau, meta2$tau, nr1 = nran1, nr2 = nran2)
res$lower.tau <-
expandmerge(meta1$lower.tau, meta2$lower.tau, nr1 = nran1, nr2 = nran2)
res$upper.tau <-
expandmerge(meta1$upper.tau, meta2$upper.tau, nr1 = nran1, nr2 = nran2)
##
res$tau2 <- expandmerge(meta1$tau2, meta2$tau2, nr1 = nran1, nr2 = nran2)
res$lower.tau2 <- expandmerge(meta1$lower.tau2, meta2$lower.tau2,
nr1 = nran1, nr2 = nran2)
res$upper.tau2 <- expandmerge(meta1$upper.tau2, meta2$upper.tau2,
nr1 = nran1, nr2 = nran2)
##
res$se.tau <-
expandmerge(meta1$se.tau, meta2$se.tau, nr1 = nran1, nr2 = nran2)
##
res$tau.preset <-
expandmerge(meta1$tau.preset, meta2$tau.preset, nr1 = nran1, nr2 = nran2)
##
res$Q.Cochrane <-
expandmerge(meta1$Q.Cochrane, meta2$Q.Cochrane, nr1 = nran1, nr2 = nran2)
}
##
res$sm <- if (meta1$sm != meta2$sm) "" else meta1$sm
##
res$common <- common
res$random <- random
res$overall <- overall
res$overall.hetstat <- overall.hetstat
res$prediction <- prediction
##
res$backtransf <- backtransf
##
## Three-level model
##
res$three.level <-
expandmerge(meta1$three.level, meta2$three.level,
ncom1, nran1, ncom2, nran2)
if (!random1 & random2)
res$cluster <- meta2$cluster
##
## Additional arguments from metabin(), metainc(), metaprop()
##
res$incr <- expandmerge(meta1$incr, meta2$incr,
ncom1, nran1, ncom2, nran2)
res$method.incr <-
expandmerge(meta1$method.incr, meta2$method.incr,
ncom1, nran1, ncom2, nran2)
res$sparse <-
expandmerge(meta1$sparse, meta2$sparse,
ncom1, nran1, ncom2, nran2)
##
## Additional arguments from metabin()
##
res$allstudies <-
expandmerge(meta1$allstudies, meta2$allstudies,
ncom1, nran1, ncom2, nran2)
res$doublezeros <-
expandmerge(meta1$doublezeros, meta2$doublezeros,
ncom1, nran1, ncom2, nran2)
res$MH.exact <-
expandmerge(meta1$MH.exact, meta2$MH.exact,
ncom1, nran1, ncom2, nran2)
res$RR.Cochrane <-
expandmerge(meta1$RR.Cochrane, meta2$RR.Cochrane,
ncom1, nran1, ncom2, nran2)
res$model.glmm <-
expandmerge(meta1$model.glmm, meta2$model.glmm,
ncom1, nran1, ncom2, nran2)
##
## Additional arguments from metacont()
##
res$pooledvar <- expandmerge(meta1$pooledvar, meta2$pooledvar,
ncom1, nran1, ncom2, nran2)
res$method.smd <- expandmerge(meta1$method.smd, meta2$method.smd,
ncom1, nran1, ncom2, nran2)
res$sd.glass <- expandmerge(meta1$sd.glass, meta2$sd.glass,
ncom1, nran1, ncom2, nran2)
res$exact.smd <- expandmerge(meta1$exact.smd, meta2$exact.smd,
ncom1, nran1, ncom2, nran2)
res$method.mean <- expandmerge(meta1$method.mean, meta2$method.mean,
ncom1, nran1, ncom2, nran2)
res$method.sd <- expandmerge(meta1$method.sd, meta2$method.sd,
ncom1, nran1, ncom2, nran2)
##
##
## (8) Backward compatibility
##
##
res <- backward(res)
##
class(res) <- c(class(res), "metamerge")
res
}
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