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R/metaprop.R
In meta: General Package for Meta-Analysis
Defines functions
metaprop
Documented in
metaprop
#' Meta-analysis of single proportions
#'
#' @description
#' Calculation of an overall proportion from studies reporting a
#' single proportion. Inverse variance method and generalised linear
#' mixed model (GLMM) are available for pooling. For GLMMs, the
#' \code{\link[metafor]{rma.glmm}} function from R package
#' \bold{metafor} (Viechtbauer 2010) is called internally.
#'
#' @param event Number of events.
#' @param n Number of observations.
#' @param studlab An optional vector with study labels.
#' @param data An optional data frame containing the study
#' information, i.e., event and n.
#' @param subset An optional vector specifying a subset of studies to
#' be used.
#' @param exclude An optional vector specifying studies to exclude
#' from meta-analysis, however, to include in printouts and forest
#' plots.
#' @param cluster An optional vector specifying which estimates come
#' from the same cluster resulting in the use of a three-level
#' meta-analysis model.
#' @param method A character string indicating which method is to be
#' used for pooling of studies. One of \code{"Inverse"} and
#' \code{"GLMM"}, can be abbreviated.
#' @param sm A character string indicating which summary measure
#' (\code{"PLOGIT"}, \code{"PAS"}, \code{"PFT"}, \code{"PLN"}, or
#' \code{"PRAW"}) is to be used for pooling of studies, see Details.
#' @param incr A numeric which is added to event number and sample
#' size of studies with zero or all events, i.e., studies with an
#' event probability of either 0 or 1.
#' @param method.incr A character string indicating which continuity
#' correction method should be used (\code{"only0"},
#' \code{"if0all"}, or \code{"all"}), see Details.
#' @param method.ci A character string indicating which method is used
#' to calculate confidence intervals for individual studies, see
#' Details.
#' @param level The level used to calculate confidence intervals for
#' individual studies.
#' @param common A logical indicating whether a common effect
#' meta-analysis should be conducted.
#' @param random A logical indicating whether a random effects
#' meta-analysis should be conducted.
#' @param overall A logical indicating whether overall summaries
#' should be reported. This argument is useful in a meta-analysis
#' with subgroups if overall results should not be reported.
#' @param overall.hetstat A logical value indicating whether to print
#' heterogeneity measures for overall treatment comparisons. This
#' argument is useful in a meta-analysis with subgroups if
#' heterogeneity statistics should only be printed on subgroup
#' level.
#' @param prediction A logical indicating whether a prediction
#' interval should be printed.
#' @param method.tau A character string indicating which method is
#' used to estimate the between-study variance \eqn{\tau^2} and its
#' square root \eqn{\tau} (see \code{\link{meta-package}}).
#' @param method.tau.ci A character string indicating which method is
#' used to estimate the confidence interval of \eqn{\tau^2} and
#' \eqn{\tau} (see \code{\link{meta-package}}).
#' @param tau.preset Prespecified value for the square root of the
#' between-study variance \eqn{\tau^2}.
#' @param TE.tau Overall treatment effect used to estimate the
#' between-study variance tau-squared.
#' @param tau.common A logical indicating whether tau-squared should
#' be the same across subgroups.
#' @param level.ma The level used to calculate confidence intervals
#' for meta-analysis estimates.
#' @param method.random.ci A character string indicating which method
#' is used to calculate confidence interval and test statistic for
#' random effects estimate (see \code{\link{meta-package}}).
#' @param adhoc.hakn.ci A character string indicating whether an
#' \emph{ad hoc} variance correction should be applied in the case
#' of an arbitrarily small Hartung-Knapp variance estimate (see
#' \code{\link{meta-package}}).
#' @param level.predict The level used to calculate prediction
#' interval for a new study.
#' @param method.predict A character string indicating which method is
#' used to calculate a prediction interval (see
#' \code{\link{meta-package}}).
#' @param adhoc.hakn.pi A character string indicating whether an
#' \emph{ad hoc} variance correction should be applied for
#' prediction interval (see \code{\link{meta-package}}).
#' @param seed.predict A numeric value used as seed to calculate
#' bootstrap prediction interval (see \code{\link{meta-package}}).
#' @param null.effect A numeric value specifying the effect under the
#' null hypothesis.
#' @param method.bias A character string indicating which test is to
#' be used. Either \code{"Begg"}, \code{"Egger"}, or
#' \code{"Thompson"}, can be abbreviated. See function
#' \code{\link{metabias}}.
#' @param backtransf A logical indicating whether results for
#' transformed proportions (argument \code{sm != "PRAW"}) should be
#' back transformed in printouts and plots. If TRUE (default),
#' results will be presented as proportions; otherwise transformed
#' proportions will be shown. See Details for presentation of
#' confidence intervals.
#' @param pscale A numeric defining a scaling factor for printing of
#' single event probabilities.
#' @param text.common A character string used in printouts and forest
#' plot to label the pooled common effect estimate.
#' @param text.random A character string used in printouts and forest
#' plot to label the pooled random effects estimate.
#' @param text.predict A character string used in printouts and forest
#' plot to label the prediction interval.
#' @param text.w.common A character string used to label weights of
#' common effect model.
#' @param text.w.random A character string used to label weights of
#' random effects model.
#' @param title Title of meta-analysis / systematic review.
#' @param complab Comparison label.
#' @param outclab Outcome label.
#' @param subgroup An optional vector to conduct a meta-analysis with
#' subgroups.
#' @param subgroup.name A character string with a name for the
#' subgroup variable.
#' @param print.subgroup.name A logical indicating whether the name of
#' the subgroup variable should be printed in front of the group
#' labels.
#' @param sep.subgroup A character string defining the separator
#' between name of subgroup variable and subgroup label.
#' @param test.subgroup A logical value indicating whether to print
#' results of test for subgroup differences.
#' @param prediction.subgroup A logical indicating whether prediction
#' intervals should be printed for subgroups.
#' @param seed.predict.subgroup A numeric vector providing seeds to
#' calculate bootstrap prediction intervals within subgroups. Must
#' be of same length as the number of subgroups.
#' @param byvar Deprecated argument (replaced by 'subgroup').
#' @param hakn Deprecated argument (replaced by 'method.random.ci').
#' @param adhoc.hakn Deprecated argument (replaced by 'adhoc.hakn.ci').
#' @param keepdata A logical indicating whether original data (set)
#' should be kept in meta object.
#' @param warn A logical indicating whether the addition of
#' \code{incr} to studies with zero or all events should result in a
#' warning.
#' @param warn.deprecated A logical indicating whether warnings should
#' be printed if deprecated arguments are used.
#' @param control An optional list to control the iterative process to
#' estimate the between-study variance \eqn{\tau^2}. This argument
#' is passed on to \code{\link[metafor]{rma.uni}} or
#' \code{\link[metafor]{rma.glmm}}, respectively.
#' @param \dots Additional arguments passed on to
#' \code{\link[metafor]{rma.glmm}} function and to catch deprecated
#' arguments.
#'
#' @details
#' This function provides methods for common effect and random effects
#' meta-analysis of single proportions to calculate an overall
#' proportion. Note, you should use R function \code{\link{metabin}}
#' to compare proportions of pairwise comparisons instead of using
#' \code{metaprop} for each treatment arm separately which will break
#' randomisation in randomised controlled trials.
#'
#' The following transformations of proportions are
#' implemented to calculate an overall proportion:
#'
#' \itemize{
#' \item Logit transformation (\code{sm = "PLOGIT"}, default)
#' \item Arcsine transformation (\code{sm = "PAS"})
#' \item Freeman-Tukey Double arcsine transformation (\code{sm = "PFT"})
#' \item Log transformation (\code{sm = "PLN"})
#' \item No transformation (\code{sm = "PRAW"})
#' }
#'
#' List elements \code{TE}, \code{TE.common}, \code{TE.random}, etc.,
#' contain the transformed proportions. In printouts and plots these
#' values are back transformed if argument \code{backtransf = TRUE}
#' (default).
#'
#' A generalised linear mixed model (GLMM) - more specific, a random
#' intercept logistic regression model - can be utilised for the
#' meta-analysis of proportions (Stijnen et al., 2010). This is the
#' default method for the logit transformation (argument \code{sm =
#' "PLOGIT"}). Internally, the \code{\link[metafor]{rma.glmm}}
#' function from R package \bold{metafor} is called to fit a GLMM.
#'
#' Classic meta-analysis (Borenstein et al., 2010) utilising the
#' (un)transformed proportions and corresponding standard errors in
#' the inverse variance method is conducted by calling the
#' \code{\link{metagen}} function internally. This is the only
#' available method for all transformations but the logit
#' transformation. The classic meta-analysis model with logit
#' transformed proportions is used by setting argument \code{method =
#' "Inverse"}.
#'
#' A three-level random effects meta-analysis model (Van den Noortgate
#' et al., 2013) is utilized if argument \code{cluster} is used and at
#' least one cluster provides more than one estimate. Internally,
#' \code{\link[metafor]{rma.mv}} is called to conduct the analysis and
#' \code{\link[metafor]{weights.rma.mv}} with argument \code{type =
#' "rowsum"} is used to calculate random effects weights.
#'
#' Default settings are utilised for several arguments (assignments
#' using \code{\link{gs}} function). These defaults can be changed for
#' the current R session using the \code{\link{settings.meta}}
#' function.
#'
#' Furthermore, R function \code{\link{update.meta}} can be used to
#' rerun a meta-analysis with different settings.
