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R/pairwise.R
In netmeta: Network Meta-Analysis using Frequentist Methods
Defines functions
pairwise
Documented in
pairwise
#' Transform meta-analysis data from two arm-based formats into
#' contrast-based format
#'
#' @description
#' This function transforms data that are given in wide or long
#' arm-based format (e.g. input format for WinBUGS) to a
#' contrast-based format that is needed as input to R function
#' \code{\link{netmeta}}. The function can transform data with binary,
#' continuous, or generic outcomes as well as incidence rates from
#' arm-based to contrast-based format.
#'
#' @param treat A list or vector with treatment information for
#' individual treatment arms (see Details).
#' @param event A list or vector with information on number of events
#' for individual treatment arms (see Details).
#' @param n A list or vector with information on number of
#' observations for individual treatment arms (see Details).
#' @param mean A list or vector with estimated means for individual
#' treatment arms (see Details).
#' @param sd A list or vector with information on the standard
#' deviation for individual treatment arms (see Details).
#' @param TE A list or vector with estimated treatment effects for
#' individual treatment arms (see Details).
#' @param seTE A list or vector with standard errors of estimated
#' treatment effect for individual treatment arms (see Details).
#' @param time A list or vector with information on person time at
#' risk for individual treatment arms (see Details).
#' @param data An optional data frame containing the study
#' information.
#' @param studlab A vector with study labels (optional).
#' @param incr A numerical value which is added to cell frequencies
#' for studies with a zero cell count, see Details.
#' @param allincr A logical indicating if \code{incr} is added to cell
#' frequencies of all studies if at least one study has a zero cell
#' count. If FALSE (default), \code{incr} is added only to cell
#' frequencies of studies with a zero cell count.
#' @param addincr A logical indicating if \code{incr} is added to cell
#' frequencies of all studies irrespective of zero cell counts.
#' @param allstudies A logical indicating if studies with zero or all
#' events in two treatment arms are to be included in the
#' meta-analysis (applies only if \code{sm} is equal to \code{"RR"}
#' or \code{"OR"}).
#' @param reference.group Reference treatment (first treatment is used
#' if argument is missing).
#' @param keep.all.comparisons A logical indicating whether all
#' pairwise comparisons or only comparisons with the study-specific
#' reference group should be kept ('basic parameters').
#' @param append A logical indicating whether variables from data set
#' provided in argument \code{data} are appended to data set with
#' pairwise comparisons.
#' @param warn A logical indicating whether warnings should be printed
#' (e.g., if studies are excluded due to only providing a single
#' treatment arm).
#' @param \dots Additional arguments passed-through to the functions
#' to calculate effects.
#'
#' @details
#' R function \code{\link{netmeta}} expects data in a
#' \bold{contrast-based format}, where each row corresponds to a
#' comparison of two treatments and contains a measure of the
#' treatment effect comparing two treatments with standard error,
#' labels for the two treatments and an optional study label. In
#' contrast-based format, a three-arm study contributes three rows
#' with treatment comparison and corresponding standard error for
#' pairwise comparison \emph{A} vs \emph{B}, \emph{A} vs \emph{C}, and
#' \emph{B} vs \emph{C} whereas a four-arm study contributes six rows
#' / pairwise comparisons: \emph{A} vs \emph{B}, \emph{A} vs \emph{C},
#' \dots{}, \emph{C} vs \emph{D}.
#'
#' Other programs for network meta-analysis in WinBUGS and Stata
#' require data in an \emph{arm-based} format, i.e. treatment estimate
#' for each treatment arm instead of a difference of two treatments. A
#' common \bold{(wide) arm-based format} consists of one data row per
#' study, containing treatment and other necessary information for all
#' study arms. For example, a four-arm study contributes one row with
#' four treatment estimates and corresponding standard errors for
#' treatments \emph{A}, \emph{B}, \emph{C}, and \emph{D}. Another
#' possible arm-based format is a long format where each row
#' corresponds to a single study arm. Accordingly, in the \bold{long
#' arm-based format} a study contributes as many rows as treatments
#' considered in the study.
#'
#' The pairwise function transforms data given in (wide or long)
#' arm-based format into the contrast-based format which consists of
#' \emph{pairwise} comparisons and is needed as input to the
#' \code{\link{netmeta}} function.
#'
#' The pairwise function can transform data with binary outcomes
#' (using the \code{\link{metabin}} function from R package meta),
#' continuous outcomes (\code{\link{metacont}} function), incidence
#' rates (\code{\link{metainc}} function), and generic outcomes
#' (\code{\link{metagen}} function). Depending on the outcome, the
#' following arguments are mandatory:
#' \itemize{
#' \item treat, event, n (see \code{\link{metabin}});
#' \item treat, n, mean, sd (see \code{\link{metacont}});
#' \item treat, event, time (see \code{\link{metainc}});
#' \item treat, TE, seTE (see \code{\link{metagen}}).
#' }
#'
#' Argument \code{treat} is mandatory to identify the individual
#' treatments. The other arguments contain outcome specific
#' data. These arguments must be either lists (wide arm-based format,
#' i.e., one row per study) or vectors (long arm-based format,
#' i.e., multiple rows per study) of the same length.
#'
#' For the wide arm-based format, each list consists of as many
#' vectors of the same length as the multi-arm study with the largest
#' number of treatments. If a single multi-arm study has five arms,
#' five vectors have to be provided for each lists. Two-arm studies
#' have entries with \code{NA} for the third and subsequent
#' vectors. Each list entry is a vector with information for each
#' individual study; i.e., the length of this vector corresponds to
#' the total number of studies incorporated in the network
#' meta-analysis. Typically, list elements are part of a data frame
#' (argument \code{data}, optional); see Examples. An optional vector
#' with study labels can be provided which can be part of the data
#' frame.
#'
#' In the long arm-based format, argument \code{studlab} is mandatory
#' to identify rows contributing to individual studies.
#'
#' Additional arguments for meta-analysis functions can be provided
#' using argument '\dots'. The most important argument is \code{sm}
#' defining the summary measure. More information on this and other
#' arguments is given in the help pages of R functions
#' \code{\link{metabin}}, \code{\link{metacont}},
#' \code{\link{metainc}}, and \code{\link{metagen}}, respectively.
#'
#' For standardised mean differences (argument \code{sm = "SMD"}),
#' equations (4) and (5) in Crippa & Orsini (2016) are used to
#' calculated SMDs and standard errors. These equations guarantee
#' consistent SMDs and standard errors for multi-arm studies. Note,
#' the summary measure is actually Cohen's d as Hedges' g is not
#' consistent in multi-arm studies.
#'
#' For binary outcomes, 0.5 is added to all cell frequencies (odds
#' ratio) or only the number of events (risk ratio) for studies with a
#' zero cell count. For odds ratio and risk ratio, treatment estimates
#' and standard errors are only calculated for studies with zero or
#' all events in both groups if \code{allstudies} is \code{TRUE}. This
#' continuity correction is used both to calculate individual study
#' results with confidence limits and to conduct meta-analysis based
#' on the inverse variance method. For the risk difference, 0.5 is
#' only added to all cell frequencies to calculate the standard error.
#'
#' For incidence rates, 0.5 is added to all cell frequencies for the
#' incidence rate ratio as summary measure. For the incidence risk
#' difference, 0.5 is only added to all cell frequencies to calculate
#' the standard error.
#'
#' The value of pairwise is a data frame with as many rows as there
#' are pairwise comparisons. For each study with \emph{p} treatments,
#' \emph{p*(p-1) / 2} contrasts are generated. Each row contains the
#' treatment effect (\code{TE}), its standard error (\code{seTE}), the
#' treatments compared ((\code{treat1}), (\code{treat2})) and the
#' study label ((\code{studlab})). Further columns are added according
#' to type of data.
#'
#' All variables from the original dataset are also part of the output
#' dataset. If data are provided in the long arm-based format, the
#' value of a variable can differ between treatment arms; for example,
#' the mean age or percentage of women in the treatment arm. In this
#' situation, two variables instead of one variable will be included
#' in the output dataset. The values "1" and "2" are added to the
#' names for these variables, e.g. "mean.age1" and "mean.age2" for the
#' mean age.
#'
#' In general, any variable names in the original dataset that are
#' identical to the main variable names (i.e., "TE", "seTE", ...) will
#' be renamed to variable names with ending ".orig".
#'
#' A reduced data set with basic comparisons (Rücker & Schwarzer,
#' 2014) can be generated using argument \code{keep.all.comparisons =
#' FALSE}. Furthermore, the reference group for the basic comparisons
#' can be specified with argument \code{reference.group}.
#'
#' @note
#' This function must not be confused with \code{\link{netpairwise}}
#' which can be used to conduct pairwise meta-analyses for all
#' comparisons with direct evidence in a network meta-analysis.
#'
#' @return
#' A data frame with the following columns:
#' \item{TE}{Treatment estimate comparing treatment 'treat1' and
#' 'treat2'.}
#' \item{seTE}{Standard error of treatment estimate.}
#' \item{studlab}{Study labels.}
#' \item{treat1}{First treatment in comparison.}
#' \item{treat2}{Second treatment in comparison.}
#' \item{event1}{Number of events for first treatment arm (for metabin
#' and metainc).}
#' \item{event2}{Number of events for second treatment arm (for
#' metabin and metainc).}
#' \item{n1}{Number of observations for first treatment arm (for
#' metabin and metacont).}
#' \item{n2}{Number of observations for second treatment arm (for
#' metabin and metacont).}
#' \item{mean1}{Estimated mean for first treatment arm (for
#' metacont).}
#' \item{mean2}{Estimated mean for second treatment arm (for
#' metacont).}
#' \item{sd1}{Standard deviation for first treatment arm (for
#' metacont).}
#' \item{sd2}{Standard deviation for second treatment arm (for
#' metacont).}
#' \item{TE1}{Estimated treatment effect for first treatment arm (for
#' metagen).}
#' \item{TE2}{Estimated treatment effect for second treatment arm (for
#' metagen).}
#' \item{seTE1}{Standard error of estimated treatment effect for first
#' treatment arm (for metagen).}
#' \item{seTE2}{Standard error of estimated treatment effect for
#' second treatment arm (for metagen).}
#' \item{time1}{Person time at risk for first treatment arm (for
#' metainc).}
#' \item{time2}{Person time at risk for second treatment arm (for
#' metainc).}
#'
#' All variables from the original dataset are also part of the output
#' dataset; see Details.
#'
#' @author Gerta Rücker\email{gerta.ruecker@@uniklinik-freiburg.de}, Guido
#' Schwarzer \email{guido.schwarzer@@uniklinik-freiburg.de}
#'
#' @seealso \code{\link{netmeta}}, \code{\link{metacont}},
#' \code{\link{metagen}}, \code{\link{metabin}},
#' \code{\link{metainc}}, \code{\link{netgraph.netmeta}},
#' \code{\link{pairwise}}
#'
#' @references
#' Crippa A, Orsini N (2016):
#' Dose-response meta-analysis of differences in means.
#' \emph{BMC Medical Research Methodology},
#' \bold{16}:91.
#'
#' @keywords datagen
#'
#' @examples
#' # Example using continuous outcomes (internal call of function
#' # metacont)
#' #
#' data(Franchini2012)
#' # Transform data from arm-based format to contrast-based format
#' p1 <- pairwise(list(Treatment1, Treatment2, Treatment3),
#' n = list(n1, n2, n3),
#' mean = list(y1, y2, y3), sd = list(sd1, sd2, sd3),
#' data = Franchini2012, studlab = Study)
#' p1
#'
#' \dontrun{
#' # Conduct network meta-analysis
#' #
#' net1 <- netmeta(p1)
#' net1
#'
#' # Draw network graphs
#' #
#' netgraph(net1, points = TRUE, cex.points = 3, cex = 1.5,
#' thickness = "se.common")
#' netgraph(net1, points = TRUE, cex.points = 3, cex = 1.5,
#' plastic = TRUE, thickness = "se.common",
#' iterate = TRUE)
#' netgraph(net1, points = TRUE, cex.points = 3, cex = 1.5,
#' plastic = TRUE, thickness = "se.common",
#' iterate = TRUE, start = "eigen")
#'
#' # Example using generic outcomes (internal call of function
#' # metagen)
#' #
#' # Calculate standard error for means y1, y2, y3
#' Franchini2012$se1 <- with(Franchini2012, sqrt(sd1^2 / n1))
#' Franchini2012$se2 <- with(Franchini2012, sqrt(sd2^2 / n2))
#' Franchini2012$se3 <- with(Franchini2012, sqrt(sd3^2 / n3))
#' # Transform data from arm-based format to contrast-based format
#' # using means and standard errors (note, argument 'sm' has to be
#' # used to specify that argument 'TE' is a mean difference)
#' p2 <- pairwise(list(Treatment1, Treatment2, Treatment3),
#' TE = list(y1, y2, y3), seTE = list(se1, se2, se3),
#' n = list(n1, n2, n3),
#' data = Franchini2012, studlab = Study,
#' sm = "MD")
#' p2
#'
#' # Compare pairwise objects p1 (based on continuous outcomes) and p2
#' # (based on generic outcomes)
#' #
#' all.equal(p1[, c("TE", "seTE", "studlab", "treat1", "treat2")],
#' p2[, c("TE", "seTE", "studlab", "treat1", "treat2")])
#'
#' # Same result as network meta-analysis based on continuous outcomes
#' # (object net1)
#' net2 <- netmeta(p2)
#' net2
#'
#' # Example with binary data
#' #
#' data(smokingcessation)
#' # Transform data from arm-based format to contrast-based format
#' # (interal call of metabin function). Argument 'sm' has to be used
#' # for odds ratio as risk ratio (sm = "RR") is default of metabin
#' # function.
