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#' Summary method for objects of class netcomb
#'
#' @description
#' Summary method for objects of class \code{netcomb}.
#'
#' @param object An object of class \code{netcomb}.
#' @param common A logical indicating whether results for the common
#' effects model should be printed.
#' @param random A logical indicating whether results for the random
#' effects model should be printed.
#' @param backtransf A logical indicating whether results should be
#' back transformed in printouts and forest plots.
#' @param nchar.comps A numeric defining the minimum number of
#' characters used to create unique component names.
#' @param warn.deprecated A logical indicating whether warnings should
#' be printed if deprecated arguments are used.
#' @param \dots Additional arguments (to catch deprecated arguments).
#'
#' @return
#' A list is returned with the same elements as a
#' \code{\link{netcomb}} object.
#'
#' @author Guido Schwarzer \email{guido.schwarzer@@uniklinik-freiburg.de}
#'
#' @seealso \code{\link{netcomb}}, \code{\link{discomb}}
#'
#' @keywords print
#'
#' @examples
#' data(Linde2016)
#'
#' # Only consider studies including Face-to-face PST (to reduce
#' # runtime of example)
#' #
#' face <- subset(Linde2016, id %in% c(16, 24, 49, 118))
#'
#' # Conduct random effects network meta-analysis
#' #
#' net1 <- netmeta(lnOR, selnOR, treat1, treat2, id,
#' data = face, reference.group = "placebo",
#' sm = "OR", common = FALSE)
#'
#' # Additive model for treatment components
#' #
#' nc1 <- netcomb(net1)
#' summary(nc1)
#' print(summary(nc1), digits = 2, digits.stat = 3)
#'
#' \dontrun{
#' # Conduct random effects network meta-analysis
#' #
#' net2 <- netmeta(lnOR, selnOR, treat1, treat2, id,
#' data = Linde2016, reference.group = "placebo",
#' sm = "OR", common = FALSE)
#'
#' # Additive model for treatment components
#' #
#' nc2 <- netcomb(net2)
#' summary(nc2)
#' print(summary(nc2), digits = 2, digits.stat = 3)
#' }
#'
#' @method summary netcomb
#' @export
summary.netcomb <- function(object,
common = object$common,
random = object$random,
backtransf = object$backtransf,
nchar.comps = object$nchar.comps,
warn.deprecated = gs("warn.deprecated"),
...) {
##
##
## (1) Check for netcomb object
##
##
chkclass(object, "netcomb")
object <- updateversion(object)
##
##
## (2) Check other arguments
##
##
chklogical(backtransf)
nchar.comps <- replaceNULL(nchar.comps, 666)
chknumeric(nchar.comps, min = 1, length = 1)
##
args <- list(...)
chklogical(warn.deprecated)
##
missing.common <- missing(common)
common <- deprecated(common, missing.common, args, "comb.fixed",
warn.deprecated)
common <- deprecated(common, missing.common, args, "fixed",
warn.deprecated)
chklogical(common)
##
random <-
deprecated(random, missing(random), args, "comb.random", warn.deprecated)
chklogical(random)
##
##
## (3) Summarise results for individual studies and network
## meta-analyses
##
##
keepvars <- c("TE", "seTE", "lower", "upper", "statistic", "p")
##
ci.comp <- data.frame(studlab = object$studlab,
treat1 = object$treat1, treat2 = object$treat2,
ci(object$TE, object$seTE, object$level)[keepvars],
stringsAsFactors = FALSE)
##
ci.nma.common <- data.frame(studlab = object$studlab,
treat1 = object$treat1,
treat2 = object$treat2,
TE = object$TE.nma.common,
seTE = object$seTE.nma.common,
lower = object$lower.nma.common,
upper = object$upper.nma.common,
statistic = object$statistic.nma.common,
p = object$pval.nma.common,
stringsAsFactors = FALSE)
##
ci.cnma.common <- data.frame(studlab = object$studlab,
treat1 = object$treat1,
treat2 = object$treat2,
TE = object$TE.cnma.common,
seTE = object$seTE.cnma.common,
lower = object$lower.cnma.common,
upper = object$upper.cnma.common,
statistic = object$statistic.cnma.common,
p = object$pval.cnma.common,
stringsAsFactors = FALSE)
##
ci.nma.random <- data.frame(studlab = object$studlab,
treat1 = object$treat1,
treat2 = object$treat2,
TE = object$TE.nma.random,
seTE = object$seTE.nma.random,