#'
#' \subsection{Choice of transformation / meta-analysis method}{
#'
#' Contradictory recommendations on the use of transformations of
#' proportions have been published in the literature. For example,
#' Barendregt et al. (2013) recommend the use of the Freeman-Tukey
#' double arcsine transformation instead of the logit transformation
#' whereas Warton & Hui (2011) strongly advise to use generalised
#' linear mixed models with the logit transformation instead of the
#' arcsine transformation.
#'
#' Schwarzer et al. (2019) describe seriously misleading results in a
#' meta-analysis with very different sample sizes due to problems with
#' the back-transformation of the Freeman-Tukey transformation which
#' requires a single sample size (Miller, 1978). Accordingly,
#' Schwarzer et al. (2019) also recommend to use GLMMs for the
#' meta-analysis of single proportions, however, admit that individual
#' study weights are not available with this method. Meta-analysts
#' which require individual study weights should consider the inverse
#' variance method with the arcsine or logit transformation.
#'
#' In order to prevent misleading conclusions for the Freeman-Tukey
#' double arcsine transformation, sensitivity analyses using other
#' transformations or using a range of sample sizes should be
#' conducted (Schwarzer et al., 2019).
#' }
#'
#' \subsection{Continuity correction}{
#'
#' Three approaches are available to apply a continuity correction:
#' \itemize{
#' \item Only studies with a zero cell count (\code{method.incr =
#' "only0"})
#' \item All studies if at least one study has a zero cell count
#' (\code{method.incr = "if0all"})
#' \item All studies irrespective of zero cell counts
#' (\code{method.incr = "all"})
#' }
#'
#' If the summary measure is equal to "PLOGIT", "PLN", or "PRAW", the
#' continuity correction is applied if a study has either zero or all
#' events, i.e., an event probability of either 0 or 1.
#'
#' By default, 0.5 is used as continuity correction (argument
#' \code{incr}). 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 GLMMs no
#' continuity correction is used.
#' }
#'
#' \subsection{Confidence intervals for individual studies}{
#'
#' Various methods are available to calculate confidence intervals for
#' individual study results (see Agresti & Coull 1998 and Newcombe
#' 1988):
#' \itemize{
#' \item Clopper-Pearson interval also called 'exact' binomial
#' interval (\code{method.ci = "CP"}, default)
#' \item Wilson Score interval (\code{method.ci = "WS"})
#' \item Wilson Score interval with continuity correction
#' (\code{method.ci = "WSCC"})
#' \item Agresti-Coull interval (\code{method.ci = "AC"})
#' \item Simple approximation interval (\code{method.ci = "SA"})
#' \item Simple approximation interval with continuity correction
#' (\code{method.ci = "SACC"})
#' \item Normal approximation interval based on summary measure,
#' i.e. defined by argument \code{sm} (\code{method.ci = "NAsm"})
#' }
#'
#' Note, with exception of the normal approximation based on the
#' summary measure, i.e. \code{method.ci = "NAsm"}, the same
#' confidence interval is calculated for individual studies for any
#' summary measure (argument \code{sm}) as only number of events and
#' observations are used in the calculation disregarding the chosen
#' transformation.
#'
#' Results will be presented for transformed proportions if argument
#' \code{backtransf = FALSE}. In this case, argument \code{method.ci =
#' "NAsm"} is used, i.e. confidence intervals based on the normal
#' approximation based on the summary measure.
#' }
#'
#' \subsection{Subgroup analysis}{
#'
#' Argument \code{subgroup} can be used to conduct subgroup analysis for
#' a categorical covariate. The \code{\link{metareg}} function can be
#' used instead for more than one categorical covariate or continuous
#' covariates.
#' }
#'
#' \subsection{Specify the null hypothesis of test for an overall proportion}{
#'
#' Argument \code{null.effect} can be used to specify the proportion
#' used under the null hypothesis in a test for an overall effect.
#'
#' By default (\code{null.effect = NA}), no hypothesis test is
#' conducted as it is unclear which value is a sensible choice for the
#' data at hand. An overall proportion of 50\%, for example, could be
#' tested by setting argument \code{null.effect = 0.5}.
#'
#' Note, all tests for an overall effect are two-sided with the
#' alternative hypothesis that the effect is unequal to
#' \code{null.effect}.
#' }
#'
#' \subsection{Exclusion of studies from meta-analysis}{
#'
#' Arguments \code{subset} and \code{exclude} can be used to exclude
#' studies from the meta-analysis. Studies are removed completely from
#' the meta-analysis using argument \code{subset}, while excluded
#' studies are shown in printouts and forest plots using argument
#' \code{exclude} (see Examples in \code{\link{metagen}}).
#' Meta-analysis results are the same for both arguments.
#' }
#'
#' \subsection{Presentation of meta-analysis results}{
#'
#' Internally, both common effect and random effects models are
#' calculated regardless of values choosen for arguments
#' \code{common} and \code{random}. Accordingly, the estimate
#' for the random effects model can be extracted from component
#' \code{TE.random} of an object of class \code{"meta"} even if
#' argument \code{random = FALSE}. However, all functions in R
#' package \bold{meta} will adequately consider the values for
#' \code{common} and \code{random}. E.g. function
#' \code{\link{print.meta}} will not print results for the random
#' effects model if \code{random = FALSE}.
#'
#' Argument \code{pscale} can be used to rescale proportions, e.g.
#' \code{pscale = 1000} means that proportions are expressed as events
#' per 1000 observations. This is useful in situations with (very) low
#' event probabilities.
#'
#' A prediction interval will only be shown if \code{prediction =
#' TRUE}.
#' }
#'
#' @return
#' An object of class \code{c("metaprop", "meta")} with corresponding
#' generic functions (see \code{\link{meta-object}}).
#'
#' @author Guido Schwarzer \email{guido.schwarzer@@uniklinik-freiburg.de}
#'
#' @seealso \code{\link{meta-package}}, \code{\link{update.meta}},
#' \code{\link{metacont}}, \code{\link{metagen}},
#' \code{\link{print.meta}}, \code{\link{forest.meta}}
#'
#' @references
#' Agresti A & Coull BA (1998):
#' Approximate is better than "exact" for interval estimation of
#' binomial proportions.
#' \emph{The American Statistician},
#' \bold{52}, 119--26
#'
#' Barendregt JJ, Doi SA, Lee YY, Norman RE, Vos T (2013):
#' Meta-analysis of prevalence.
#' \emph{Journal of Epidemiology and Community Health},
#' \bold{67}, 974--8
#'
#' Borenstein M, Hedges LV, Higgins JP, Rothstein HR (2010):
#' A basic introduction to fixed-effect and random-effects models for
#' meta-analysis.
#' \emph{Research Synthesis Methods},
#' \bold{1}, 97--111
#'
#' Freeman MF & Tukey JW (1950):
#' Transformations related to the angular and the square root.
#' \emph{Annals of Mathematical Statistics},
#' \bold{21}, 607--11
#'
#' Miller JJ (1978):
#' The inverse of the Freeman-Tukey double arcsine transformation.
#' \emph{The American Statistician},
#' \bold{32}, 138
#'
#' Newcombe RG (1998):
#' Two-sided confidence intervals for the single proportion:
#' comparison of seven methods.
#' \emph{Statistics in Medicine},
#' \bold{17}, 857--72
#'
#' Pettigrew HM, Gart JJ, Thomas DG (1986):
#' The bias and higher cumulants of the logarithm of a binomial
#' variate.
#' \emph{Biometrika},
#' \bold{73}, 425--35
#'
#' Schwarzer G, Chemaitelly H, Abu-Raddad LJ, Rücker G (2019):
#' Seriously misleading results using inverse of Freeman-Tukey double
#' arcsine transformation in meta-analysis of single proportions.
#' \emph{Research Synthesis Methods},
#' \bold{10}, 476--83
#'
#' Stijnen T, Hamza TH, Ozdemir P (2010):
#' Random effects meta-analysis of event outcome in the framework of
#' the generalized linear mixed model with applications in sparse
#' data.
#' \emph{Statistics in Medicine},
#' \bold{29}, 3046--67
#'
#' Van den Noortgate W, López-López JA, Marín-Martínez F, Sánchez-Meca J (2013):
#' Three-level meta-analysis of dependent effect sizes.
#' \emph{Behavior Research Methods},
#' \bold{45}, 576--94
#'
#' Viechtbauer W (2010):
#' Conducting meta-analyses in R with the metafor package.
#' \emph{Journal of Statistical Software},
#' \bold{36}, 1--48
#'
#' Warton DI, Hui FKC (2011):
#' The arcsine is asinine: the analysis of proportions in ecology.