#' #
#' p3 <- pairwise(list(treat1, treat2, treat3),
#' list(event1, event2, event3), list(n1, n2, n3),
#' data = smokingcessation,
#' sm = "OR")
#' p3
#'
#' # Conduct network meta-analysis
#' #
#' net3 <- netmeta(p3)
#' net3
#'
#' # Example with incidence rates
#' #
#' data(dietaryfat)
#'
#' # Transform data from arm-based format to contrast-based format
#' #
#' p4 <- pairwise(list(treat1, treat2, treat3),
#' list(d1, d2, d3), time = list(years1, years2, years3),
#' studlab = ID,
#' data = dietaryfat)
#' p4
#'
#' # Conduct network meta-analysis using incidence rate ratios (sm =
#' # "IRR"). Note, the argument 'sm' is not necessary as this is the
#' # default in R function metainc called internally.
#' #
#' net4 <- netmeta(p4, sm = "IRR")
#' summary(net4)
#'
#' # Example with long data format
#' #
#' data(Woods2010)
#'
#' # Transform data from long arm-based format to contrast-based
#' # format Argument 'sm' has to be used for odds ratio as summary
#' # measure; by default the risk ratio is used in the metabin
#' # function called internally.
#' #
#' p5 <- pairwise(treatment, event = r, n = N,
#' studlab = author, data = Woods2010, sm = "OR")
#' p5
#'
#' # Conduct network meta-analysis
#' net5 <- netmeta(p5)
#' net5
#' }
#'
#' @export pairwise
pairwise <- function(treat,
event, n, mean, sd, TE, seTE, time,
data = NULL, studlab,
incr = gs("incr"), allincr = gs("allincr"),
addincr = gs("addincr"), allstudies = gs("allstudies"),
##
reference.group,
keep.all.comparisons,
##
append = !is.null(data),
warn = FALSE,
...) {
##
##
## (1) Check arguments
##
##
chknumeric(incr, min = 0, length = 1)
chklogical(allincr)
chklogical(addincr)
chklogical(allstudies)
chklogical(append)
chklogical(warn)
##
##
## (2) Read data
##
##
nulldata <- is.null(data)
sfsp <- sys.frame(sys.parent())
mc <- match.call()
##
if (nulldata)
data <- sfsp
##
missing.reference.group <- missing(reference.group)
##
## Catch studlab, treat, event, n, mean, sd, time from data:
##
treat <- catch("treat", mc, data, sfsp)
##
if (is.data.frame(treat) & !is.null(attr(treat, "pairwise"))) {
is.pairwise <- TRUE
res <- treat
##
if (missing.reference.group)
if (!is.null(attributes(treat)$reference.group)) {
reference.group <- attributes(treat)$reference.group
missing.reference.group <- FALSE
}
if (missing(keep.all.comparisons)) {
if (!is.null(attributes(treat)$keep.all.comparisons))
keep.all.comparisons <- attributes(treat)$keep.all.comparisons
else
keep.all.comparisons <- TRUE
}
##
if (!missing(event))
warning("Argument 'event' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(n))
warning("Argument 'n' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(mean))
warning("Argument 'mean' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(sd))
warning("Argument 'sd' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(TE))
warning("Argument 'TE' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(seTE))
warning("Argument 'seTE' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(time))
warning("Argument 'time' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!nulldata)
warning("Argument 'data' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(studlab))
warning("Argument 'studlab' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(incr))
warning("Argument 'incr' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(allincr))
warning("Argument 'allincr' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(addincr))
warning("Argument 'addincr' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(allstudies))
warning("Argument 'allstudies' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
##
type <- "pairwise"
}
else {
is.pairwise <- FALSE
##
if (missing(keep.all.comparisons))
keep.all.comparisons <- TRUE
chklogical(keep.all.comparisons)
##
studlab <- catch("studlab", mc, data, sfsp)
event <- catch("event", mc, data, sfsp)
n <- catch("n", mc, data, sfsp)
mean <- catch("mean", mc, data, sfsp)
sd <- catch("sd", mc, data, sfsp)
TE <- catch("TE", mc, data, sfsp)
seTE <- catch("seTE", mc, data, sfsp)
time <- catch("time", mc, data, sfsp)
args <- list(...)
nam.args <- names(args)
if (is.null(treat))
stop("Argument 'treat' mandatory.")
##
if (is.list(treat))
chklist(treat)
##
if (!is.null(event))
if (is.list(event))
chklist(event)
else
chknumeric(event)
##
if (!is.null(n))
if (is.list(n))
chklist(n)
else
chknumeric(n)
##
if (!is.null(mean))
if (is.list(mean))
chklist(mean)
else
chknumeric(mean)
##
if (!is.null(sd))
if (is.list(sd))
chklist(sd)
else
chknumeric(sd)
##
if (!is.null(TE))
if (is.list(TE))
chklist(TE)
else
chknumeric(TE)
##
if (!is.null(seTE))
if (is.list(seTE))
chklist(seTE)
else
chknumeric(seTE)
##
if (!is.null(time))
if (is.list(time))
chklist(time)
else
chknumeric(time)
if (!is.null(TE) & !is.null(seTE))
type <- "generic"
else if (!is.null(event) & !is.null(time) &
is.null(mean) & is.null(sd))
type <- "count"
else if (!is.null(event) & !is.null(n) &
is.null(mean) & is.null(sd))
type <- "binary"
else if (!is.null(n) & !is.null(mean) & !is.null(sd))
type <- "continuous"
else if (is.null(event) & is.null(mean) & is.null(sd) &
is.null(TE) & is.null(seTE) & is.null(time))
type <- "onlytreat"
else
stop("Type of outcome unclear. Please provide the necessary ",
"information:\n - event, n (binary outcome)\n - n, ",
"mean, sd (continuous outcome)\n - TE, seTE (generic outcome)\n",
" - event, time (incidence rates).")
##
## Determine whether data is in wide or long arm-based format
##
if (type == "generic")
wide.armbased <- is.list(TE) & is.list(seTE)
if (type == "binary")
wide.armbased <- is.list(event) & is.list(n)
else if (type == "continuous")
wide.armbased <- is.list(n) & is.list(mean) & is.list(sd)
else if (type == "count")
wide.armbased <- is.list(event) & is.list(time)
else if (type == "onlytreat")
wide.armbased <- is.list(treat)
##
## Transform long arm-based format to list format
##
nulldata <- !(!nulldata & append)
#
if (!wide.armbased) {
if (is.null(studlab))
stop("Argument 'studlab' mandatory if argument 'event' is a vector.")
##
studlab <- as.character(studlab)
##
treat <- as.character(treat)
##
ttab <- table(studlab, treat)
n.arms <- apply(ttab, 1, sum)
max.arms <- max(n.arms)
##
treat.list <- vector("list", max.arms)
event.list <- vector("list", max.arms)
n.list <- vector("list", max.arms)
mean.list <- vector("list", max.arms)
sd.list <- vector("list", max.arms)
TE.list <- vector("list", max.arms)
seTE.list <- vector("list", max.arms)
time.list <- vector("list", max.arms)
##
if (!nulldata)
adddata <- vector("list", max.arms)
##
if (type == "binary") {
##
## Generate lists
##
tdat <- data.frame(studlab, treat, event, n,
.order = seq_along(studlab),
stringsAsFactors = FALSE)
##
if (!nulldata) {
tdat <- cbind(tdat, data)
dupl <- duplicated(names(tdat))
if (any(dupl))
names(tdat)[dupl] <- paste(names(tdat)[dupl], "orig", sep = ".")
}
##
studlab <- names(n.arms)
dat.studlab <- data.frame(studlab, stringsAsFactors = FALSE)
##
for (i in 1:max.arms) {
sel.i <- !duplicated(tdat$studlab)
tdat.i <- merge(dat.studlab, tdat[sel.i, ],
by = "studlab", all.x = TRUE)
##
treat.list[[i]] <- tdat.i$treat
event.list[[i]] <- tdat.i$event
n.list[[i]] <- tdat.i$n
##
tdat.i$event <- NULL
tdat.i$n <- NULL
##
if (!nulldata)
adddata[[i]] <- tdat.i
##
tdat <- tdat[!sel.i, ]
}
##
treat <- treat.list
event <- event.list
n <- n.list
}
##
else if (type == "continuous") {
##
## Generate lists
##
tdat <- data.frame(studlab, treat, n, mean, sd,
.order = seq_along(studlab),
stringsAsFactors = FALSE)
##
if (!nulldata) {
tdat <- cbind(tdat, data)
dupl <- duplicated(names(tdat))
if (any(dupl))
names(tdat)[dupl] <- paste(names(tdat)[dupl], "orig", sep = ".")
}
##
studlab <- names(n.arms)
dat.studlab <- data.frame(studlab, stringsAsFactors = FALSE)
##
for (i in 1:max.arms) {
sel.i <- !duplicated(tdat$studlab)
tdat.i <- merge(dat.studlab, tdat[sel.i, ],
by = "studlab", all.x = TRUE)
##
treat.list[[i]] <- tdat.i$treat
n.list[[i]] <- tdat.i$n
mean.list[[i]] <- tdat.i$mean
sd.list[[i]] <- tdat.i$sd
##
tdat.i$n <- NULL
tdat.i$mean <- NULL
tdat.i$sd <- NULL
##
if (!nulldata)
adddata[[i]] <- tdat.i
##
tdat <- tdat[!sel.i, ]
}
##
treat <- treat.list
n <- n.list
mean <- mean.list
sd <- sd.list
}
##
else if (type == "count") {
##
## Generate lists
##
tdat <- data.frame(studlab, treat, event, time,
.order = seq_along(studlab),
stringsAsFactors = FALSE)
##
if (!is.null(n))
tdat$n <- n
##
if (!nulldata) {
tdat <- cbind(tdat, data)
dupl <- duplicated(names(tdat))
if (any(dupl))
names(tdat)[dupl] <- paste(names(tdat)[dupl], "orig", sep = ".")
}
##
studlab <- names(n.arms)
dat.studlab <- data.frame(studlab, stringsAsFactors = FALSE)
##
for (i in 1:max.arms) {
sel.i <- !duplicated(tdat$studlab)
tdat.i <- merge(dat.studlab, tdat[sel.i, ],
by = "studlab", all.x = TRUE)
##
treat.list[[i]] <- tdat.i$treat
event.list[[i]] <- tdat.i$event
time.list[[i]] <- tdat.i$time
##
tdat.i$event <- NULL
tdat.i$time <- NULL
##
if (!nulldata)
adddata[[i]] <- tdat.i
##
tdat <- tdat[!sel.i, ]
}
##
treat <- treat.list
event <- event.list
time <- time.list
}
##
else if (type == "generic") {
##
## Generate lists
##
tdat <- data.frame(studlab, treat, TE, seTE,
.order = seq_along(studlab),
stringsAsFactors = FALSE)
##
if (!is.null(n))
tdat$n <- n
##
if (!is.null(event))
tdat$event <- event
##
if (!nulldata) {
tdat <- cbind(tdat, data)
dupl <- duplicated(names(tdat))
if (any(dupl))
names(tdat)[dupl] <- paste(names(tdat)[dupl], "orig", sep = ".")