lower = object$lower.nma.random,
upper = object$upper.nma.random,
statistic = object$statistic.nma.random,
p = object$pval.nma.random,
stringsAsFactors = FALSE)
##
ci.cnma.random <- data.frame(studlab = object$studlab,
treat1 = object$treat1,
treat2 = object$treat2,
TE = object$TE.cnma.random,
seTE = object$seTE.cnma.random,
lower = object$lower.cnma.random,
upper = object$upper.cnma.random,
statistic = object$statistic.cnma.random,
p = object$pval.cnma.random,
stringsAsFactors = FALSE)
##
ci.f <- list(TE = object$TE.common,
seTE = object$seTE.common,
lower = object$lower.common,
upper = object$upper.common,
statistic = object$statistic.common,
p = object$pval.common)
##
ci.r <- list(TE = object$TE.random,
seTE = object$seTE.random,
lower = object$lower.random,
upper = object$upper.random,
statistic = object$statistic.random,
p = object$pval.random)
##
ci.comp.f <- data.frame(TE = object$Comp.common,
seTE = object$seComp.common,
lower = object$lower.Comp.common,
upper = object$upper.Comp.common,
statistic = object$statistic.Comp.common,
p = object$pval.Comp.common,
stringsAsFactors = FALSE)
rownames(ci.comp.f) <- object$comps
##
ci.comp.r <- data.frame(TE = object$Comp.random,
seTE = object$seComp.random,
lower = object$lower.Comp.random,
upper = object$upper.Comp.random,
statistic = object$statistic.Comp.random,
p = object$pval.Comp.random,
stringsAsFactors = FALSE)
rownames(ci.comp.r) <- object$comps
##
ci.comb.f <- data.frame(TE = object$Comb.common,
seTE = object$seComb.common,
lower = object$lower.Comb.common,
upper = object$upper.Comb.common,
statistic = object$statistic.Comb.common,
p = object$pval.Comb.common,
stringsAsFactors = FALSE)
rownames(ci.comb.f) <- object$trts
##
ci.comb.r <- data.frame(TE = object$Comb.random,
seTE = object$seComb.random,
lower = object$lower.Comb.random,
upper = object$upper.Comb.random,
statistic = object$statistic.Comb.random,
p = object$pval.Comb.random,
stringsAsFactors = FALSE)
rownames(ci.comb.r) <- object$trts
##
##
## (4) Create summary.netmeta object
##
##
res <- list(k = object$k,
m = object$m,
n = object$n,
d = object$d,
c = object$c,
s = object$s,
##
trts = object$trts,
k.trts = object$k.trts,
n.trts = object$n.trts,
events.trts = object$events.trts,
##
studies = object$studies,
narms = object$narms,
##
designs = object$designs,
##
comps = object$comps,
k.comps = object$k.comps,
n.comps = object$n.comps,
events.comps = object$events.comps,
##
comparison = ci.comp,
comparison.nma.common = ci.nma.common,
comparison.nma.random = ci.nma.random,
comparison.cnma.common = ci.cnma.common,
comparison.cnma.random = ci.cnma.random,
##
components.common = ci.comp.f,
components.random = ci.comp.r,
##
combinations.common = ci.comb.f,
combinations.random = ci.comb.r,
##
common = ci.f, random = ci.r,
##
Q.additive = object$Q.additive,
df.Q.additive = object$df.Q.additive,
pval.Q.additive = object$pval.Q.additive,
tau = object$tau,
I2 = object$I2,
lower.I2 = object$lower.I2, upper.I2 = object$upper.I2,
##
Q.standard = object$Q.standard,
df.Q.standard = object$df.Q.standard,
pval.Q.standard = object$pval.Q.standard,
##
Q.diff = object$Q.diff,
df.Q.diff = object$df.Q.diff,
pval.Q.diff = object$pval.Q.diff,
##
sm = object$sm,
method = object$method,
level = object$level,
level.ma = object$level.ma,
##
ci.lab = paste0(round(100 * object$level.ma, 1),"%-CI"),
##
reference.group = object$reference.group,
baseline.reference = object$baseline.reference,
all.treatments = object$all.treatments,
seq = object$seq,
##
tau.preset = object$tau.preset,
##
sep.comps = object$sep.comps,
nchar.comps = nchar.comps,
##
inactive = object$inactive,
sep.comps = object$sep.comps,
##
backtransf = backtransf,
##
title = object$title,
##
x = object,
##
call = match.call(),
version = packageDescription("netmeta")$Version
)
##
res$x$common <- common
res$x$random <- random
##
class(res) <- c(if (inherits(object, "discomb")) "summary.discomb",
"summary.netcomb")
res
}
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