#' \emph{Ecology},
#' \bold{92}, 3--10
#'
#' @examples
#' # Meta-analysis using generalised linear mixed model
#' #
#' metaprop(4:1, 10 * 1:4)
#'
#' # Apply various classic meta-analysis methods to estimate
#' # proportions
#' #
#' m1 <- metaprop(4:1, 10 * 1:4, method = "Inverse")
#' m2 <- update(m1, sm = "PAS")
#' m3 <- update(m1, sm = "PRAW")
#' m4 <- update(m1, sm = "PLN")
#' m5 <- update(m1, sm = "PFT")
#' #
#' m1
#' m2
#' m3
#' m4
#' m5
#' #
#' forest(m1)
#' \dontrun{
#' forest(m2)
#' forest(m3)
#' forest(m3, pscale = 100)
#' forest(m4)
#' forest(m5)
#' }
#'
#' # Do not back transform results, e.g. print logit transformed
#' # proportions if sm = "PLOGIT" and store old settings
#' #
#' oldset <- settings.meta(backtransf = FALSE)
#' #
#' m6 <- metaprop(4:1, c(10, 20, 30, 40), method = "Inverse")
#' m7 <- update(m6, sm = "PAS")
#' m8 <- update(m6, sm = "PRAW")
#' m9 <- update(m6, sm = "PLN")
#' m10 <- update(m6, sm = "PFT")
#' #
#' forest(m6)
#' \dontrun{
#' forest(m7)
#' forest(m8)
#' forest(m8, pscale = 100)
#' forest(m9)
#' forest(m10)
#' }
#'
#' # Use old settings
#' #
#' settings.meta(oldset)
#'
#' # Examples with zero events
#' #
#' m1 <- metaprop(c(0, 0, 10, 10), rep(100, 4), method = "Inverse")
#' m2 <- metaprop(c(0, 0, 10, 10), rep(100, 4), incr = 0.1, method = "Inverse")
#' #
#' m1
#' m2
#' #
#' \dontrun{
#' forest(m1)
#' forest(m2)
#' }
#'
#' # Example from Miller (1978):
#' #
#' death <- c(3, 6, 10, 1)
#' animals <- c(11, 17, 21, 6)
#' #
#' m3 <- metaprop(death, animals, sm = "PFT")
#' forest(m3)
#'
#' # Data examples from Newcombe (1998)
#' # - apply various methods to estimate confidence intervals for
#' # individual studies
#' #
#' event <- c(81, 15, 0, 1)
#' n <- c(263, 148, 20, 29)
#' #
#' m1 <- metaprop(event, n, method.ci = "SA", method = "Inverse")
#' m2 <- update(m1, method.ci = "SACC")
#' m3 <- update(m1, method.ci = "WS")
#' m4 <- update(m1, method.ci = "WSCC")
#' m5 <- update(m1, method.ci = "CP")
#' #
#' lower <- round(rbind(NA, m1$lower, m2$lower, NA, m3$lower,
#' m4$lower, NA, m5$lower), 4)
#' upper <- round(rbind(NA, m1$upper, m2$upper, NA, m3$upper,
#' m4$upper, NA, m5$upper), 4)
#' #
#' tab1 <- data.frame(
#' scen1 = meta:::formatCI(lower[, 1], upper[, 1]),
#' scen2 = meta:::formatCI(lower[, 2], upper[, 2]),
#' scen3 = meta:::formatCI(lower[, 3], upper[, 3]),
#' scen4 = meta:::formatCI(lower[, 4], upper[, 4])
#' )
#' names(tab1) <- c("r=81, n=263", "r=15, n=148",
#' "r=0, n=20", "r=1, n=29")
#' row.names(tab1) <- c("Simple", "- SA", "- SACC",
#' "Score", "- WS", "- WSCC", "Binomial", "- CP")
#' tab1[is.na(tab1)] <- ""
#' # Newcombe (1998), Table I, methods 1-5:
#' tab1
#'
#' # Same confidence interval, i.e. unaffected by choice of summary
#' # measure
#' #
#' print(metaprop(event, n, method.ci = "WS", method = "Inverse"), ma = FALSE)
#' print(metaprop(event, n, sm = "PLN", method.ci = "WS"), ma = FALSE)
#' print(metaprop(event, n, sm = "PFT", method.ci = "WS"), ma = FALSE)
#' print(metaprop(event, n, sm = "PAS", method.ci = "WS"), ma = FALSE)
#' print(metaprop(event, n, sm = "PRAW", method.ci = "WS"), ma = FALSE)
#'
#' # Different confidence intervals as argument sm = "NAsm"
#' #
#' print(metaprop(event, n, method.ci = "NAsm", method = "Inverse"), ma = FALSE)
#' print(metaprop(event, n, sm = "PLN", method.ci = "NAsm"), ma = FALSE)
#' print(metaprop(event, n, sm = "PFT", method.ci = "NAsm"), ma = FALSE)
#' print(metaprop(event, n, sm = "PAS", method.ci = "NAsm"), ma = FALSE)
#' print(metaprop(event, n, sm = "PRAW", method.ci = "NAsm"), ma = FALSE)
#'
#' # Different confidence intervals as argument backtransf = FALSE.
#' # Accordingly, method.ci = "NAsm" used internally.
#' #
#' print(metaprop(event, n, method.ci = "WS", method = "Inverse"),
#' ma = FALSE, backtransf = FALSE)
#' print(metaprop(event, n, sm = "PLN", method.ci = "WS"),
#' ma = FALSE, backtransf = FALSE)
#' print(metaprop(event, n, sm = "PFT", method.ci = "WS"),
#' ma = FALSE, backtransf = FALSE)
#' print(metaprop(event, n, sm = "PAS", method.ci = "WS"),
#' ma = FALSE, backtransf = FALSE)
#' print(metaprop(event, n, sm = "PRAW", method.ci = "WS"),
#' ma = FALSE, backtransf = FALSE)
#'
#' # Same results (printed on original and log scale, respectively)
#' #
#' print(metaprop(event, n, sm = "PLN", method.ci = "NAsm"), ma = FALSE)
#' print(metaprop(event, n, sm = "PLN"), ma = FALSE, backtransf = FALSE)
#' # Results for first study (on log scale)
#' round(log(c(0.3079848, 0.2569522, 0.3691529)), 4)
#'
#' # Print results as events per 1000 observations
#' #
#' print(metaprop(6:8, c(100, 1200, 1000), method = "Inverse"),
#' pscale = 1000, digits = 1)
#'
#' @export metaprop
metaprop <- function(event, n, studlab,
##
data = NULL, subset = NULL, exclude = NULL,
cluster = NULL,
method,
##
sm = gs("smprop"),
##
incr = gs("incr"), method.incr = gs("method.incr"),
##
method.ci = gs("method.ci.prop"), level = gs("level"),
##
common = gs("common"),
random = gs("random") | !is.null(tau.preset),
overall = common | random,
overall.hetstat = common | random,
prediction = gs("prediction") | !missing(method.predict),
##
method.tau =
ifelse(!is.na(charmatch(tolower(method), "glmm",
nomatch = NA)),
"ML", gs("method.tau")),
method.tau.ci = gs("method.tau.ci"),
tau.preset = NULL, TE.tau = NULL,
tau.common = gs("tau.common"),
##
level.ma = gs("level.ma"),
method.random.ci = gs("method.random.ci"),
adhoc.hakn.ci = gs("adhoc.hakn.ci"),
##
level.predict = gs("level.predict"),
method.predict = gs("method.predict"),
adhoc.hakn.pi = gs("adhoc.hakn.pi"),
seed.predict = NULL,
##
null.effect = NA,
##
method.bias = gs("method.bias"),
##
backtransf = gs("backtransf"),
pscale = 1,
##
text.common = gs("text.common"),
text.random = gs("text.random"),
text.predict = gs("text.predict"),
text.w.common = gs("text.w.common"),
text.w.random = gs("text.w.random"),
##
title = gs("title"), complab = gs("complab"),
outclab = "",
##
subgroup, subgroup.name = NULL,
print.subgroup.name = gs("print.subgroup.name"),
sep.subgroup = gs("sep.subgroup"),
test.subgroup = gs("test.subgroup"),
prediction.subgroup = gs("prediction.subgroup"),
seed.predict.subgroup = NULL,
##
byvar, hakn, adhoc.hakn,
##
keepdata = gs("keepdata"),
warn = gs("warn"), warn.deprecated = gs("warn.deprecated"),
##
control = NULL,
...
) {
##
##
## (1) Check and set arguments
##
##
missing.method <- missing(method)
if (missing.method)
method <- if (sm == "PLOGIT") "GLMM" else "Inverse"
else
method <- setchar(method, gs("meth4prop"))
is.glmm <- method == "GLMM"
##
chknull(sm)
sm <- setchar(sm, gs("sm4prop"))
##
missing.method.incr <- missing(method.incr)
method.incr <- setchar(method.incr, gs("meth4incr"))
##
chklevel(level)
##
missing.method.tau <- missing(method.tau)
if (missing.method.tau)
method.tau <- if (method == "GLMM") "ML" else gs("method.tau")
method.tau <- setchar(method.tau, gs("meth4tau"))
##
missing.tau.common <- missing(tau.common)
tau.common <- replaceNULL(tau.common, FALSE)
chklogical(tau.common)
##
chklogical(prediction)
chklevel(level.predict)
##
missing.method.predict <- missing(method.predict)
##
method.tau <-
setmethodtau(method.tau, missing.method.tau,
method.predict, missing.method.predict)
method.predict <-
setmethodpredict(method.predict, missing.method.predict,
method.tau, missing.method.tau)
##
if (any(method.predict == "NNF"))
is_installed_package("pimeta", argument = "method.predict", value = "NNF")
##
missing.adhoc.hakn.pi <- missing(adhoc.hakn.pi)
adhoc.hakn.pi <- setchar(adhoc.hakn.pi, gs("adhoc4hakn.pi"))
##
if (!anyNA(null.effect) | length(null.effect) != 1)
chknumeric(null.effect, min = 0, max = 1, length = 1)
##
method.bias <- setmethodbias(method.bias)
##
chklogical(backtransf)
##
chknumeric(pscale, length = 1)
if (!backtransf & pscale != 1 & !is_untransformed(sm)) {
warning("Argument 'pscale' set to 1 as argument 'backtransf' is FALSE.",
call. = FALSE)
pscale <- 1
}
##
if (!is.null(text.common))
chkchar(text.common, length = 1)
if (!is.null(text.random))
chkchar(text.random)
if (!is.null(text.predict))
chkchar(text.predict)
if (!is.null(text.w.common))
chkchar(text.w.common, length = 1)
if (!is.null(text.w.random))
chkchar(text.w.random, length = 1)
##
chklogical(keepdata)
##
## Additional arguments / checks for metainc objects
##
fun <- "metaprop"
##
method.ci <- setchar(method.ci, gs("ci4prop"))
chklogical(warn)
##
## Check for deprecated arguments in '...'
##
args <- list(...)