}
##
studlab <- names(n.arms)
dat.studlab <- data.frame(studlab, stringsAsFactors = FALSE)
##
for (i in 1:max.arms) {
sel.i <- !duplicated(tdat$studlab)
tdat.i <- merge(dat.studlab, tdat[sel.i, ],
by = "studlab", all.x = TRUE)
##
treat.list[[i]] <- tdat.i$treat
TE.list[[i]] <- tdat.i$TE
seTE.list[[i]] <- tdat.i$seTE
##
tdat.i$TE <- NULL
tdat.i$seTE <- NULL
##
if (!nulldata)
adddata[[i]] <- tdat.i
##
tdat <- tdat[!sel.i, ]
}
##
treat <- treat.list
TE <- TE.list
seTE <- seTE.list
}
##
else if (type == "onlytreat") {
##
## Generate lists
##
tdat <- data.frame(studlab, treat,
.order = seq_along(studlab),
stringsAsFactors = FALSE)
##
if (!is.null(n))
tdat$n <- n
##
if (!nulldata) {
tdat <- cbind(tdat, data)
dupl <- duplicated(names(tdat))
if (any(dupl))
names(tdat)[dupl] <- paste(names(tdat)[dupl], "orig", sep = ".")
}
##
studlab <- names(n.arms)
dat.studlab <- data.frame(studlab, stringsAsFactors = FALSE)
##
for (i in 1:max.arms) {
sel.i <- !duplicated(tdat$studlab)
tdat.i <- merge(dat.studlab, tdat[sel.i, ],
by = "studlab", all.x = TRUE)
##
treat.list[[i]] <- tdat.i$treat
##
if (!nulldata)
adddata[[i]] <- tdat.i
##
tdat <- tdat[!sel.i, ]
}
##
treat <- treat.list
}
}
##
## Check and set study labels
##
if (is.null(studlab))
studlab <- seq(along = treat[[1]])
##
if (length(treat) != 2 && length(studlab) != length(unique(studlab)))
stop("Study labels must all be distinct.")
##
levs <- unique(studlab)
narms <- length(treat)
nstud <- length(studlab)
##
## Auxiliary functions
##
sumzero <- function(x)
sum(x[!is.na(x)] == 0)
##
anytrue <- function(x)
any(x == TRUE, na.rm = TRUE)
##
##
## Generate dataset with variables from original dataset
##
##
if (!nulldata & !wide.armbased) {
names.adddata <- names(adddata[[1]])
##
notunique <- matrix(NA,
ncol = length(names.adddata),
nrow = narms * (narms - 1) / 2)
colnames(notunique) <- names.adddata
##
oneNA <- matrix(NA,
ncol = length(names.adddata),
nrow = narms * (narms - 1) / 2)
##
allNA <- matrix(NA,
ncol = length(names.adddata),
nrow = narms * (narms - 1) / 2)
colnames(oneNA) <- names.adddata
##
n.ij <- 0
##
for (i in 1:(narms - 1)) {
for (j in (i + 1):narms) {
n.ij <- n.ij + 1
notunique[n.ij, ] <-
apply(adddata[[i]] != adddata[[j]], 2, anytrue)
##
allNA[n.ij, ] <- apply(is.na(adddata[[j]]), 2, all)
##
oneNA[n.ij, ] <-
apply(is.na(adddata[[i]]) & !is.na(adddata[[j]]), 2, anytrue)
}
}
##
notunique <- apply(notunique, 2, anytrue)
oneNA <- apply(oneNA, 2, anytrue)
allNA <- apply(allNA, 2, anytrue)
##print(apply(rbind(notunique, oneNA, allNA), 2, anytrue))
notunique <- apply(rbind(notunique, oneNA, allNA), 2, anytrue)
##
for (i in 1:(narms - 1)) {
for (j in (i + 1):narms) {
dat.i <- adddata[[i]]
dat.j <- adddata[[j]]
##
if (any(!notunique))
dat.ij <- dat.i[, names.adddata[!notunique], drop = FALSE]
else
stop("Study label must be unique for single treatment arm.")
##
for (nam in names.adddata[notunique]) {
dat.ij[, paste(nam, 1, sep = "")] <- adddata[[i]][nam]
dat.ij[, paste(nam, 2, sep = "")] <- adddata[[j]][nam]
}
##
if (i == 1 & j == 2)
newdata <- dat.ij
else
newdata <- rbind(newdata, dat.ij)
}
}
##
names.basic <- c("studlab", "treat1", "treat2")
names.newdata <- names(newdata)
##
newdata <- newdata[, c(names.basic,
names.newdata[!(names.newdata %in% names.basic)])]
newdata <- newdata[!is.na(newdata$treat1) & !is.na(newdata$treat2), ]
}
if (type == "binary") {
##
if (length(event) != narms)
stop("Different length of lists 'treat' and 'event'.")
if (length(n) != narms)
stop("Different length of lists 'treat' and 'n'.")
##
## Determine increment for individual studies
##
n.zeros <- apply(matrix(unlist(event), ncol = length(event)), 1, sumzero)
n.all <- apply(matrix(unlist(n), ncol = length(event)) -
matrix(unlist(event), ncol = length(event)),
1, sumzero)
##
incr.study <- rep(0, length(n.zeros))
##
if ("sm" %in% nam.args)
sm <- args$sm
else
sm <- gs("smbin")
##
sm <- setchar(sm, c("OR", "RD", "RR", "ASD"))
##
sparse <- switch(sm,
OR = (n.zeros > 0) | (n.all > 0),
RD = (n.zeros > 0) | (n.all > 0),
RR = (n.zeros > 0) | (n.all > 0),
ASD = rep(FALSE, length(n.zeros)))
##
if (!allincr & !addincr)
incr.study[sparse] <- incr
else if (addincr)
incr.study[] <- incr
else {
if (any(n.zeros > 0))
incr.study[] <- incr
else
incr.study[] <- 0
}
##
for (i in 1:(narms - 1)) {
##
if (i == 1 & (length(treat[[i]]) != length(event[[i]])))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'event'.")
if (i == 1 & (length(event[[i]]) != length(n[[i]])))
stop("Different length of element ", i, " of ",
"lists 'event' and 'n'.")
##
for (j in (i + 1):narms) {
##
if (length(treat[[j]]) != length(event[[j]]))
stop("Different length of element ", j, " of ",
"lists 'treat' and 'event'.")
if (length(event[[j]]) != length(n[[j]]))
stop("Different length of element ", j, " of ",
"lists 'event' and 'n'.")
##
dat <- data.frame(TE = NA, seTE = NA,
studlab,
treat1 = treat[[i]], treat2 = treat[[j]],
event1 = event[[i]], n1 = n[[i]],
event2 = event[[j]], n2 = n[[j]],
.order = seq_along(studlab),
incr = incr.study,
allstudies = allstudies,
stringsAsFactors = FALSE,
row.names = NULL)
##
if (wide.armbased & !nulldata) {
dat <- cbind(dat, data, stringsAsFactors = FALSE)
dupl <- duplicated(names(dat))
if (any(dupl))
names(dat)[dupl] <- paste(names(dat)[dupl], "orig", sep = ".")
}
##
dat <- dat[!(is.na(dat$event1) & is.na(dat$n1)), ]
dat <- dat[!(is.na(dat$event2) & is.na(dat$n2)), ]
##
if (nrow(dat) > 0) {
m1 <- metabin(dat$event1, dat$n1,
dat$event2, dat$n2,
incr = dat$incr, addincr = TRUE,
allstudies = allstudies,
method.tau = "DL", method.tau.ci = "",
warn = warn,
warn.deprecated = FALSE, ...)
##
dat$TE <- m1$TE
dat$seTE <- m1$seTE
##
dat$TE[is.infinite(dat$TE)] <- NA
dat$seTE[is.infinite(dat$seTE)] <- NA
##
dat.NAs <- dat[is.na(dat$TE) | is.na(dat$seTE) | dat$seTE <= 0, ]
##
if (i == 1 & j == 2) {
res <- dat
res.NAs <- dat.NAs
}
else {
res <- rbind(res, dat)
res.NAs <- rbind(res.NAs, dat.NAs)
}
}
else
if (i == 1 & j == 2)
stop("No studies available for comparison of ",
"first and second treatment.")
}
}
}
#
else if (type == "continuous") {
if (length(n) != narms)
stop("Different length of lists 'treat' and 'n'.")
if (length(mean) != narms)
stop("Different length of lists 'treat' and 'mean'.")
if (length(sd) != narms)
stop("Different length of lists 'treat' and 'sd'.")
##
for (i in seq_len(narms)) {
##
if (length(treat[[i]]) != length(n[[i]]))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'n'.",
call. = FALSE)
if (length(treat[[i]]) != length(mean[[i]]))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'mean'.",
call. = FALSE)
if (length(treat[[i]]) != length(sd[[i]]))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'sd'.",
call. = FALSE)
if (length(treat[[i]]) != nstud)
stop("Different length of study labels and ",
"element ", i, " of list 'treat'.",
call. = FALSE)
}
##
## For standardized mean difference, calculate pooled standard
## deviation for multi-arm studies
##
if ("sm" %in% nam.args && (tolower(args$sm) == "smd" & narms > 2)) {
pooled.sd <- function(sd, n) {
sel <- !is.na(sd) & !is.na(n)
##
if (any(sel))
res <- sqrt(sum((n[sel] - 1) * sd[sel]^2) / sum(n[sel] - 1))
else
res <- NA
##
res
}
##
N <- matrix(unlist(n), ncol = narms, nrow = nstud, byrow = FALSE)
M <- matrix(unlist(mean), ncol = narms, nrow = nstud, byrow = FALSE)
S <- matrix(unlist(sd), ncol = narms, nrow = nstud, byrow = FALSE)
##
sel.n <- apply(!is.na(N) & N > 0, 1, sum) > 2
sel.mean <- apply(!is.na(M), 1, sum) > 2
sel.sd <- apply(!is.na(S) & S > 0, 1, sum) > 2
sel <- sel.n & sel.mean & sel.sd
##
if (any(sel)) {
N <- N[sel, , drop = FALSE]
S <- S[sel, , drop = FALSE]
sd.p <- rep_len(NA, nrow(N))
##
for (i in seq_len(nrow(N)))
sd.p[i] <- pooled.sd(S[i, ], N[i, ])
}
##
for (i in seq_len(narms))
sd[[i]][sel] <- ifelse(is.na(sd[[i]][sel]), NA, sd.p)
}
##
for (i in 1:(narms - 1)) {
for (j in (i + 1):narms) {
dat <- data.frame(TE = NA, seTE = NA,
studlab,
treat1 = treat[[i]],
treat2 = treat[[j]],
n1 = n[[i]], mean1 = mean[[i]], sd1 = sd[[i]],
n2 = n[[j]], mean2 = mean[[j]], sd2 = sd[[j]],
.order = seq_along(studlab),
stringsAsFactors = FALSE,
row.names = NULL)
##
if (wide.armbased) {
dat <- cbind(dat, data, stringsAsFactors = FALSE)
dupl <- duplicated(names(dat))
if (any(dupl))
names(dat)[dupl] <- paste(names(dat)[dupl], "orig", sep = ".")
}
##
dat <- dat[!(is.na(dat$n1) & is.na(dat$mean1) & is.na(dat$sd1)), ]
dat <- dat[!(is.na(dat$n2) & is.na(dat$mean2) & is.na(dat$sd2)), ]
##
if (nrow(dat) > 0) {
m1 <- metacont(dat$n1, dat$mean1, dat$sd1,
dat$n2, dat$mean2, dat$sd2,
method.tau = "DL", method.tau.ci = "",
method.smd = "Cohen",
warn = warn,
warn.deprecated = FALSE, ...)