chklogical(warn.deprecated)
##
level.ma <- deprecated(level.ma, missing(level.ma), args, "level.comb",
warn.deprecated)
chklevel(level.ma)
##
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)
##
method.random.ci <-
deprecated2(method.random.ci, missing(method.random.ci),
hakn, missing(hakn),
warn.deprecated)
if (is.logical(method.random.ci))
if (method.random.ci)
method.random.ci <- "HK"
else
method.random.ci <- "classic"
method.random.ci <- setchar(method.random.ci, gs("meth4random.ci"))
##
missing.adhoc.hakn.ci <- missing(adhoc.hakn.ci)
adhoc.hakn.ci <-
deprecated2(adhoc.hakn.ci, missing(adhoc.hakn.ci),
adhoc.hakn, missing(adhoc.hakn), warn.deprecated)
adhoc.hakn.ci <- setchar(adhoc.hakn.ci, gs("adhoc4hakn.ci"))
##
missing.subgroup.name <- missing(subgroup.name)
subgroup.name <-
deprecated(subgroup.name, missing.subgroup.name, args, "bylab",
warn.deprecated)
##
print.subgroup.name <-
deprecated(print.subgroup.name, missing(print.subgroup.name),
args, "print.byvar", warn.deprecated)
print.subgroup.name <-
replaceNULL(print.subgroup.name, gs("print.subgroup.name"))
chklogical(print.subgroup.name)
##
sep.subgroup <-
deprecated(sep.subgroup, missing(sep.subgroup), args, "byseparator",
warn.deprecated)
if (!is.null(sep.subgroup))
chkchar(sep.subgroup, length = 1)
##
addincr <-
deprecated(method.incr, missing.method.incr, args, "addincr",
warn.deprecated)
allincr <-
deprecated(method.incr, missing.method.incr, args, "allincr",
warn.deprecated)
if (missing.method.incr) {
method.incr <- gs("method.incr")
##
if (is.logical(addincr) && addincr)
method.incr <- "all"
else if (is.logical(allincr) && allincr)
method.incr <- "if0all"
}
##
addincr <- allincr <- FALSE
if (method.incr == "all")
addincr <- TRUE
else if (method.incr == "if0all")
allincr <- TRUE
##
## Some more checks
##
chklogical(overall)
chklogical(overall.hetstat)
##
##
## (2) Read data
##
##
nulldata <- is.null(data)
sfsp <- sys.frame(sys.parent())
mc <- match.call()
##
if (nulldata)
data <- sfsp
##
## Catch 'event' and 'n' from data:
##
event <- catch("event", mc, data, sfsp)
chknull(event)
k.All <- length(event)
##
n <- catch("n", mc, data, sfsp)
chknull(n)
##
## Catch 'incr' from data:
##
if (!missing(incr))
incr <- catch("incr", mc, data, sfsp)
chknumeric(incr, min = 0)
##
## Catch 'studlab', 'subgroup', 'subset', 'exclude' and 'cluster'
## from data:
##
studlab <- catch("studlab", mc, data, sfsp)
studlab <- setstudlab(studlab, k.All)
##
missing.subgroup <- missing(subgroup)
subgroup <- catch("subgroup", mc, data, sfsp)
missing.byvar <- missing(byvar)
byvar <- catch("byvar", mc, data, sfsp)
##
subgroup <- deprecated2(subgroup, missing.subgroup, byvar, missing.byvar,
warn.deprecated)
by <- !is.null(subgroup)
##
subset <- catch("subset", mc, data, sfsp)
missing.subset <- is.null(subset)
##
exclude <- catch("exclude", mc, data, sfsp)
missing.exclude <- is.null(exclude)
##
cluster <- catch("cluster", mc, data, sfsp)
with.cluster <- !is.null(cluster)
##
##
## (3) Check length of essential variables
##
##
chklength(n, k.All, fun)
chklength(studlab, k.All, fun)
if (with.cluster)
chklength(cluster, k.All, fun)
##
if (length(incr) > 1)
chklength(incr, k.All, fun)
##
if (by) {
chklength(subgroup, k.All, fun)
chklogical(test.subgroup)
chklogical(prediction.subgroup)
}
##
## Additional checks
##
if (!by & tau.common) {
warning("Value for argument 'tau.common' set to FALSE as ",
"argument 'subgroup' is missing.",
call. = FALSE)
tau.common <- FALSE
}
if (by & !tau.common & !is.null(tau.preset)) {
warning("Argument 'tau.common' set to TRUE as ",
"argument tau.preset is not NULL.",
call. = FALSE)
tau.common <- TRUE
}
##
##
## (4) Subset, exclude studies, and subgroups
##
##
if (!missing.subset)
if ((is.logical(subset) & (sum(subset) > k.All)) ||
(length(subset) > k.All))
stop("Length of subset is larger than number of studies.")
##
if (!missing.exclude) {
if ((is.logical(exclude) & (sum(exclude) > k.All)) ||
(length(exclude) > k.All))
stop("Length of argument 'exclude' is larger than number of studies.")
##
exclude2 <- rep(FALSE, k.All)
exclude2[exclude] <- TRUE
exclude <- exclude2
}
else
exclude <- rep(FALSE, k.All)
##
##
## (5) Store complete dataset in list object data
## (if argument keepdata is TRUE)
##
##
if (keepdata) {
if (nulldata)
data <- data.frame(.event = event)
else
data$.event <- event
##
data$.n <- n
data$.studlab <- studlab
##
data$.incr <- incr
##
if (by)
data$.subgroup <- subgroup
##
if (!missing.subset) {
if (length(subset) == dim(data)[1])
data$.subset <- subset
else {
data$.subset <- FALSE
data$.subset[subset] <- TRUE
}
}
##
if (!missing.exclude)
data$.exclude <- exclude
##
if (with.cluster)
data$.id <- data$.cluster <- cluster
}
##
##
## (6) Use subset for analysis
##
##
if (!missing.subset) {
event <- event[subset]
n <- n[subset]
studlab <- studlab[subset]
##
cluster <- cluster[subset]
exclude <- exclude[subset]
##
if (length(incr) > 1)
incr <- incr[subset]
##
if (by)
subgroup <- subgroup[subset]
}
##
## Determine total number of studies
##
k.all <- length(event)
##
if (k.all == 0)
stop("No studies to combine in meta-analysis.")
##
## No meta-analysis for a single study
##
if (k.all == 1) {
common <- FALSE
random <- FALSE
prediction <- FALSE
overall <- FALSE
overall.hetstat <- FALSE
}
##
## Check variable values
##
chknumeric(event, 0)
chknumeric(n, 0, zero = TRUE)
##
if (any(event > n, na.rm = TRUE))
stop("Number of events must not be larger than number of observations")
##
## Recode integer as numeric:
##
event <- int2num(event)
n <- int2num(n)
##
## Check for whole numbers
##
if (method.ci != "NAsm") {
if (any(!is_wholenumber(event), na.rm = TRUE)) {
warning("Normal approximation confidence interval ",
"(argument method.ci = \"NAsm\") used as\n",
"at least one number of events contains a non-integer value.",
call. = FALSE)
method.ci <- "NAsm"
}
else if (any(!is_wholenumber(n), na.rm = TRUE)) {
warning("Normal approximation confidence interval ",
"(argument method.ci = \"NAsm\") used as\n",
"at least one sample size contains a non-integer value.",
call. = FALSE)
method.ci <- "NAsm"
}
}
##
if (by) {
chkmiss(subgroup)
##
if (missing.subgroup.name & is.null(subgroup.name)) {
if (!missing.subgroup)
subgroup.name <- byvarname("subgroup", mc)
else if (!missing.byvar)
subgroup.name <- byvarname("byvar", mc)
}
}
##
if (!is.null(subgroup.name))
chkchar(subgroup.name, length = 1)
##
##
## (7) Calculate results for individual studies
##
##
sel <- switch(sm,
PLOGIT = event == 0 | (n - event) == 0,
PAS = rep(FALSE, length(event)),
PFT = rep(FALSE, length(event)),
PLN = event == 0 | (n - event) == 0,
PRAW = event == 0 | (n - event) == 0)
##
sparse <- any(sel, na.rm = TRUE)
##
## No need to add anything to cell counts for arcsine transformation
##
if (addincr)
incr.event <- if (length(incr) == 1) rep(incr, k.all) else incr
else
if (sparse)
if (allincr)
incr.event <- if (length(incr) == 1) rep(incr, k.all) else incr
else
incr.event <- incr * sel
else
incr.event <- rep(0, k.all)
##
if (sm == "PLOGIT") {
TE <- log((event + incr.event) / (n - event + incr.event))
seTE <- sqrt(1 / (event + incr.event) +
1 / ((n - event + incr.event)))
transf.null.effect <- log(null.effect / (1 - null.effect))
}
else if (sm == "PAS") {
TE <- asin(sqrt(event / n))
seTE <- sqrt(1 / (4 * n))
transf.null.effect <- asin(sqrt(null.effect))
}
else if (sm == "PFT") {
TE <- 0.5 * (asin(sqrt(event / (n + 1))) + asin(sqrt((event + 1) / (n + 1))))
seTE <- sqrt(1 / (4 * n + 2))
transf.null.effect <- asin(sqrt(null.effect))
}
else if (sm == "PLN") {
TE <- log((event + incr.event) / (n + incr.event))
## Hartung, Knapp (2001), p. 3880, formula (18):
seTE <- ifelse(event == n,
sqrt(1 / event - 1 / (n + incr.event)),
sqrt(1 / (event + incr.event) - 1 / (n + incr.event))
)
transf.null.effect <- log(null.effect)
}
else if (sm == "PRAW") {
TE <- event / n
seTE <- sqrt((event + incr.event) * (n - event + incr.event) /
(n + 2 * incr.event)^3)
transf.null.effect <- null.effect
}
##
## Calculate confidence intervals
##
if (method.ci == "CP")
ci.study <- ciClopperPearson(event, n, level, null.effect)
else if (method.ci == "WS")
ci.study <- ciWilsonScore(event, n, level)
else if (method.ci == "WSCC")
ci.study <- ciWilsonScore(event, n, level, correct = TRUE)
else if (method.ci == "AC")
ci.study <- ciAgrestiCoull(event, n, level)
else if (method.ci == "SA")
ci.study <- ciSimpleAsymptotic(event, n, level)
else if (method.ci == "SACC")
ci.study <- ciSimpleAsymptotic(event, n, level, correct = TRUE)
else if (method.ci == "NAsm")
ci.study <- ci(TE, seTE, level, null.effect = null.effect)
##
lower.study <- ci.study$lower
upper.study <- ci.study$upper
##
if (method.ci == "NAsm") {
if (sm == "PLOGIT") {
lower.study <- logit2p(lower.study)
upper.study <- logit2p(upper.study)
}
##
else if (sm == "PAS") {
lower.study <- asin2p(lower.study, value = "lower")
upper.study <- asin2p(upper.study, value = "upper")
}
##
else if (sm == "PFT") {
lower.study <- asin2p(lower.study, n, value = "lower")
upper.study <- asin2p(upper.study, n, value = "upper")
}
##
else if (sm == "PLN") {
lower.study <- exp(lower.study)
upper.study <- exp(upper.study)
}
##
lower.study[lower.study < 0] <- 0
upper.study[upper.study > 1] <- 1
}
##
##
## (8) Additional checks for three-level model
##
##
three.level <- FALSE
sel.ni <- !is.infinite(TE) & !is.infinite(seTE)
##
## Only conduct three-level meta-analysis if variable 'cluster'
## contains duplicate values after removing inestimable study
## results standard errors
##
if (with.cluster &&
length(unique(cluster[sel.ni])) != length(cluster[sel.ni]))
three.level <- TRUE
##
if (three.level) {
chkmlm(method.tau, missing.method.tau, method.predict,
by, tau.common, missing.tau.common,
method, missing.method)
##
common <- FALSE
method <- "Inverse"
is.glmm <- FALSE
##
if (!(method.tau %in% c("REML", "ML")))
method.tau <- "REML"
}
##
##
## (9) Additional checks for GLMM
##
##
if (is.glmm) {
chkglmm(sm, method.tau, method.random.ci, method.predict,
adhoc.hakn.ci, adhoc.hakn.pi,
"PLOGIT")
##
if (!is.null(TE.tau)) {
if (warn)
warning("Argument 'TE.tau' not considered for GLMM.",
call. = FALSE)
TE.tau <- NULL
}
##
if (!is.null(tau.preset)) {
if (warn)
warning("Argument 'tau.preset' not considered for GLMM.",
call. = FALSE)
tau.preset <- NULL
}
}
##
##
## (10) Do meta-analysis
##
##
k <- sum(!is.na(event[!exclude]) & !is.na(n[!exclude]))
##
for (i in seq_along(method.random.ci))
if (k == 1 & method.random.ci[i] == "HK")
method.random.ci[i] <- "classic"
##
m <- metagen(TE, seTE, studlab,
exclude = if (missing.exclude) NULL else exclude,
cluster = cluster,
##
sm = sm,
level = level,
##
common = common,
random = random,
overall = overall,
overall.hetstat = overall.hetstat,
prediction = prediction,
##
method.tau = method.tau, method.tau.ci = method.tau.ci,
tau.preset = tau.preset,
TE.tau = TE.tau,
tau.common = FALSE,
##
level.ma = level.ma,
method.random.ci = method.random.ci,
adhoc.hakn.ci = adhoc.hakn.ci,
##
level.predict = level.predict,
method.predict = method.predict,
adhoc.hakn.pi = adhoc.hakn.pi,
seed.predict = seed.predict,
##
null.effect = transf.null.effect,
##
method.bias = method.bias,
##
backtransf = backtransf,
##
text.common = text.common, text.random = text.random,
text.predict = text.predict,
text.w.common = text.w.common, text.w.random = text.w.random,
##
title = title, complab = complab, outclab = outclab,
##
keepdata = FALSE,
warn = warn,
##
control = control)
##
if (by & tau.common & !is.glmm) {
## Estimate common tau-squared across subgroups
hcc <- hetcalc(TE, seTE, method.tau, "",
TE.tau, level.ma, subgroup, control)
}
##
##
## (9) Generate R object
##
##
res <- list(event = event, n = n,
incr = if (length(unique(incr)) == 1) unique(incr) else incr,
method.incr = method.incr,
sparse = sparse,
method.ci = method.ci,
incr.event = incr.event)
##
## Add meta-analysis results
## (after removing unneeded list elements)
##
m$n.e <- NULL
m$n.c <- NULL
m$pscale <- NULL
m$irscale <- NULL
m$irunit <- NULL
m$method.ci <- NULL
m$method.mean <- NULL
m$approx.TE <- NULL
m$approx.seTE <- NULL
##
m$label.e <- ""
m$label.c <- ""
m$label.left <- ""
m$label.right <- ""
##
if (method.ci == "CP") {
m$statistic <- rep(NA, length(m$statistic))
m$pval <- ci.study$p
}
else if (method.ci != "NAsm") {
m$statistic <- rep(NA, length(m$statistic))
m$pval <- rep(NA, length(m$pval))
}
##
if (is.glmm | three.level) {
m$seTE.hakn.ci <- m$seTE.hakn.adhoc.ci <-
m$seTE.hakn.pi <- m$seTE.hakn.adhoc.pi <-
m$seTE.kero <- NA
##
m$text.random <- gsub("(HK)", "(T)", m$text.random, fixed = TRUE)
}
##
res <- c(res, m)
res$null.effect <- null.effect
##
## Run GLMM and add data
##
if (is.glmm & k > 0) {
res$method <- "GLMM"
res$method.random <- "GLMM"
##
list.prop <- list(xi = event[!exclude], ni = n[!exclude], measure = "PLO")
##
use.random <-
sum(!exclude) > 1 &
sum(event[!exclude], na.rm = TRUE) > 0 &
any(event[!exclude] != n[!exclude])
##
res.glmm <-
runGLMM(list.prop,
method.tau = method.tau,
method.random.ci = method.random.ci,
level = level.ma,
control = control, use.random = use.random)
##
res <- addGLMM(res, res.glmm)
##
if (by) {
n.subgroups <- length(unique(subgroup[!exclude]))
if (n.subgroups > 1)
subgroup.glmm <-
factor(subgroup[!exclude], bylevs(subgroup[!exclude]))
##
hcc <-
hccGLMM(
res,
runGLMM(list.prop,
method.tau = method.tau,
method.random.ci = method.random.ci,
level = level.ma,
data =
if (n.subgroups > 1)
list(data = data.frame(subgroup.glmm))
else
NULL,
mods =
if (n.subgroups > 1)
as.call(~ subgroup.glmm)
else
NULL,
control = control, use.random = use.random)$glmm.random[[1]]
)
}
}
##
res$lower <- lower.study
res$upper <- upper.study
##
res$pscale <- pscale
##
res$call <- match.call()
res$allincr <- allincr
res$addincr <- addincr
##
if (keepdata) {
res$data <- data
if (!missing.subset)
res$subset <- subset
}
##
class(res) <- c(fun, "meta")
##
## Add results from subgroup analysis
##
if (by) {
res$subgroup <- subgroup
res$subgroup.name <- subgroup.name
res$print.subgroup.name <- print.subgroup.name
res$sep.subgroup <- sep.subgroup
res$test.subgroup <- test.subgroup
res$prediction.subgroup <- prediction.subgroup
res$tau.common <- tau.common
##
if (!tau.common) {
res <- c(res, subgroup(res, seed = seed.predict.subgroup))
if (res$three.level)
res <- setNA3(res)
}
else if (!is.null(tau.preset))
res <-
c(res, subgroup(res, tau.preset, seed = seed.predict.subgroup))
else {
if (is.glmm)
res <- c(res,
subgroup(res, NULL,
factor(res$subgroup, bylevs(res$subgroup)), ...))
else if (res$three.level)
res <- c(res,
subgroup(res, NULL,
factor(res$subgroup, bylevs(res$subgroup))))
else
res <-
c(res, subgroup(res, hcc$tau.resid, seed = seed.predict.subgroup))
}
##
if (tau.common && is.null(tau.preset))
res <- addHet(res, hcc, !is.glmm)
##
res$n.e.w <- NULL
res$n.c.w <- NULL
res$event.e.w <- NULL
res$event.c.w <- NULL
##
res$time.e.w <- NULL
res$time.c.w <- NULL
res$t.harmonic.mean.w <- NULL
##
res <- setNAwithin(res, res$three.level | is.glmm)
}
##
## Backward compatibility
##
res <- backward(res)
##
class(res) <- c(fun, "meta")
res
}
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Defines functions metaprop
Documented in metaprop
#' Meta-analysis of single proportions
#'
#' @description
#' Calculation of an overall proportion from studies reporting a
#' single proportion. Inverse variance method and generalised linear
#' mixed model (GLMM) are available for pooling. For GLMMs, the
#' \code{\link[metafor]{rma.glmm}} function from R package
#' \bold{metafor} (Viechtbauer 2010) is called internally.