##
dat$TE <- m1$TE
dat$seTE <- m1$seTE
##
dat$TE[is.infinite(dat$TE)] <- NA
dat$seTE[is.infinite(dat$seTE)] <- NA
##
dat.NAs <- dat[is.na(dat$TE) | is.na(dat$seTE) | dat$seTE <= 0, ]
##
if (i == 1 & j == 2) {
res <- dat
res.NAs <- dat.NAs
}
else {
res <- rbind(res, dat)
res.NAs <- rbind(res.NAs, dat.NAs)
}
}
else
if (i == 1 & j == 2)
stop("No studies available for comparison of ",
"first and second treatment.",
call. = FALSE)
}
}
}
#
else if (type == "generic") {
if (length(TE) != narms)
stop("Different length of lists 'treat' and 'TE'.",
call. = FALSE)
if (length(seTE) != narms)
stop("Different length of lists 'treat' and 'seTE'.",
call. = FALSE)
##
for (i in 1:(narms - 1)) {
##
if (i == 1 & (length(treat[[i]]) != length(TE[[i]])))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'TE'.",
call. = FALSE)
if (i == 1 & (length(treat[[i]]) != length(seTE[[i]])))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'seTE'.",
call. = FALSE)
##
for (j in (i + 1):narms) {
##
if (length(treat[[j]]) != length(TE[[j]]))
stop("Different length of element ", j, " of ",
"lists 'treat' and 'TE'.",
call. = FALSE)
if (length(treat[[j]]) != length(seTE[[j]]))
stop("Different length of element ", j, " of ",
"lists 'treat' and 'seTE'.",
call. = FALSE)
##
dat <- data.frame(TE = NA, seTE = NA,
studlab,
treat1 = treat[[i]],
treat2 = treat[[j]],
TE1 = TE[[i]], seTE1 = seTE[[i]],
TE2 = TE[[j]], seTE2 = seTE[[j]],
.order = seq_along(studlab),
stringsAsFactors = FALSE,
row.names = NULL)
##
if (!is.null(event)) {
dat$event1 <- event[[i]]
dat$event2 <- event[[j]]
}
##
if (!is.null(n)) {
dat$n1 <- n[[i]]
dat$n2 <- n[[j]]
}
##
if (!is.null(mean)) {
dat$mean1 <- mean[[i]]
dat$mean2 <- mean[[j]]
}
##
if (!is.null(sd)) {
dat$sd1 <- sd[[i]]
dat$sd2 <- sd[[j]]
}
##
if (!is.null(time)) {
dat$time1 <- time[[i]]
dat$time2 <- time[[j]]
}
##
if (wide.armbased) {
dat <- cbind(dat, data, stringsAsFactors = FALSE)
dupl <- duplicated(names(dat))
if (any(dupl))
names(dat)[dupl] <- paste(names(dat)[dupl], "orig", sep = ".")
}
##
dat <- dat[!(is.na(dat$TE1) & is.na(dat$seTE1)), ]
dat <- dat[!(is.na(dat$TE2) & is.na(dat$seTE2)), ]
##
if (nrow(dat) > 0) {
m1 <- metagen(dat$TE1 - dat$TE2,
sqrt(dat$seTE1^2 + dat$seTE2^2),
method.tau = "DL", method.tau.ci = "",
warn = warn,
warn.deprecated = FALSE, ...)
##
dat$TE <- m1$TE
dat$seTE <- m1$seTE
##
dat$TE[is.infinite(dat$TE)] <- NA
dat$seTE[is.infinite(dat$seTE)] <- NA
##
dat.NAs <- dat[is.na(dat$TE) | is.na(dat$seTE) | dat$seTE <= 0, ]
##
if (i == 1 & j == 2) {
res <- dat
res.NAs <- dat.NAs
}
else {
res <- rbind(res, dat)
res.NAs <- rbind(res.NAs, dat.NAs)
}
}
else
if (i == 1 & j == 2)
stop("No studies available for comparison of ",
"first and second treatment.",
call. = FALSE)
}
}
}
#
else if (type == "count") {
if (length(event) != narms)
stop("Different length of lists 'treat' and 'event'.",
call. = FALSE)
if (length(time) != narms)
stop("Different length of lists 'treat' and 'time'.",
call. = FALSE)
##
## Determine increment for individual studies
##
n.zeros <- apply(matrix(unlist(event), ncol = length(event)), 1, sumzero)
##
incr.study <- rep(0, length(n.zeros))
##
sparse <- n.zeros > 0
##
if (!allincr & !addincr)
incr.study[sparse] <- incr
else if (addincr)
incr.study[] <- incr
else {
if (any(n.zeros > 0))
incr.study[] <- incr
else
incr.study[] <- 0
}
##
for (i in 1:(narms - 1)) {
##
if (i == 1 & (length(treat[[i]]) != length(event[[i]])))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'event'.",
call. = FALSE)
if (i == 1 & (length(treat[[i]]) != length(time[[i]])))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'time'.",
call. = FALSE)
##
for (j in (i + 1):narms) {
##
if (length(treat[[j]]) != length(event[[j]]))
stop("Different length of element ", j, " of ",
"lists 'treat' and 'event'.",
call. = FALSE)
if (length(treat[[j]]) != length(time[[j]]))
stop("Different length of element ", j, " of ",
"lists 'treat' and 'time'.",
call. = FALSE)
##
dat <- data.frame(TE = NA, seTE = NA,
studlab,
treat1 = treat[[i]],
treat2 = treat[[j]],
event1 = event[[i]], time1 = time[[i]],
event2 = event[[j]], time2 = time[[j]],
incr = incr.study,
.order = seq_along(studlab),
stringsAsFactors = FALSE,
row.names = NULL)
##
if (wide.armbased) {
dat <- cbind(dat, data, stringsAsFactors = FALSE)
dupl <- duplicated(names(dat))
if (any(dupl))
names(dat)[dupl] <- paste(names(dat)[dupl], "orig", sep = ".")
}
##
dat <- dat[!(is.na(dat$event1) & is.na(dat$time1)), ]
dat <- dat[!(is.na(dat$event2) & is.na(dat$time2)), ]
##
if (nrow(dat) > 0) {
m1 <- metainc(dat$event1, dat$time1,
dat$event2, dat$time2,
incr = dat$incr, addincr = TRUE,
allstudies = allstudies,
method.tau = "DL", method.tau.ci = "",
warn = warn,
warn.deprecated = FALSE, ...)
##
dat$TE <- m1$TE
dat$seTE <- m1$seTE
##
dat$TE[is.infinite(dat$TE)] <- NA
dat$seTE[is.infinite(dat$seTE)] <- NA
##
dat.NAs <- dat[is.na(dat$TE) | is.na(dat$seTE) | dat$seTE <= 0, ]
##
if (i == 1 & j == 2) {
res <- dat
res.NAs <- dat.NAs
}
else {
res <- rbind(res, dat)
res.NAs <- rbind(res.NAs, dat.NAs)
}
}
else
if (i == 1 & j == 2)
stop("No studies available for comparison of ",
"first and second treatment.",
call. = FALSE)
}
}
}
#
else if (type == "onlytreat") {
for (i in 1:(narms - 1)) {
for (j in (i + 1):narms) {
##
dat <- data.frame(studlab,
treat1 = treat[[i]],
treat2 = treat[[j]],
.order = seq_along(studlab),
stringsAsFactors = FALSE,
row.names = NULL)
##
if (!is.null(n)) {
dat$n1 <- n[[i]]
dat$n2 <- n[[j]]
}
##
if (wide.armbased) {
dat <- cbind(dat, data, stringsAsFactors = FALSE)
dupl <- duplicated(names(dat))
if (any(dupl))
names(dat)[dupl] <- paste(names(dat)[dupl], "orig", sep = ".")
}
##
dat <- dat[!is.na(dat$treat2), ]
##
if (nrow(dat) > 0) {
dat.NAs <- data.frame()
##
if (i == 1 & j == 2) {
res <- dat
res.NAs <- dat.NAs
}
else {
res <- rbind(res, dat)
res.NAs <- rbind(res.NAs, dat.NAs)
}
}
else
if (i == 1 & j == 2)
stop("No studies available for comparison of ",
"first and second treatment.",
call. = FALSE)
}
}
}
##
if (!nulldata & !wide.armbased)
res <- merge(res, newdata,
by = c("studlab", "treat1", "treat2"),
suffixes = c("",".orig"),
all.x = TRUE)
##
## Additional checks
##
##
## a) Duplicate treatments ?
##
sel.treat <- as.character(res$treat1) == as.character(res$treat2)
##
if (any(sel.treat)) {
sel.stud <- unique(sort(res$studlab[sel.treat]))
##
stop(paste0("Identical treatments for the following stud",
if (length(sel.stud) == 1) "y: " else "ies:\n ",
paste0(paste0("'", sel.stud, "'"),
collapse = " - "),
"\n Please check dataset."),
call. = FALSE)
}
##
## b) Studies missing ?
##
sel.study <- !(studlab %in% unique(as.character(res$studlab)))
##
if (any(sel.study) & warn)
warning(paste0("The following stud",
if (sum(sel.study) == 1) "y is " else "ies are ",
"excluded from the analysis\n ",
"(due to a single study arm or missing values):",
if (sum(sel.study) == 1) " " else "\n ",
paste0(paste0("'", studlab[sel.study], "'"),
collapse = " - ")),
call. = FALSE)
##
## c) Missing treatment estimates or standard errors?
##
if (type != "onlytreat" && nrow(res.NAs) > 0 & warn) {
warning("Comparison",
if (nrow(res.NAs) > 1) "s",
" with missing TE / seTE or zero seTE",
" will not be considered in network meta-analysis.",
call. = FALSE)
cat(paste("Comparison",
if (nrow(res.NAs) > 1) "s",
" will not be considered in network meta-analysis:\n",
sep = ""))
##
res.NAs$.order <- NULL
res.NAs$.order1 <- NULL
res.NAs$.order2 <- NULL
##
prmatrix(res.NAs,
quote = FALSE, right = TRUE, na.print = "NA",
rowlab = rep("", nrow(res.NAs)))
}
## Calculate standard error for SMDs
##
if (type == "continuous" && m1$sm == "SMD") {
res$.seTE <- res$seTE
##
for (i in unique(res$studlab)) {
sel.i <- res$studlab == i
dat.i <- res[sel.i, ]
##
## Calculate total sample size in study i
##
ndat.i <- rbind(data.frame(treat = dat.i$treat1, n = dat.i$n1),
data.frame(treat = dat.i$treat2, n = dat.i$n2))
n.i <- sum(ndat.i[!duplicated(ndat.i), ]$n)
##
## Crippa & Orsini (2016), BMC Med Res Meth, equation (5)
##
varTE.i <-
1 / res$n1[sel.i] + 1 / res$n2[sel.i] + res$TE[sel.i]^2 / (2 * n.i)
##
res$seTE[sel.i] <- sqrt(varTE.i)
}
}
attr(res, "sm") <- if (type != "onlytreat") m1$sm else ""
attr(res, "method") <- if (type != "onlytreat") m1$method else ""
attr(res, "incr") <- incr
attr(res, "allincr") <- allincr
attr(res, "addincr") <- addincr
attr(res, "allstudies") <- allstudies
if (!is.null(res$.order1)) {
res <- res[order(res$.order1), ]
res$.order1 <- NULL
res$.order2 <- NULL
res$.order <- NULL
res <- unique(res)
}
else if (!is.null(res$.order)) {
res <- res[order(res$.order), ]
res$.order <- NULL
res$.order.orig <- NULL
res <- unique(res)
}
else {
res <- res[order(factor(res$studlab, levels = levs),
res$treat1, res$treat2), ]
}
##
rownames(res) <- 1:nrow(res)
}
##
## Use first treatment with estimable effect as reference if
## argument is missing
##
labels <- unique(sort(c(res$treat1, res$treat2)))
##
if (missing.reference.group) {
if (type == "onlytreat")
reference.group <- ""
else {
go.on <- TRUE
i <- 0
while (go.on) {
i <- i + 1
sel.i <-
!is.na(res$TE) & !is.na(res$seTE) &
(res$treat1 == labels[i] | res$treat2 == labels[i])
if (sum(sel.i) > 0) {
go.on <- FALSE
reference.group <- labels[i]
}
else if (i == length(labels)) {
go.on <- FALSE
reference.group <- ""
}
}
}
}
##
if (is.factor(reference.group))
reference.group <- as.character(reference.group)
##
if (is.numeric(reference.group))
chknumeric(reference.group, length = 1)
else
chkchar(reference.group, length = 1)
##
reference.group <- setchar(reference.group, c(labels, ""))
##
if (!keep.all.comparisons) {
##
drop <- logical(0)
##
for (i in unique(res$studlab)) {
d.i <- res[res$studlab == i, , drop = FALSE]
trts.i <- unique(sort(c(d.i$treat1, d.i$treat2)))
##
## Keep comparisons with reference group or first treatment if
## reference treatment is missing in study
##
if (reference.group %in% trts.i)
ref.i <- reference.group
else
ref.i <- rev(trts.i)[1]
##
drop.i <- d.i$treat1 != ref.i & d.i$treat2 != ref.i
##
drop <- c(drop, drop.i)
}
##
if (!keep.all.comparisons)
res <- res[!drop, , drop = FALSE]
}
attr(res, "pairwise") <- TRUE
attr(res, "reference.group") <- reference.group
attr(res, "keep.all.comparisons") <- keep.all.comparisons
attr(res, "type") <- type
attr(res, "version") <- packageDescription("netmeta")$Version
class(res) <- c("pairwise", class(res))
res
}
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Defines functions pairwise
Documented in pairwise
#' Transform meta-analysis data from two arm-based formats into
#' contrast-based format
#'
#' @description
#' This function transforms data that are given in wide or long
#' arm-based format (e.g. input format for WinBUGS) to a
#' contrast-based format that is needed as input to R function
#' \code{\link{netmeta}}. The function can transform data with binary,
#' continuous, or generic outcomes as well as incidence rates from
#' arm-based to contrast-based format.