#'
#' @param event Number of events.
#' @param n Number of observations.
#' @param studlab An optional vector with study labels.
#' @param data An optional data frame containing the study
#' information, i.e., event and n.
#' @param subset An optional vector specifying a subset of studies to
#' be used.
#' @param exclude An optional vector specifying studies to exclude
#' from meta-analysis, however, to include in printouts and forest
#' plots.
#' @param cluster An optional vector specifying which estimates come
#' from the same cluster resulting in the use of a three-level
#' meta-analysis model.
#' @param method A character string indicating which method is to be
#' used for pooling of studies. One of \code{"Inverse"} and
#' \code{"GLMM"}, can be abbreviated.
#' @param sm A character string indicating which summary measure
#' (\code{"PLOGIT"}, \code{"PAS"}, \code{"PFT"}, \code{"PLN"}, or
#' \code{"PRAW"}) is to be used for pooling of studies, see Details.
#' @param incr A numeric which is added to event number and sample
#' size of studies with zero or all events, i.e., studies with an
#' event probability of either 0 or 1.
#' @param method.incr A character string indicating which continuity
#' correction method should be used (\code{"only0"},
#' \code{"if0all"}, or \code{"all"}), see Details.
#' @param method.ci A character string indicating which method is used
#' to calculate confidence intervals for individual studies, see
#' Details.
#' @param level The level used to calculate confidence intervals for
#' individual studies.
#' @param common A logical indicating whether a common effect
#' meta-analysis should be conducted.
#' @param random A logical indicating whether a random effects
#' meta-analysis should be conducted.
#' @param overall A logical indicating whether overall summaries
#' should be reported. This argument is useful in a meta-analysis
#' with subgroups if overall results should not be reported.
#' @param overall.hetstat A logical value indicating whether to print
#' heterogeneity measures for overall treatment comparisons. This
#' argument is useful in a meta-analysis with subgroups if
#' heterogeneity statistics should only be printed on subgroup
#' level.
#' @param prediction A logical indicating whether a prediction
#' interval should be printed.
#' @param method.tau A character string indicating which method is
#' used to estimate the between-study variance \eqn{\tau^2} and its
#' square root \eqn{\tau} (see \code{\link{meta-package}}).
#' @param method.tau.ci A character string indicating which method is
#' used to estimate the confidence interval of \eqn{\tau^2} and
#' \eqn{\tau} (see \code{\link{meta-package}}).
#' @param tau.preset Prespecified value for the square root of the
#' between-study variance \eqn{\tau^2}.
#' @param TE.tau Overall treatment effect used to estimate the
#' between-study variance tau-squared.
#' @param tau.common A logical indicating whether tau-squared should
#' be the same across subgroups.
#' @param level.ma The level used to calculate confidence intervals
#' for meta-analysis estimates.
#' @param method.random.ci A character string indicating which method
#' is used to calculate confidence interval and test statistic for
#' random effects estimate (see \code{\link{meta-package}}).
#' @param adhoc.hakn.ci A character string indicating whether an
#' \emph{ad hoc} variance correction should be applied in the case
#' of an arbitrarily small Hartung-Knapp variance estimate (see
#' \code{\link{meta-package}}).
#' @param level.predict The level used to calculate prediction
#' interval for a new study.
#' @param method.predict A character string indicating which method is
#' used to calculate a prediction interval (see
#' \code{\link{meta-package}}).
#' @param adhoc.hakn.pi A character string indicating whether an
#' \emph{ad hoc} variance correction should be applied for
#' prediction interval (see \code{\link{meta-package}}).
#' @param seed.predict A numeric value used as seed to calculate
#' bootstrap prediction interval (see \code{\link{meta-package}}).
#' @param null.effect A numeric value specifying the effect under the
#' null hypothesis.
#' @param method.bias A character string indicating which test is to
#' be used. Either \code{"Begg"}, \code{"Egger"}, or
#' \code{"Thompson"}, can be abbreviated. See function
#' \code{\link{metabias}}.
#' @param backtransf A logical indicating whether results for
#' transformed proportions (argument \code{sm != "PRAW"}) should be
#' back transformed in printouts and plots. If TRUE (default),
#' results will be presented as proportions; otherwise transformed
#' proportions will be shown. See Details for presentation of
#' confidence intervals.
#' @param pscale A numeric defining a scaling factor for printing of
#' single event probabilities.
#' @param text.common A character string used in printouts and forest
#' plot to label the pooled common effect estimate.
#' @param text.random A character string used in printouts and forest
#' plot to label the pooled random effects estimate.
#' @param text.predict A character string used in printouts and forest
#' plot to label the prediction interval.
#' @param text.w.common A character string used to label weights of
#' common effect model.
#' @param text.w.random A character string used to label weights of
#' random effects model.
#' @param title Title of meta-analysis / systematic review.
#' @param complab Comparison label.
#' @param outclab Outcome label.
#' @param subgroup An optional vector to conduct a meta-analysis with
#' subgroups.
#' @param subgroup.name A character string with a name for the
#' subgroup variable.
#' @param print.subgroup.name A logical indicating whether the name of
#' the subgroup variable should be printed in front of the group
#' labels.
#' @param sep.subgroup A character string defining the separator
#' between name of subgroup variable and subgroup label.
#' @param test.subgroup A logical value indicating whether to print
#' results of test for subgroup differences.
#' @param prediction.subgroup A logical indicating whether prediction
#' intervals should be printed for subgroups.
#' @param seed.predict.subgroup A numeric vector providing seeds to
#' calculate bootstrap prediction intervals within subgroups. Must
#' be of same length as the number of subgroups.
#' @param byvar Deprecated argument (replaced by 'subgroup').
#' @param hakn Deprecated argument (replaced by 'method.random.ci').
#' @param adhoc.hakn Deprecated argument (replaced by 'adhoc.hakn.ci').
#' @param keepdata A logical indicating whether original data (set)
#' should be kept in meta object.
#' @param warn A logical indicating whether the addition of
#' \code{incr} to studies with zero or all events should result in a
#' warning.
#' @param warn.deprecated A logical indicating whether warnings should
#' be printed if deprecated arguments are used.
#' @param control An optional list to control the iterative process to
#' estimate the between-study variance \eqn{\tau^2}. This argument
#' is passed on to \code{\link[metafor]{rma.uni}} or
#' \code{\link[metafor]{rma.glmm}}, respectively.
#' @param \dots Additional arguments passed on to
#' \code{\link[metafor]{rma.glmm}} function and to catch deprecated
#' arguments.
#'
#' @details
#' This function provides methods for common effect and random effects
#' meta-analysis of single proportions to calculate an overall
#' proportion. Note, you should use R function \code{\link{metabin}}
#' to compare proportions of pairwise comparisons instead of using
#' \code{metaprop} for each treatment arm separately which will break
#' randomisation in randomised controlled trials.
#'
#' The following transformations of proportions are
#' implemented to calculate an overall proportion:
#'
#' \itemize{
#' \item Logit transformation (\code{sm = "PLOGIT"}, default)
#' \item Arcsine transformation (\code{sm = "PAS"})
#' \item Freeman-Tukey Double arcsine transformation (\code{sm = "PFT"})
#' \item Log transformation (\code{sm = "PLN"})
#' \item No transformation (\code{sm = "PRAW"})
#' }
#'
#' List elements \code{TE}, \code{TE.common}, \code{TE.random}, etc.,
#' contain the transformed proportions. In printouts and plots these
#' values are back transformed if argument \code{backtransf = TRUE}
#' (default).
#'
#' A generalised linear mixed model (GLMM) - more specific, a random
#' intercept logistic regression model - can be utilised for the
#' meta-analysis of proportions (Stijnen et al., 2010). This is the
#' default method for the logit transformation (argument \code{sm =
#' "PLOGIT"}). Internally, the \code{\link[metafor]{rma.glmm}}
#' function from R package \bold{metafor} is called to fit a GLMM.
#'
#' Classic meta-analysis (Borenstein et al., 2010) utilising the
#' (un)transformed proportions and corresponding standard errors in
#' the inverse variance method is conducted by calling the
#' \code{\link{metagen}} function internally. This is the only
#' available method for all transformations but the logit
#' transformation. The classic meta-analysis model with logit
#' transformed proportions is used by setting argument \code{method =
#' "Inverse"}.
#'
#' A three-level random effects meta-analysis model (Van den Noortgate
#' et al., 2013) is utilized if argument \code{cluster} is used and at
#' least one cluster provides more than one estimate. Internally,
#' \code{\link[metafor]{rma.mv}} is called to conduct the analysis and
#' \code{\link[metafor]{weights.rma.mv}} with argument \code{type =
#' "rowsum"} is used to calculate random effects weights.
#'
#' Default settings are utilised for several arguments (assignments
#' using \code{\link{gs}} function). These defaults can be changed for
#' the current R session using the \code{\link{settings.meta}}
#' function.
#'
#' Furthermore, R function \code{\link{update.meta}} can be used to
#' rerun a meta-analysis with different settings.