#'
#' @param treat A list or vector with treatment information for
#' individual treatment arms (see Details).
#' @param event A list or vector with information on number of events
#' for individual treatment arms (see Details).
#' @param n A list or vector with information on number of
#' observations for individual treatment arms (see Details).
#' @param mean A list or vector with estimated means for individual
#' treatment arms (see Details).
#' @param sd A list or vector with information on the standard
#' deviation for individual treatment arms (see Details).
#' @param TE A list or vector with estimated treatment effects for
#' individual treatment arms (see Details).
#' @param seTE A list or vector with standard errors of estimated
#' treatment effect for individual treatment arms (see Details).
#' @param time A list or vector with information on person time at
#' risk for individual treatment arms (see Details).
#' @param data An optional data frame containing the study
#' information.
#' @param studlab A vector with study labels (optional).
#' @param incr A numerical value which is added to cell frequencies
#' for studies with a zero cell count, see Details.
#' @param allincr A logical indicating if \code{incr} is added to cell
#' frequencies of all studies if at least one study has a zero cell
#' count. If FALSE (default), \code{incr} is added only to cell
#' frequencies of studies with a zero cell count.
#' @param addincr A logical indicating if \code{incr} is added to cell
#' frequencies of all studies irrespective of zero cell counts.
#' @param allstudies A logical indicating if studies with zero or all
#' events in two treatment arms are to be included in the
#' meta-analysis (applies only if \code{sm} is equal to \code{"RR"}
#' or \code{"OR"}).
#' @param reference.group Reference treatment (first treatment is used
#' if argument is missing).
#' @param keep.all.comparisons A logical indicating whether all
#' pairwise comparisons or only comparisons with the study-specific
#' reference group should be kept ('basic parameters').
#' @param append A logical indicating whether variables from data set
#' provided in argument \code{data} are appended to data set with
#' pairwise comparisons.
#' @param warn A logical indicating whether warnings should be printed
#' (e.g., if studies are excluded due to only providing a single
#' treatment arm).
#' @param \dots Additional arguments passed-through to the functions
#' to calculate effects.
#'
#' @details
#' R function \code{\link{netmeta}} expects data in a
#' \bold{contrast-based format}, where each row corresponds to a
#' comparison of two treatments and contains a measure of the
#' treatment effect comparing two treatments with standard error,
#' labels for the two treatments and an optional study label. In
#' contrast-based format, a three-arm study contributes three rows
#' with treatment comparison and corresponding standard error for
#' pairwise comparison \emph{A} vs \emph{B}, \emph{A} vs \emph{C}, and
#' \emph{B} vs \emph{C} whereas a four-arm study contributes six rows
#' / pairwise comparisons: \emph{A} vs \emph{B}, \emph{A} vs \emph{C},
#' \dots{}, \emph{C} vs \emph{D}.
#'
#' Other programs for network meta-analysis in WinBUGS and Stata
#' require data in an \emph{arm-based} format, i.e. treatment estimate
#' for each treatment arm instead of a difference of two treatments. A
#' common \bold{(wide) arm-based format} consists of one data row per
#' study, containing treatment and other necessary information for all
#' study arms. For example, a four-arm study contributes one row with
#' four treatment estimates and corresponding standard errors for
#' treatments \emph{A}, \emph{B}, \emph{C}, and \emph{D}. Another
#' possible arm-based format is a long format where each row
#' corresponds to a single study arm. Accordingly, in the \bold{long
#' arm-based format} a study contributes as many rows as treatments
#' considered in the study.
#'
#' The pairwise function transforms data given in (wide or long)
#' arm-based format into the contrast-based format which consists of
#' \emph{pairwise} comparisons and is needed as input to the
#' \code{\link{netmeta}} function.
#'
#' The pairwise function can transform data with binary outcomes
#' (using the \code{\link{metabin}} function from R package meta),
#' continuous outcomes (\code{\link{metacont}} function), incidence
#' rates (\code{\link{metainc}} function), and generic outcomes
#' (\code{\link{metagen}} function). Depending on the outcome, the
#' following arguments are mandatory:
#' \itemize{
#' \item treat, event, n (see \code{\link{metabin}});
#' \item treat, n, mean, sd (see \code{\link{metacont}});
#' \item treat, event, time (see \code{\link{metainc}});
#' \item treat, TE, seTE (see \code{\link{metagen}}).
#' }
#'
#' Argument \code{treat} is mandatory to identify the individual
#' treatments. The other arguments contain outcome specific
#' data. These arguments must be either lists (wide arm-based format,
#' i.e., one row per study) or vectors (long arm-based format,
#' i.e., multiple rows per study) of the same length.
#'
#' For the wide arm-based format, each list consists of as many
#' vectors of the same length as the multi-arm study with the largest
#' number of treatments. If a single multi-arm study has five arms,
#' five vectors have to be provided for each lists. Two-arm studies
#' have entries with \code{NA} for the third and subsequent
#' vectors. Each list entry is a vector with information for each
#' individual study; i.e., the length of this vector corresponds to
#' the total number of studies incorporated in the network
#' meta-analysis. Typically, list elements are part of a data frame
#' (argument \code{data}, optional); see Examples. An optional vector
#' with study labels can be provided which can be part of the data
#' frame.
#'
#' In the long arm-based format, argument \code{studlab} is mandatory
#' to identify rows contributing to individual studies.
#'
#' Additional arguments for meta-analysis functions can be provided
#' using argument '\dots'. The most important argument is \code{sm}
#' defining the summary measure. More information on this and other
#' arguments is given in the help pages of R functions
#' \code{\link{metabin}}, \code{\link{metacont}},
#' \code{\link{metainc}}, and \code{\link{metagen}}, respectively.
#'
#' For standardised mean differences (argument \code{sm = "SMD"}),
#' equations (4) and (5) in Crippa & Orsini (2016) are used to
#' calculated SMDs and standard errors. These equations guarantee
#' consistent SMDs and standard errors for multi-arm studies. Note,
#' the summary measure is actually Cohen's d as Hedges' g is not
#' consistent in multi-arm studies.
#'
#' For binary outcomes, 0.5 is added to all cell frequencies (odds
#' ratio) or only the number of events (risk ratio) for studies with a
#' zero cell count. For odds ratio and risk ratio, treatment estimates
#' and standard errors are only calculated for studies with zero or
#' all events in both groups if \code{allstudies} is \code{TRUE}. This
#' continuity correction is used both to calculate individual study
#' results with confidence limits and to conduct meta-analysis based
#' on the inverse variance method. For the risk difference, 0.5 is
#' only added to all cell frequencies to calculate the standard error.
#'
#' For incidence rates, 0.5 is added to all cell frequencies for the
#' incidence rate ratio as summary measure. For the incidence risk
#' difference, 0.5 is only added to all cell frequencies to calculate
#' the standard error.
#'
#' The value of pairwise is a data frame with as many rows as there
#' are pairwise comparisons. For each study with \emph{p} treatments,
#' \emph{p*(p-1) / 2} contrasts are generated. Each row contains the
#' treatment effect (\code{TE}), its standard error (\code{seTE}), the
#' treatments compared ((\code{treat1}), (\code{treat2})) and the
#' study label ((\code{studlab})). Further columns are added according
#' to type of data.
#'
#' All variables from the original dataset are also part of the output
#' dataset. If data are provided in the long arm-based format, the
#' value of a variable can differ between treatment arms; for example,
#' the mean age or percentage of women in the treatment arm. In this
#' situation, two variables instead of one variable will be included
#' in the output dataset. The values "1" and "2" are added to the
#' names for these variables, e.g. "mean.age1" and "mean.age2" for the
#' mean age.
#'
#' In general, any variable names in the original dataset that are
#' identical to the main variable names (i.e., "TE", "seTE", ...) will
#' be renamed to variable names with ending ".orig".
#'
#' A reduced data set with basic comparisons (Rücker & Schwarzer,
#' 2014) can be generated using argument \code{keep.all.comparisons =
#' FALSE}. Furthermore, the reference group for the basic comparisons
#' can be specified with argument \code{reference.group}.
#'
#' @note
#' This function must not be confused with \code{\link{netpairwise}}
#' which can be used to conduct pairwise meta-analyses for all
#' comparisons with direct evidence in a network meta-analysis.
#'
#' @return
#' A data frame with the following columns:
#' \item{TE}{Treatment estimate comparing treatment 'treat1' and
#' 'treat2'.}
#' \item{seTE}{Standard error of treatment estimate.}
#' \item{studlab}{Study labels.}
#' \item{treat1}{First treatment in comparison.}
#' \item{treat2}{Second treatment in comparison.}
#' \item{event1}{Number of events for first treatment arm (for metabin
#' and metainc).}
#' \item{event2}{Number of events for second treatment arm (for
#' metabin and metainc).}
#' \item{n1}{Number of observations for first treatment arm (for
#' metabin and metacont).}
#' \item{n2}{Number of observations for second treatment arm (for
#' metabin and metacont).}
#' \item{mean1}{Estimated mean for first treatment arm (for
#' metacont).}
#' \item{mean2}{Estimated mean for second treatment arm (for
#' metacont).}
#' \item{sd1}{Standard deviation for first treatment arm (for
#' metacont).}
#' \item{sd2}{Standard deviation for second treatment arm (for
#' metacont).}
#' \item{TE1}{Estimated treatment effect for first treatment arm (for
#' metagen).}
#' \item{TE2}{Estimated treatment effect for second treatment arm (for
#' metagen).}
#' \item{seTE1}{Standard error of estimated treatment effect for first
#' treatment arm (for metagen).}
#' \item{seTE2}{Standard error of estimated treatment effect for
#' second treatment arm (for metagen).}
#' \item{time1}{Person time at risk for first treatment arm (for
#' metainc).}
#' \item{time2}{Person time at risk for second treatment arm (for
#' metainc).}
#'
#' All variables from the original dataset are also part of the output
#' dataset; see Details.
#'
#' @author Gerta Rücker\email{gerta.ruecker@@uniklinik-freiburg.de}, Guido
#' Schwarzer \email{guido.schwarzer@@uniklinik-freiburg.de}
#'
#' @seealso \code{\link{netmeta}}, \code{\link{metacont}},
#' \code{\link{metagen}}, \code{\link{metabin}},
#' \code{\link{metainc}}, \code{\link{netgraph.netmeta}},
#' \code{\link{pairwise}}
#'
#' @references
#' Crippa A, Orsini N (2016):
#' Dose-response meta-analysis of differences in means.
#' \emph{BMC Medical Research Methodology},
#' \bold{16}:91.
#'
#' @keywords datagen
#'
#' @examples
#' # Example using continuous outcomes (internal call of function
#' # metacont)
#' #
#' data(Franchini2012)
#' # Transform data from arm-based format to contrast-based format
#' p1 <- pairwise(list(Treatment1, Treatment2, Treatment3),
#' n = list(n1, n2, n3),
#' mean = list(y1, y2, y3), sd = list(sd1, sd2, sd3),
#' data = Franchini2012, studlab = Study)
#' p1
#'
#' \dontrun{
#' # Conduct network meta-analysis
#' #
#' net1 <- netmeta(p1)
#' net1
#'
#' # Draw network graphs
#' #
#' netgraph(net1, points = TRUE, cex.points = 3, cex = 1.5,
#' thickness = "se.common")
#' netgraph(net1, points = TRUE, cex.points = 3, cex = 1.5,
#' plastic = TRUE, thickness = "se.common",
#' iterate = TRUE)
#' netgraph(net1, points = TRUE, cex.points = 3, cex = 1.5,
#' plastic = TRUE, thickness = "se.common",
#' iterate = TRUE, start = "eigen")
#'
#' # Example using generic outcomes (internal call of function
#' # metagen)
#' #
#' # Calculate standard error for means y1, y2, y3
#' Franchini2012$se1 <- with(Franchini2012, sqrt(sd1^2 / n1))
#' Franchini2012$se2 <- with(Franchini2012, sqrt(sd2^2 / n2))
#' Franchini2012$se3 <- with(Franchini2012, sqrt(sd3^2 / n3))
#' # Transform data from arm-based format to contrast-based format
#' # using means and standard errors (note, argument 'sm' has to be
#' # used to specify that argument 'TE' is a mean difference)
#' p2 <- pairwise(list(Treatment1, Treatment2, Treatment3),
#' TE = list(y1, y2, y3), seTE = list(se1, se2, se3),
#' n = list(n1, n2, n3),
#' data = Franchini2012, studlab = Study,
#' sm = "MD")
#' p2
#'
#' # Compare pairwise objects p1 (based on continuous outcomes) and p2
#' # (based on generic outcomes)
#' #
#' all.equal(p1[, c("TE", "seTE", "studlab", "treat1", "treat2")],
#' p2[, c("TE", "seTE", "studlab", "treat1", "treat2")])
#'
#' # Same result as network meta-analysis based on continuous outcomes
#' # (object net1)
#' net2 <- netmeta(p2)
#' net2
#'
#' # Example with binary data
#' #
#' data(smokingcessation)
#' # Transform data from arm-based format to contrast-based format
#' # (interal call of metabin function). Argument 'sm' has to be used
#' # for odds ratio as risk ratio (sm = "RR") is default of metabin
#' # function.
#' #
#' p3 <- pairwise(list(treat1, treat2, treat3),
#' list(event1, event2, event3), list(n1, n2, n3),
#' data = smokingcessation,
#' sm = "OR")
#' p3
#'
#' # Conduct network meta-analysis
#' #
#' net3 <- netmeta(p3)
#' net3
#'
#' # Example with incidence rates
#' #
#' data(dietaryfat)
#'
#' # Transform data from arm-based format to contrast-based format
#' #
#' p4 <- pairwise(list(treat1, treat2, treat3),
#' list(d1, d2, d3), time = list(years1, years2, years3),
#' studlab = ID,
#' data = dietaryfat)
#' p4
#'
#' # Conduct network meta-analysis using incidence rate ratios (sm =
#' # "IRR"). Note, the argument 'sm' is not necessary as this is the
#' # default in R function metainc called internally.
#' #
#' net4 <- netmeta(p4, sm = "IRR")
#' summary(net4)
#'
#' # Example with long data format
#' #
#' data(Woods2010)
#'
#' # Transform data from long arm-based format to contrast-based
#' # format Argument 'sm' has to be used for odds ratio as summary
#' # measure; by default the risk ratio is used in the metabin
#' # function called internally.
#' #
#' p5 <- pairwise(treatment, event = r, n = N,
#' studlab = author, data = Woods2010, sm = "OR")
#' p5
#'
#' # Conduct network meta-analysis
#' net5 <- netmeta(p5)
#' net5
#' }
#'
#' @export pairwise
pairwise <- function(treat,
event, n, mean, sd, TE, seTE, time,
data = NULL, studlab,
incr = gs("incr"), allincr = gs("allincr"),
addincr = gs("addincr"), allstudies = gs("allstudies"),
##
reference.group,
keep.all.comparisons,
##
append = !is.null(data),
warn = FALSE,
...) {
##
##
## (1) Check arguments
##
##
chknumeric(incr, min = 0, length = 1)
chklogical(allincr)
chklogical(addincr)
chklogical(allstudies)
chklogical(append)
chklogical(warn)
##
##
## (2) Read data
##
##
nulldata <- is.null(data)
sfsp <- sys.frame(sys.parent())
mc <- match.call()
##
if (nulldata)
data <- sfsp
##
missing.reference.group <- missing(reference.group)
##
## Catch studlab, treat, event, n, mean, sd, time from data:
##
treat <- catch("treat", mc, data, sfsp)
##
if (is.data.frame(treat) & !is.null(attr(treat, "pairwise"))) {
is.pairwise <- TRUE
res <- treat
##
if (missing.reference.group)
if (!is.null(attributes(treat)$reference.group)) {
reference.group <- attributes(treat)$reference.group
missing.reference.group <- FALSE
}
if (missing(keep.all.comparisons)) {
if (!is.null(attributes(treat)$keep.all.comparisons))
keep.all.comparisons <- attributes(treat)$keep.all.comparisons
else
keep.all.comparisons <- TRUE
}
##
if (!missing(event))
warning("Argument 'event' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(n))
warning("Argument 'n' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(mean))
warning("Argument 'mean' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(sd))
warning("Argument 'sd' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(TE))
warning("Argument 'TE' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(seTE))
warning("Argument 'seTE' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(time))
warning("Argument 'time' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!nulldata)
warning("Argument 'data' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(studlab))
warning("Argument 'studlab' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(incr))
warning("Argument 'incr' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(allincr))
warning("Argument 'allincr' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(addincr))
warning("Argument 'addincr' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
if (!missing(allstudies))
warning("Argument 'allstudies' ignored as ",
"first argument is a pairwise object.",
call. = FALSE)
##
type <- "pairwise"
}
else {
is.pairwise <- FALSE
##
if (missing(keep.all.comparisons))
keep.all.comparisons <- TRUE
chklogical(keep.all.comparisons)
##
studlab <- catch("studlab", mc, data, sfsp)
event <- catch("event", mc, data, sfsp)
n <- catch("n", mc, data, sfsp)
mean <- catch("mean", mc, data, sfsp)
sd <- catch("sd", mc, data, sfsp)
TE <- catch("TE", mc, data, sfsp)
seTE <- catch("seTE", mc, data, sfsp)
time <- catch("time", mc, data, sfsp)
args <- list(...)
nam.args <- names(args)
if (is.null(treat))
stop("Argument 'treat' mandatory.")
##
if (is.list(treat))
chklist(treat)
##
if (!is.null(event))
if (is.list(event))
chklist(event)
else
chknumeric(event)
##
if (!is.null(n))
if (is.list(n))
chklist(n)
else
chknumeric(n)
##
if (!is.null(mean))
if (is.list(mean))
chklist(mean)
else
chknumeric(mean)
##
if (!is.null(sd))
if (is.list(sd))
chklist(sd)
else
chknumeric(sd)
##
if (!is.null(TE))
if (is.list(TE))
chklist(TE)
else
chknumeric(TE)
##
if (!is.null(seTE))
if (is.list(seTE))
chklist(seTE)
else
chknumeric(seTE)
##
if (!is.null(time))
if (is.list(time))
chklist(time)
else
chknumeric(time)
if (!is.null(TE) & !is.null(seTE))
type <- "generic"
else if (!is.null(event) & !is.null(time) &
is.null(mean) & is.null(sd))
type <- "count"
else if (!is.null(event) & !is.null(n) &
is.null(mean) & is.null(sd))
type <- "binary"
else if (!is.null(n) & !is.null(mean) & !is.null(sd))
type <- "continuous"
else if (is.null(event) & is.null(mean) & is.null(sd) &
is.null(TE) & is.null(seTE) & is.null(time))
type <- "onlytreat"
else
stop("Type of outcome unclear. Please provide the necessary ",
"information:\n - event, n (binary outcome)\n - n, ",
"mean, sd (continuous outcome)\n - TE, seTE (generic outcome)\n",
" - event, time (incidence rates).")
##
## Determine whether data is in wide or long arm-based format
##
if (type == "generic")
wide.armbased <- is.list(TE) & is.list(seTE)
if (type == "binary")
wide.armbased <- is.list(event) & is.list(n)
else if (type == "continuous")
wide.armbased <- is.list(n) & is.list(mean) & is.list(sd)
else if (type == "count")
wide.armbased <- is.list(event) & is.list(time)
else if (type == "onlytreat")
wide.armbased <- is.list(treat)
##
## Transform long arm-based format to list format
##
nulldata <- !(!nulldata & append)
#
if (!wide.armbased) {
if (is.null(studlab))
stop("Argument 'studlab' mandatory if argument 'event' is a vector.")
##
studlab <- as.character(studlab)
##
treat <- as.character(treat)
##
ttab <- table(studlab, treat)
n.arms <- apply(ttab, 1, sum)
max.arms <- max(n.arms)
##
treat.list <- vector("list", max.arms)
event.list <- vector("list", max.arms)
n.list <- vector("list", max.arms)
mean.list <- vector("list", max.arms)
sd.list <- vector("list", max.arms)
TE.list <- vector("list", max.arms)
seTE.list <- vector("list", max.arms)
time.list <- vector("list", max.arms)
##
if (!nulldata)
adddata <- vector("list", max.arms)
##
if (type == "binary") {
##
## Generate lists
##
tdat <- data.frame(studlab, treat, event, n,
.order = seq_along(studlab),
stringsAsFactors = FALSE)
##
if (!nulldata) {
tdat <- cbind(tdat, data)
dupl <- duplicated(names(tdat))
if (any(dupl))
names(tdat)[dupl] <- paste(names(tdat)[dupl], "orig", sep = ".")
}
##
studlab <- names(n.arms)
dat.studlab <- data.frame(studlab, stringsAsFactors = FALSE)
##
for (i in 1:max.arms) {
sel.i <- !duplicated(tdat$studlab)
tdat.i <- merge(dat.studlab, tdat[sel.i, ],
by = "studlab", all.x = TRUE)
##
treat.list[[i]] <- tdat.i$treat
event.list[[i]] <- tdat.i$event
n.list[[i]] <- tdat.i$n
##
tdat.i$event <- NULL
tdat.i$n <- NULL
##
if (!nulldata)
adddata[[i]] <- tdat.i
##
tdat <- tdat[!sel.i, ]
}
##
treat <- treat.list
event <- event.list
n <- n.list
}
##
else if (type == "continuous") {
##
## Generate lists
##
tdat <- data.frame(studlab, treat, n, mean, sd,
.order = seq_along(studlab),
stringsAsFactors = FALSE)
##
if (!nulldata) {
tdat <- cbind(tdat, data)
dupl <- duplicated(names(tdat))
if (any(dupl))
names(tdat)[dupl] <- paste(names(tdat)[dupl], "orig", sep = ".")
}
##
studlab <- names(n.arms)
dat.studlab <- data.frame(studlab, stringsAsFactors = FALSE)
##
for (i in 1:max.arms) {
sel.i <- !duplicated(tdat$studlab)
tdat.i <- merge(dat.studlab, tdat[sel.i, ],
by = "studlab", all.x = TRUE)
##
treat.list[[i]] <- tdat.i$treat
n.list[[i]] <- tdat.i$n
mean.list[[i]] <- tdat.i$mean
sd.list[[i]] <- tdat.i$sd
##
tdat.i$n <- NULL
tdat.i$mean <- NULL
tdat.i$sd <- NULL
##
if (!nulldata)
adddata[[i]] <- tdat.i
##
tdat <- tdat[!sel.i, ]
}
##
treat <- treat.list
n <- n.list
mean <- mean.list
sd <- sd.list
}
##
else if (type == "count") {
##
## Generate lists
##
tdat <- data.frame(studlab, treat, event, time,
.order = seq_along(studlab),
stringsAsFactors = FALSE)
##
if (!is.null(n))
tdat$n <- n
##
if (!nulldata) {
tdat <- cbind(tdat, data)
dupl <- duplicated(names(tdat))
if (any(dupl))
names(tdat)[dupl] <- paste(names(tdat)[dupl], "orig", sep = ".")
}
##
studlab <- names(n.arms)
dat.studlab <- data.frame(studlab, stringsAsFactors = FALSE)
##
for (i in 1:max.arms) {
sel.i <- !duplicated(tdat$studlab)
tdat.i <- merge(dat.studlab, tdat[sel.i, ],
by = "studlab", all.x = TRUE)
##
treat.list[[i]] <- tdat.i$treat
event.list[[i]] <- tdat.i$event
time.list[[i]] <- tdat.i$time
##
tdat.i$event <- NULL
tdat.i$time <- NULL
##
if (!nulldata)
adddata[[i]] <- tdat.i
##
tdat <- tdat[!sel.i, ]
}
##
treat <- treat.list
event <- event.list
time <- time.list
}
##
else if (type == "generic") {
##
## Generate lists
##
tdat <- data.frame(studlab, treat, TE, seTE,
.order = seq_along(studlab),
stringsAsFactors = FALSE)
##
if (!is.null(n))
tdat$n <- n
##
if (!is.null(event))
tdat$event <- event
##
if (!nulldata) {
tdat <- cbind(tdat, data)
dupl <- duplicated(names(tdat))
if (any(dupl))
names(tdat)[dupl] <- paste(names(tdat)[dupl], "orig", sep = ".")