#'
#' \subsection{Choice of transformation / meta-analysis method}{
#'
#' Contradictory recommendations on the use of transformations of
#' proportions have been published in the literature. For example,
#' Barendregt et al. (2013) recommend the use of the Freeman-Tukey
#' double arcsine transformation instead of the logit transformation
#' whereas Warton & Hui (2011) strongly advise to use generalised
#' linear mixed models with the logit transformation instead of the
#' arcsine transformation.
#'
#' Schwarzer et al. (2019) describe seriously misleading results in a
#' meta-analysis with very different sample sizes due to problems with
#' the back-transformation of the Freeman-Tukey transformation which
#' requires a single sample size (Miller, 1978). Accordingly,
#' Schwarzer et al. (2019) also recommend to use GLMMs for the
#' meta-analysis of single proportions, however, admit that individual
#' study weights are not available with this method. Meta-analysts
#' which require individual study weights should consider the inverse
#' variance method with the arcsine or logit transformation.
#'
#' In order to prevent misleading conclusions for the Freeman-Tukey
#' double arcsine transformation, sensitivity analyses using other
#' transformations or using a range of sample sizes should be
#' conducted (Schwarzer et al., 2019).
#' }
#'
#' \subsection{Continuity correction}{
#'
#' Three approaches are available to apply a continuity correction:
#' \itemize{
#' \item Only studies with a zero cell count (\code{method.incr =
#' "only0"})
#' \item All studies if at least one study has a zero cell count
#' (\code{method.incr = "if0all"})
#' \item All studies irrespective of zero cell counts
#' (\code{method.incr = "all"})
#' }
#'
#' If the summary measure is equal to "PLOGIT", "PLN", or "PRAW", the
#' continuity correction is applied if a study has either zero or all
#' events, i.e., an event probability of either 0 or 1.
#'
#' By default, 0.5 is used as continuity correction (argument
#' \code{incr}). 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 GLMMs no
#' continuity correction is used.
#' }
#'
#' \subsection{Confidence intervals for individual studies}{
#'
#' Various methods are available to calculate confidence intervals for
#' individual study results (see Agresti & Coull 1998 and Newcombe
#' 1988):
#' \itemize{
#' \item Clopper-Pearson interval also called 'exact' binomial
#' interval (\code{method.ci = "CP"}, default)
#' \item Wilson Score interval (\code{method.ci = "WS"})
#' \item Wilson Score interval with continuity correction
#' (\code{method.ci = "WSCC"})
#' \item Agresti-Coull interval (\code{method.ci = "AC"})
#' \item Simple approximation interval (\code{method.ci = "SA"})
#' \item Simple approximation interval with continuity correction
#' (\code{method.ci = "SACC"})
#' \item Normal approximation interval based on summary measure,
#' i.e. defined by argument \code{sm} (\code{method.ci = "NAsm"})
#' }
#'
#' Note, with exception of the normal approximation based on the
#' summary measure, i.e. \code{method.ci = "NAsm"}, the same
#' confidence interval is calculated for individual studies for any
#' summary measure (argument \code{sm}) as only number of events and
#' observations are used in the calculation disregarding the chosen
#' transformation.
#'
#' Results will be presented for transformed proportions if argument
#' \code{backtransf = FALSE}. In this case, argument \code{method.ci =
#' "NAsm"} is used, i.e. confidence intervals based on the normal
#' approximation based on the summary measure.
#' }
#'
#' \subsection{Subgroup analysis}{
#'
#' Argument \code{subgroup} can be used to conduct subgroup analysis for
#' a categorical covariate. The \code{\link{metareg}} function can be
#' used instead for more than one categorical covariate or continuous
#' covariates.
#' }
#'
#' \subsection{Specify the null hypothesis of test for an overall proportion}{
#'
#' Argument \code{null.effect} can be used to specify the proportion
#' used under the null hypothesis in a test for an overall effect.
#'
#' By default (\code{null.effect = NA}), no hypothesis test is
#' conducted as it is unclear which value is a sensible choice for the
#' data at hand. An overall proportion of 50\%, for example, could be
#' tested by setting argument \code{null.effect = 0.5}.
#'
#' Note, all tests for an overall effect are two-sided with the
#' alternative hypothesis that the effect is unequal to
#' \code{null.effect}.
#' }
#'
#' \subsection{Exclusion of studies from meta-analysis}{
#'
#' Arguments \code{subset} and \code{exclude} can be used to exclude
#' studies from the meta-analysis. Studies are removed completely from
#' the meta-analysis using argument \code{subset}, while excluded
#' studies are shown in printouts and forest plots using argument
#' \code{exclude} (see Examples in \code{\link{metagen}}).
#' Meta-analysis results are the same for both arguments.
#' }
#'
#' \subsection{Presentation of meta-analysis results}{
#'
#' Internally, both common effect and random effects models are
#' calculated regardless of values choosen for arguments
#' \code{common} and \code{random}. Accordingly, the estimate
#' for the random effects model can be extracted from component
#' \code{TE.random} of an object of class \code{"meta"} even if
#' argument \code{random = FALSE}. However, all functions in R
#' package \bold{meta} will adequately consider the values for
#' \code{common} and \code{random}. E.g. function
#' \code{\link{print.meta}} will not print results for the random
#' effects model if \code{random = FALSE}.
#'
#' Argument \code{pscale} can be used to rescale proportions, e.g.
#' \code{pscale = 1000} means that proportions are expressed as events
#' per 1000 observations. This is useful in situations with (very) low
#' event probabilities.
#'
#' A prediction interval will only be shown if \code{prediction =
#' TRUE}.
#' }
#'
#' @return
#' An object of class \code{c("metaprop", "meta")} with corresponding
#' generic functions (see \code{\link{meta-object}}).
#'
#' @author Guido Schwarzer \email{guido.schwarzer@@uniklinik-freiburg.de}
#'
#' @seealso \code{\link{meta-package}}, \code{\link{update.meta}},
#' \code{\link{metacont}}, \code{\link{metagen}},
#' \code{\link{print.meta}}, \code{\link{forest.meta}}
#'
#' @references
#' Agresti A & Coull BA (1998):
#' Approximate is better than "exact" for interval estimation of
#' binomial proportions.
#' \emph{The American Statistician},
#' \bold{52}, 119--26
#'
#' Barendregt JJ, Doi SA, Lee YY, Norman RE, Vos T (2013):
#' Meta-analysis of prevalence.
#' \emph{Journal of Epidemiology and Community Health},
#' \bold{67}, 974--8
#'
#' Borenstein M, Hedges LV, Higgins JP, Rothstein HR (2010):
#' A basic introduction to fixed-effect and random-effects models for
#' meta-analysis.
#' \emph{Research Synthesis Methods},
#' \bold{1}, 97--111
#'
#' Freeman MF & Tukey JW (1950):
#' Transformations related to the angular and the square root.
#' \emph{Annals of Mathematical Statistics},
#' \bold{21}, 607--11
#'
#' Miller JJ (1978):
#' The inverse of the Freeman-Tukey double arcsine transformation.
#' \emph{The American Statistician},
#' \bold{32}, 138
#'
#' Newcombe RG (1998):
#' Two-sided confidence intervals for the single proportion:
#' comparison of seven methods.
#' \emph{Statistics in Medicine},
#' \bold{17}, 857--72
#'
#' Pettigrew HM, Gart JJ, Thomas DG (1986):
#' The bias and higher cumulants of the logarithm of a binomial
#' variate.
#' \emph{Biometrika},
#' \bold{73}, 425--35
#'
#' Schwarzer G, Chemaitelly H, Abu-Raddad LJ, Rücker G (2019):
#' Seriously misleading results using inverse of Freeman-Tukey double
#' arcsine transformation in meta-analysis of single proportions.
#' \emph{Research Synthesis Methods},
#' \bold{10}, 476--83
#'
#' Stijnen T, Hamza TH, Ozdemir P (2010):
#' Random effects meta-analysis of event outcome in the framework of
#' the generalized linear mixed model with applications in sparse
#' data.
#' \emph{Statistics in Medicine},
#' \bold{29}, 3046--67
#'
#' Van den Noortgate W, López-López JA, Marín-Martínez F, Sánchez-Meca J (2013):
#' Three-level meta-analysis of dependent effect sizes.
#' \emph{Behavior Research Methods},
#' \bold{45}, 576--94
#'
#' Viechtbauer W (2010):
#' Conducting meta-analyses in R with the metafor package.
#' \emph{Journal of Statistical Software},
#' \bold{36}, 1--48
#'
#' Warton DI, Hui FKC (2011):
#' The arcsine is asinine: the analysis of proportions in ecology.