}
##
studlab <- names(n.arms)
dat.studlab <- data.frame(studlab, stringsAsFactors = FALSE)
##
for (i in 1:max.arms) {
sel.i <- !duplicated(tdat$studlab)
tdat.i <- merge(dat.studlab, tdat[sel.i, ],
by = "studlab", all.x = TRUE)
##
treat.list[[i]] <- tdat.i$treat
TE.list[[i]] <- tdat.i$TE
seTE.list[[i]] <- tdat.i$seTE
##
tdat.i$TE <- NULL
tdat.i$seTE <- NULL
##
if (!nulldata)
adddata[[i]] <- tdat.i
##
tdat <- tdat[!sel.i, ]
}
##
treat <- treat.list
TE <- TE.list
seTE <- seTE.list
}
##
else if (type == "onlytreat") {
##
## Generate lists
##
tdat <- data.frame(studlab, treat,
.order = seq_along(studlab),
stringsAsFactors = FALSE)
##
if (!is.null(n))
tdat$n <- n
##
if (!nulldata) {
tdat <- cbind(tdat, data)
dupl <- duplicated(names(tdat))
if (any(dupl))
names(tdat)[dupl] <- paste(names(tdat)[dupl], "orig", sep = ".")
}
##
studlab <- names(n.arms)
dat.studlab <- data.frame(studlab, stringsAsFactors = FALSE)
##
for (i in 1:max.arms) {
sel.i <- !duplicated(tdat$studlab)
tdat.i <- merge(dat.studlab, tdat[sel.i, ],
by = "studlab", all.x = TRUE)
##
treat.list[[i]] <- tdat.i$treat
##
if (!nulldata)
adddata[[i]] <- tdat.i
##
tdat <- tdat[!sel.i, ]
}
##
treat <- treat.list
}
}
##
## Check and set study labels
##
if (is.null(studlab))
studlab <- seq(along = treat[[1]])
##
if (length(treat) != 2 && length(studlab) != length(unique(studlab)))
stop("Study labels must all be distinct.")
##
levs <- unique(studlab)
narms <- length(treat)
nstud <- length(studlab)
##
## Auxiliary functions
##
sumzero <- function(x)
sum(x[!is.na(x)] == 0)
##
anytrue <- function(x)
any(x == TRUE, na.rm = TRUE)
##
##
## Generate dataset with variables from original dataset
##
##
if (!nulldata & !wide.armbased) {
names.adddata <- names(adddata[[1]])
##
notunique <- matrix(NA,
ncol = length(names.adddata),
nrow = narms * (narms - 1) / 2)
colnames(notunique) <- names.adddata
##
oneNA <- matrix(NA,
ncol = length(names.adddata),
nrow = narms * (narms - 1) / 2)
##
allNA <- matrix(NA,
ncol = length(names.adddata),
nrow = narms * (narms - 1) / 2)
colnames(oneNA) <- names.adddata
##
n.ij <- 0
##
for (i in 1:(narms - 1)) {
for (j in (i + 1):narms) {
n.ij <- n.ij + 1
notunique[n.ij, ] <-
apply(adddata[[i]] != adddata[[j]], 2, anytrue)
##
allNA[n.ij, ] <- apply(is.na(adddata[[j]]), 2, all)
##
oneNA[n.ij, ] <-
apply(is.na(adddata[[i]]) & !is.na(adddata[[j]]), 2, anytrue)
}
}
##
notunique <- apply(notunique, 2, anytrue)
oneNA <- apply(oneNA, 2, anytrue)
allNA <- apply(allNA, 2, anytrue)
##print(apply(rbind(notunique, oneNA, allNA), 2, anytrue))
notunique <- apply(rbind(notunique, oneNA, allNA), 2, anytrue)
##
for (i in 1:(narms - 1)) {
for (j in (i + 1):narms) {
dat.i <- adddata[[i]]
dat.j <- adddata[[j]]
##
if (any(!notunique))
dat.ij <- dat.i[, names.adddata[!notunique], drop = FALSE]
else
stop("Study label must be unique for single treatment arm.")
##
for (nam in names.adddata[notunique]) {
dat.ij[, paste(nam, 1, sep = "")] <- adddata[[i]][nam]
dat.ij[, paste(nam, 2, sep = "")] <- adddata[[j]][nam]
}
##
if (i == 1 & j == 2)
newdata <- dat.ij
else
newdata <- rbind(newdata, dat.ij)
}
}
##
names.basic <- c("studlab", "treat1", "treat2")
names.newdata <- names(newdata)
##
newdata <- newdata[, c(names.basic,
names.newdata[!(names.newdata %in% names.basic)])]
newdata <- newdata[!is.na(newdata$treat1) & !is.na(newdata$treat2), ]
}
if (type == "binary") {
##
if (length(event) != narms)
stop("Different length of lists 'treat' and 'event'.")
if (length(n) != narms)
stop("Different length of lists 'treat' and 'n'.")
##
## Determine increment for individual studies
##
n.zeros <- apply(matrix(unlist(event), ncol = length(event)), 1, sumzero)
n.all <- apply(matrix(unlist(n), ncol = length(event)) -
matrix(unlist(event), ncol = length(event)),
1, sumzero)
##
incr.study <- rep(0, length(n.zeros))
##
if ("sm" %in% nam.args)
sm <- args$sm
else
sm <- gs("smbin")
##
sm <- setchar(sm, c("OR", "RD", "RR", "ASD"))
##
sparse <- switch(sm,
OR = (n.zeros > 0) | (n.all > 0),
RD = (n.zeros > 0) | (n.all > 0),
RR = (n.zeros > 0) | (n.all > 0),
ASD = rep(FALSE, length(n.zeros)))
##
if (!allincr & !addincr)
incr.study[sparse] <- incr
else if (addincr)
incr.study[] <- incr
else {
if (any(n.zeros > 0))
incr.study[] <- incr
else
incr.study[] <- 0
}
##
for (i in 1:(narms - 1)) {
##
if (i == 1 & (length(treat[[i]]) != length(event[[i]])))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'event'.")
if (i == 1 & (length(event[[i]]) != length(n[[i]])))
stop("Different length of element ", i, " of ",
"lists 'event' and 'n'.")
##
for (j in (i + 1):narms) {
##
if (length(treat[[j]]) != length(event[[j]]))
stop("Different length of element ", j, " of ",
"lists 'treat' and 'event'.")
if (length(event[[j]]) != length(n[[j]]))
stop("Different length of element ", j, " of ",
"lists 'event' and 'n'.")
##
dat <- data.frame(TE = NA, seTE = NA,
studlab,
treat1 = treat[[i]], treat2 = treat[[j]],
event1 = event[[i]], n1 = n[[i]],
event2 = event[[j]], n2 = n[[j]],
.order = seq_along(studlab),
incr = incr.study,
allstudies = allstudies,
stringsAsFactors = FALSE,
row.names = NULL)
##
if (wide.armbased & !nulldata) {
dat <- cbind(dat, data, stringsAsFactors = FALSE)
dupl <- duplicated(names(dat))
if (any(dupl))
names(dat)[dupl] <- paste(names(dat)[dupl], "orig", sep = ".")
}
##
dat <- dat[!(is.na(dat$event1) & is.na(dat$n1)), ]
dat <- dat[!(is.na(dat$event2) & is.na(dat$n2)), ]
##
if (nrow(dat) > 0) {
m1 <- metabin(dat$event1, dat$n1,
dat$event2, dat$n2,
incr = dat$incr, addincr = TRUE,
allstudies = allstudies,
method.tau = "DL", method.tau.ci = "",
warn = warn,
warn.deprecated = FALSE, ...)
##
dat$TE <- m1$TE
dat$seTE <- m1$seTE
##
dat$TE[is.infinite(dat$TE)] <- NA
dat$seTE[is.infinite(dat$seTE)] <- NA
##
dat.NAs <- dat[is.na(dat$TE) | is.na(dat$seTE) | dat$seTE <= 0, ]
##
if (i == 1 & j == 2) {
res <- dat
res.NAs <- dat.NAs
}
else {
res <- rbind(res, dat)
res.NAs <- rbind(res.NAs, dat.NAs)
}
}
else
if (i == 1 & j == 2)
stop("No studies available for comparison of ",
"first and second treatment.")
}
}
}
#
else if (type == "continuous") {
if (length(n) != narms)
stop("Different length of lists 'treat' and 'n'.")
if (length(mean) != narms)
stop("Different length of lists 'treat' and 'mean'.")
if (length(sd) != narms)
stop("Different length of lists 'treat' and 'sd'.")
##
for (i in seq_len(narms)) {
##
if (length(treat[[i]]) != length(n[[i]]))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'n'.",
call. = FALSE)
if (length(treat[[i]]) != length(mean[[i]]))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'mean'.",
call. = FALSE)
if (length(treat[[i]]) != length(sd[[i]]))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'sd'.",
call. = FALSE)
if (length(treat[[i]]) != nstud)
stop("Different length of study labels and ",
"element ", i, " of list 'treat'.",
call. = FALSE)
}
##
## For standardized mean difference, calculate pooled standard
## deviation for multi-arm studies
##
if ("sm" %in% nam.args && (tolower(args$sm) == "smd" & narms > 2)) {
pooled.sd <- function(sd, n) {
sel <- !is.na(sd) & !is.na(n)
##
if (any(sel))
res <- sqrt(sum((n[sel] - 1) * sd[sel]^2) / sum(n[sel] - 1))
else
res <- NA
##
res
}
##
N <- matrix(unlist(n), ncol = narms, nrow = nstud, byrow = FALSE)
M <- matrix(unlist(mean), ncol = narms, nrow = nstud, byrow = FALSE)
S <- matrix(unlist(sd), ncol = narms, nrow = nstud, byrow = FALSE)
##
sel.n <- apply(!is.na(N) & N > 0, 1, sum) > 2
sel.mean <- apply(!is.na(M), 1, sum) > 2
sel.sd <- apply(!is.na(S) & S > 0, 1, sum) > 2
sel <- sel.n & sel.mean & sel.sd
##
if (any(sel)) {
N <- N[sel, , drop = FALSE]
S <- S[sel, , drop = FALSE]
sd.p <- rep_len(NA, nrow(N))
##
for (i in seq_len(nrow(N)))
sd.p[i] <- pooled.sd(S[i, ], N[i, ])
}
##
for (i in seq_len(narms))
sd[[i]][sel] <- ifelse(is.na(sd[[i]][sel]), NA, sd.p)
}
##
for (i in 1:(narms - 1)) {
for (j in (i + 1):narms) {
dat <- data.frame(TE = NA, seTE = NA,
studlab,
treat1 = treat[[i]],
treat2 = treat[[j]],
n1 = n[[i]], mean1 = mean[[i]], sd1 = sd[[i]],
n2 = n[[j]], mean2 = mean[[j]], sd2 = sd[[j]],
.order = seq_along(studlab),
stringsAsFactors = FALSE,
row.names = NULL)
##
if (wide.armbased) {
dat <- cbind(dat, data, stringsAsFactors = FALSE)
dupl <- duplicated(names(dat))
if (any(dupl))
names(dat)[dupl] <- paste(names(dat)[dupl], "orig", sep = ".")
}
##
dat <- dat[!(is.na(dat$n1) & is.na(dat$mean1) & is.na(dat$sd1)), ]
dat <- dat[!(is.na(dat$n2) & is.na(dat$mean2) & is.na(dat$sd2)), ]
##
if (nrow(dat) > 0) {
m1 <- metacont(dat$n1, dat$mean1, dat$sd1,
dat$n2, dat$mean2, dat$sd2,
method.tau = "DL", method.tau.ci = "",
method.smd = "Cohen",
warn = warn,
warn.deprecated = FALSE, ...)
##
dat$TE <- m1$TE
dat$seTE <- m1$seTE
##
dat$TE[is.infinite(dat$TE)] <- NA
dat$seTE[is.infinite(dat$seTE)] <- NA
##
dat.NAs <- dat[is.na(dat$TE) | is.na(dat$seTE) | dat$seTE <= 0, ]
##
if (i == 1 & j == 2) {
res <- dat
res.NAs <- dat.NAs
}
else {
res <- rbind(res, dat)
res.NAs <- rbind(res.NAs, dat.NAs)
}
}
else
if (i == 1 & j == 2)
stop("No studies available for comparison of ",
"first and second treatment.",
call. = FALSE)
}
}
}
#
else if (type == "generic") {
if (length(TE) != narms)
stop("Different length of lists 'treat' and 'TE'.",
call. = FALSE)
if (length(seTE) != narms)
stop("Different length of lists 'treat' and 'seTE'.",
call. = FALSE)
##
for (i in 1:(narms - 1)) {
##
if (i == 1 & (length(treat[[i]]) != length(TE[[i]])))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'TE'.",
call. = FALSE)
if (i == 1 & (length(treat[[i]]) != length(seTE[[i]])))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'seTE'.",
call. = FALSE)
##
for (j in (i + 1):narms) {
##
if (length(treat[[j]]) != length(TE[[j]]))
stop("Different length of element ", j, " of ",
"lists 'treat' and 'TE'.",
call. = FALSE)
if (length(treat[[j]]) != length(seTE[[j]]))
stop("Different length of element ", j, " of ",
"lists 'treat' and 'seTE'.",
call. = FALSE)
##
dat <- data.frame(TE = NA, seTE = NA,
studlab,
treat1 = treat[[i]],
treat2 = treat[[j]],
TE1 = TE[[i]], seTE1 = seTE[[i]],
TE2 = TE[[j]], seTE2 = seTE[[j]],
.order = seq_along(studlab),
stringsAsFactors = FALSE,
row.names = NULL)
##
if (!is.null(event)) {
dat$event1 <- event[[i]]
dat$event2 <- event[[j]]
}
##
if (!is.null(n)) {
dat$n1 <- n[[i]]
dat$n2 <- n[[j]]
}
##
if (!is.null(mean)) {
dat$mean1 <- mean[[i]]
dat$mean2 <- mean[[j]]
}
##
if (!is.null(sd)) {
dat$sd1 <- sd[[i]]
dat$sd2 <- sd[[j]]
}
##
if (!is.null(time)) {
dat$time1 <- time[[i]]
dat$time2 <- time[[j]]
}
##
if (wide.armbased) {
dat <- cbind(dat, data, stringsAsFactors = FALSE)
dupl <- duplicated(names(dat))
if (any(dupl))
names(dat)[dupl] <- paste(names(dat)[dupl], "orig", sep = ".")
}
##
dat <- dat[!(is.na(dat$TE1) & is.na(dat$seTE1)), ]
dat <- dat[!(is.na(dat$TE2) & is.na(dat$seTE2)), ]
##
if (nrow(dat) > 0) {
m1 <- metagen(dat$TE1 - dat$TE2,
sqrt(dat$seTE1^2 + dat$seTE2^2),
method.tau = "DL", method.tau.ci = "",
warn = warn,
warn.deprecated = FALSE, ...)
##
dat$TE <- m1$TE
dat$seTE <- m1$seTE
##
dat$TE[is.infinite(dat$TE)] <- NA
dat$seTE[is.infinite(dat$seTE)] <- NA
##
dat.NAs <- dat[is.na(dat$TE) | is.na(dat$seTE) | dat$seTE <= 0, ]
##
if (i == 1 & j == 2) {
res <- dat
res.NAs <- dat.NAs
}
else {
res <- rbind(res, dat)
res.NAs <- rbind(res.NAs, dat.NAs)
}
}
else
if (i == 1 & j == 2)
stop("No studies available for comparison of ",
"first and second treatment.",
call. = FALSE)
}
}
}
#
else if (type == "count") {
if (length(event) != narms)
stop("Different length of lists 'treat' and 'event'.",
call. = FALSE)
if (length(time) != narms)
stop("Different length of lists 'treat' and 'time'.",
call. = FALSE)
##
## Determine increment for individual studies
##
n.zeros <- apply(matrix(unlist(event), ncol = length(event)), 1, sumzero)
##
incr.study <- rep(0, length(n.zeros))
##
sparse <- n.zeros > 0
##
if (!allincr & !addincr)
incr.study[sparse] <- incr
else if (addincr)
incr.study[] <- incr
else {
if (any(n.zeros > 0))
incr.study[] <- incr
else
incr.study[] <- 0
}
##
for (i in 1:(narms - 1)) {
##
if (i == 1 & (length(treat[[i]]) != length(event[[i]])))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'event'.",
call. = FALSE)
if (i == 1 & (length(treat[[i]]) != length(time[[i]])))
stop("Different length of element ", i, " of ",
"lists 'treat' and 'time'.",
call. = FALSE)
##
for (j in (i + 1):narms) {
##
if (length(treat[[j]]) != length(event[[j]]))
stop("Different length of element ", j, " of ",
"lists 'treat' and 'event'.",
call. = FALSE)
if (length(treat[[j]]) != length(time[[j]]))
stop("Different length of element ", j, " of ",
"lists 'treat' and 'time'.",
call. = FALSE)
##
dat <- data.frame(TE = NA, seTE = NA,
studlab,
treat1 = treat[[i]],
treat2 = treat[[j]],
event1 = event[[i]], time1 = time[[i]],
event2 = event[[j]], time2 = time[[j]],
incr = incr.study,
.order = seq_along(studlab),
stringsAsFactors = FALSE,
row.names = NULL)
##
if (wide.armbased) {
dat <- cbind(dat, data, stringsAsFactors = FALSE)
dupl <- duplicated(names(dat))
if (any(dupl))
names(dat)[dupl] <- paste(names(dat)[dupl], "orig", sep = ".")
}
##
dat <- dat[!(is.na(dat$event1) & is.na(dat$time1)), ]
dat <- dat[!(is.na(dat$event2) & is.na(dat$time2)), ]
##
if (nrow(dat) > 0) {
m1 <- metainc(dat$event1, dat$time1,
dat$event2, dat$time2,
incr = dat$incr, addincr = TRUE,
allstudies = allstudies,
method.tau = "DL", method.tau.ci = "",
warn = warn,
warn.deprecated = FALSE, ...)
##
dat$TE <- m1$TE
dat$seTE <- m1$seTE
##
dat$TE[is.infinite(dat$TE)] <- NA
dat$seTE[is.infinite(dat$seTE)] <- NA
##
dat.NAs <- dat[is.na(dat$TE) | is.na(dat$seTE) | dat$seTE <= 0, ]
##
if (i == 1 & j == 2) {
res <- dat
res.NAs <- dat.NAs
}
else {
res <- rbind(res, dat)
res.NAs <- rbind(res.NAs, dat.NAs)
}
}
else
if (i == 1 & j == 2)
stop("No studies available for comparison of ",
"first and second treatment.",
call. = FALSE)
}
}
}
#
else if (type == "onlytreat") {
for (i in 1:(narms - 1)) {
for (j in (i + 1):narms) {
##
dat <- data.frame(studlab,
treat1 = treat[[i]],
treat2 = treat[[j]],
.order = seq_along(studlab),
stringsAsFactors = FALSE,
row.names = NULL)
##
if (!is.null(n)) {
dat$n1 <- n[[i]]
dat$n2 <- n[[j]]
}
##
if (wide.armbased) {
dat <- cbind(dat, data, stringsAsFactors = FALSE)
dupl <- duplicated(names(dat))
if (any(dupl))
names(dat)[dupl] <- paste(names(dat)[dupl], "orig", sep = ".")
}
##
dat <- dat[!is.na(dat$treat2), ]
##
if (nrow(dat) > 0) {
dat.NAs <- data.frame()
##
if (i == 1 & j == 2) {
res <- dat
res.NAs <- dat.NAs
}
else {
res <- rbind(res, dat)
res.NAs <- rbind(res.NAs, dat.NAs)
}
}
else
if (i == 1 & j == 2)
stop("No studies available for comparison of ",
"first and second treatment.",
call. = FALSE)
}
}
}
##
if (!nulldata & !wide.armbased)
res <- merge(res, newdata,
by = c("studlab", "treat1", "treat2"),
suffixes = c("",".orig"),
all.x = TRUE)
##
## Additional checks
##
##
## a) Duplicate treatments ?
##
sel.treat <- as.character(res$treat1) == as.character(res$treat2)
##
if (any(sel.treat)) {
sel.stud <- unique(sort(res$studlab[sel.treat]))
##
stop(paste0("Identical treatments for the following stud",
if (length(sel.stud) == 1) "y: " else "ies:\n ",
paste0(paste0("'", sel.stud, "'"),
collapse = " - "),
"\n Please check dataset."),
call. = FALSE)
}
##
## b) Studies missing ?
##
sel.study <- !(studlab %in% unique(as.character(res$studlab)))
##
if (any(sel.study) & warn)
warning(paste0("The following stud",
if (sum(sel.study) == 1) "y is " else "ies are ",
"excluded from the analysis\n ",
"(due to a single study arm or missing values):",
if (sum(sel.study) == 1) " " else "\n ",
paste0(paste0("'", studlab[sel.study], "'"),
collapse = " - ")),
call. = FALSE)
##
## c) Missing treatment estimates or standard errors?
##
if (type != "onlytreat" && nrow(res.NAs) > 0 & warn) {
warning("Comparison",
if (nrow(res.NAs) > 1) "s",
" with missing TE / seTE or zero seTE",
" will not be considered in network meta-analysis.",
call. = FALSE)
cat(paste("Comparison",
if (nrow(res.NAs) > 1) "s",
" will not be considered in network meta-analysis:\n",
sep = ""))
##
res.NAs$.order <- NULL
res.NAs$.order1 <- NULL
res.NAs$.order2 <- NULL
##
prmatrix(res.NAs,
quote = FALSE, right = TRUE, na.print = "NA",
rowlab = rep("", nrow(res.NAs)))
}
## Calculate standard error for SMDs
##
if (type == "continuous" && m1$sm == "SMD") {
res$.seTE <- res$seTE
##
for (i in unique(res$studlab)) {
sel.i <- res$studlab == i
dat.i <- res[sel.i, ]
##
## Calculate total sample size in study i
##
ndat.i <- rbind(data.frame(treat = dat.i$treat1, n = dat.i$n1),
data.frame(treat = dat.i$treat2, n = dat.i$n2))
n.i <- sum(ndat.i[!duplicated(ndat.i), ]$n)
##
## Crippa & Orsini (2016), BMC Med Res Meth, equation (5)
##
varTE.i <-
1 / res$n1[sel.i] + 1 / res$n2[sel.i] + res$TE[sel.i]^2 / (2 * n.i)
##
res$seTE[sel.i] <- sqrt(varTE.i)
}
}
attr(res, "sm") <- if (type != "onlytreat") m1$sm else ""
attr(res, "method") <- if (type != "onlytreat") m1$method else ""
attr(res, "incr") <- incr
attr(res, "allincr") <- allincr
attr(res, "addincr") <- addincr
attr(res, "allstudies") <- allstudies
if (!is.null(res$.order1)) {
res <- res[order(res$.order1), ]
res$.order1 <- NULL
res$.order2 <- NULL
res$.order <- NULL
res <- unique(res)
}
else if (!is.null(res$.order)) {
res <- res[order(res$.order), ]
res$.order <- NULL
res$.order.orig <- NULL
res <- unique(res)
}
else {
res <- res[order(factor(res$studlab, levels = levs),
res$treat1, res$treat2), ]
}
##
rownames(res) <- 1:nrow(res)
}
##
## Use first treatment with estimable effect as reference if
## argument is missing
##
labels <- unique(sort(c(res$treat1, res$treat2)))
##
if (missing.reference.group) {
if (type == "onlytreat")
reference.group <- ""
else {
go.on <- TRUE
i <- 0
while (go.on) {
i <- i + 1
sel.i <-
!is.na(res$TE) & !is.na(res$seTE) &
(res$treat1 == labels[i] | res$treat2 == labels[i])
if (sum(sel.i) > 0) {
go.on <- FALSE
reference.group <- labels[i]
}
else if (i == length(labels)) {
go.on <- FALSE
reference.group <- ""
}
}
}
}
##
if (is.factor(reference.group))
reference.group <- as.character(reference.group)
##
if (is.numeric(reference.group))
chknumeric(reference.group, length = 1)
else
chkchar(reference.group, length = 1)
##
reference.group <- setchar(reference.group, c(labels, ""))
##
if (!keep.all.comparisons) {
##
drop <- logical(0)
##
for (i in unique(res$studlab)) {
d.i <- res[res$studlab == i, , drop = FALSE]
trts.i <- unique(sort(c(d.i$treat1, d.i$treat2)))
##
## Keep comparisons with reference group or first treatment if
## reference treatment is missing in study
##
if (reference.group %in% trts.i)
ref.i <- reference.group
else
ref.i <- rev(trts.i)[1]
##
drop.i <- d.i$treat1 != ref.i & d.i$treat2 != ref.i
##
drop <- c(drop, drop.i)
}
##
if (!keep.all.comparisons)
res <- res[!drop, , drop = FALSE]
}
attr(res, "pairwise") <- TRUE
attr(res, "reference.group") <- reference.group
attr(res, "keep.all.comparisons") <- keep.all.comparisons
attr(res, "type") <- type
attr(res, "version") <- packageDescription("netmeta")$Version
class(res) <- c("pairwise", class(res))
res
}
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