#' \emph{Ecology},
#' \bold{92}, 3--10
#'
#' @examples
#' # Meta-analysis using generalised linear mixed model
#' #
#' metaprop(4:1, 10 * 1:4)
#'
#' # Apply various classic meta-analysis methods to estimate
#' # proportions
#' #
#' m1 <- metaprop(4:1, 10 * 1:4, method = "Inverse")
#' m2 <- update(m1, sm = "PAS")
#' m3 <- update(m1, sm = "PRAW")
#' m4 <- update(m1, sm = "PLN")
#' m5 <- update(m1, sm = "PFT")
#' #
#' m1
#' m2
#' m3
#' m4
#' m5
#' #
#' forest(m1)
#' \dontrun{
#' forest(m2)
#' forest(m3)
#' forest(m3, pscale = 100)
#' forest(m4)
#' forest(m5)
#' }
#'
#' # Do not back transform results, e.g. print logit transformed
#' # proportions if sm = "PLOGIT" and store old settings
#' #
#' oldset <- settings.meta(backtransf = FALSE)
#' #
#' m6 <- metaprop(4:1, c(10, 20, 30, 40), method = "Inverse")
#' m7 <- update(m6, sm = "PAS")
#' m8 <- update(m6, sm = "PRAW")
#' m9 <- update(m6, sm = "PLN")
#' m10 <- update(m6, sm = "PFT")
#' #
#' forest(m6)
#' \dontrun{
#' forest(m7)
#' forest(m8)
#' forest(m8, pscale = 100)
#' forest(m9)
#' forest(m10)
#' }
#'
#' # Use old settings
#' #
#' settings.meta(oldset)
#'
#' # Examples with zero events
#' #
#' m1 <- metaprop(c(0, 0, 10, 10), rep(100, 4), method = "Inverse")
#' m2 <- metaprop(c(0, 0, 10, 10), rep(100, 4), incr = 0.1, method = "Inverse")
#' #
#' m1
#' m2
#' #
#' \dontrun{
#' forest(m1)
#' forest(m2)
#' }
#'
#' # Example from Miller (1978):
#' #
#' death <- c(3, 6, 10, 1)
#' animals <- c(11, 17, 21, 6)
#' #
#' m3 <- metaprop(death, animals, sm = "PFT")
#' forest(m3)
#'
#' # Data examples from Newcombe (1998)
#' # - apply various methods to estimate confidence intervals for
#' # individual studies
#' #
#' event <- c(81, 15, 0, 1)
#' n <- c(263, 148, 20, 29)
#' #
#' m1 <- metaprop(event, n, method.ci = "SA", method = "Inverse")
#' m2 <- update(m1, method.ci = "SACC")
#' m3 <- update(m1, method.ci = "WS")
#' m4 <- update(m1, method.ci = "WSCC")
#' m5 <- update(m1, method.ci = "CP")
#' #
#' lower <- round(rbind(NA, m1$lower, m2$lower, NA, m3$lower,
#' m4$lower, NA, m5$lower), 4)
#' upper <- round(rbind(NA, m1$upper, m2$upper, NA, m3$upper,
#' m4$upper, NA, m5$upper), 4)
#' #
#' tab1 <- data.frame(
#' scen1 = meta:::formatCI(lower[, 1], upper[, 1]),
#' scen2 = meta:::formatCI(lower[, 2], upper[, 2]),
#' scen3 = meta:::formatCI(lower[, 3], upper[, 3]),
#' scen4 = meta:::formatCI(lower[, 4], upper[, 4])
#' )
#' names(tab1) <- c("r=81, n=263", "r=15, n=148",
#' "r=0, n=20", "r=1, n=29")
#' row.names(tab1) <- c("Simple", "- SA", "- SACC",
#' "Score", "- WS", "- WSCC", "Binomial", "- CP")
#' tab1[is.na(tab1)] <- ""
#' # Newcombe (1998), Table I, methods 1-5:
#' tab1
#'
#' # Same confidence interval, i.e. unaffected by choice of summary
#' # measure
#' #
#' print(metaprop(event, n, method.ci = "WS", method = "Inverse"), ma = FALSE)
#' print(metaprop(event, n, sm = "PLN", method.ci = "WS"), ma = FALSE)
#' print(metaprop(event, n, sm = "PFT", method.ci = "WS"), ma = FALSE)
#' print(metaprop(event, n, sm = "PAS", method.ci = "WS"), ma = FALSE)
#' print(metaprop(event, n, sm = "PRAW", method.ci = "WS"), ma = FALSE)
#'
#' # Different confidence intervals as argument sm = "NAsm"
#' #
#' print(metaprop(event, n, method.ci = "NAsm", method = "Inverse"), ma = FALSE)
#' print(metaprop(event, n, sm = "PLN", method.ci = "NAsm"), ma = FALSE)
#' print(metaprop(event, n, sm = "PFT", method.ci = "NAsm"), ma = FALSE)
#' print(metaprop(event, n, sm = "PAS", method.ci = "NAsm"), ma = FALSE)
#' print(metaprop(event, n, sm = "PRAW", method.ci = "NAsm"), ma = FALSE)
#'
#' # Different confidence intervals as argument backtransf = FALSE.
#' # Accordingly, method.ci = "NAsm" used internally.
#' #
#' print(metaprop(event, n, method.ci = "WS", method = "Inverse"),
#' ma = FALSE, backtransf = FALSE)
#' print(metaprop(event, n, sm = "PLN", method.ci = "WS"),
#' ma = FALSE, backtransf = FALSE)
#' print(metaprop(event, n, sm = "PFT", method.ci = "WS"),
#' ma = FALSE, backtransf = FALSE)
#' print(metaprop(event, n, sm = "PAS", method.ci = "WS"),
#' ma = FALSE, backtransf = FALSE)
#' print(metaprop(event, n, sm = "PRAW", method.ci = "WS"),
#' ma = FALSE, backtransf = FALSE)
#'
#' # Same results (printed on original and log scale, respectively)
#' #
#' print(metaprop(event, n, sm = "PLN", method.ci = "NAsm"), ma = FALSE)
#' print(metaprop(event, n, sm = "PLN"), ma = FALSE, backtransf = FALSE)
#' # Results for first study (on log scale)
#' round(log(c(0.3079848, 0.2569522, 0.3691529)), 4)
#'
#' # Print results as events per 1000 observations
#' #
#' print(metaprop(6:8, c(100, 1200, 1000), method = "Inverse"),
#' pscale = 1000, digits = 1)
#'
#' @export metaprop
metaprop <- function(event, n, studlab,
##
data = NULL, subset = NULL, exclude = NULL,
cluster = NULL,
method,
##
sm = gs("smprop"),
##
incr = gs("incr"), method.incr = gs("method.incr"),
##
method.ci = gs("method.ci.prop"), level = gs("level"),
##
common = gs("common"),
random = gs("random") | !is.null(tau.preset),
overall = common | random,
overall.hetstat = common | random,
prediction = gs("prediction") | !missing(method.predict),
##
method.tau =
ifelse(!is.na(charmatch(tolower(method), "glmm",
nomatch = NA)),
"ML", gs("method.tau")),
method.tau.ci = gs("method.tau.ci"),
tau.preset = NULL, TE.tau = NULL,
tau.common = gs("tau.common"),
##
level.ma = gs("level.ma"),
method.random.ci = gs("method.random.ci"),
adhoc.hakn.ci = gs("adhoc.hakn.ci"),
##
level.predict = gs("level.predict"),
method.predict = gs("method.predict"),
adhoc.hakn.pi = gs("adhoc.hakn.pi"),
seed.predict = NULL,
##
null.effect = NA,
##
method.bias = gs("method.bias"),
##
backtransf = gs("backtransf"),
pscale = 1,
##
text.common = gs("text.common"),
text.random = gs("text.random"),
text.predict = gs("text.predict"),
text.w.common = gs("text.w.common"),
text.w.random = gs("text.w.random"),
##
title = gs("title"), complab = gs("complab"),
outclab = "",
##
subgroup, subgroup.name = NULL,
print.subgroup.name = gs("print.subgroup.name"),
sep.subgroup = gs("sep.subgroup"),
test.subgroup = gs("test.subgroup"),
prediction.subgroup = gs("prediction.subgroup"),
seed.predict.subgroup = NULL,
##
byvar, hakn, adhoc.hakn,
##
keepdata = gs("keepdata"),
warn = gs("warn"), warn.deprecated = gs("warn.deprecated"),
##
control = NULL,
...
) {
##
##
## (1) Check and set arguments
##
##
missing.method <- missing(method)
if (missing.method)
method <- if (sm == "PLOGIT") "GLMM" else "Inverse"
else
method <- setchar(method, gs("meth4prop"))
is.glmm <- method == "GLMM"
##
chknull(sm)
sm <- setchar(sm, gs("sm4prop"))
##
missing.method.incr <- missing(method.incr)
method.incr <- setchar(method.incr, gs("meth4incr"))
##
chklevel(level)
##
missing.method.tau <- missing(method.tau)
if (missing.method.tau)
method.tau <- if (method == "GLMM") "ML" else gs("method.tau")
method.tau <- setchar(method.tau, gs("meth4tau"))
##
missing.tau.common <- missing(tau.common)
tau.common <- replaceNULL(tau.common, FALSE)
chklogical(tau.common)
##
chklogical(prediction)
chklevel(level.predict)
##
missing.method.predict <- missing(method.predict)
##
method.tau <-
setmethodtau(method.tau, missing.method.tau,
method.predict, missing.method.predict)
method.predict <-
setmethodpredict(method.predict, missing.method.predict,
method.tau, missing.method.tau)
##
if (any(method.predict == "NNF"))
is_installed_package("pimeta", argument = "method.predict", value = "NNF")
##
missing.adhoc.hakn.pi <- missing(adhoc.hakn.pi)
adhoc.hakn.pi <- setchar(adhoc.hakn.pi, gs("adhoc4hakn.pi"))
##
if (!anyNA(null.effect) | length(null.effect) != 1)
chknumeric(null.effect, min = 0, max = 1, length = 1)
##
method.bias <- setmethodbias(method.bias)
##
chklogical(backtransf)
##
chknumeric(pscale, length = 1)
if (!backtransf & pscale != 1 & !is_untransformed(sm)) {
warning("Argument 'pscale' set to 1 as argument 'backtransf' is FALSE.",
call. = FALSE)
pscale <- 1
}
##
if (!is.null(text.common))
chkchar(text.common, length = 1)
if (!is.null(text.random))
chkchar(text.random)
if (!is.null(text.predict))
chkchar(text.predict)
if (!is.null(text.w.common))
chkchar(text.w.common, length = 1)
if (!is.null(text.w.random))
chkchar(text.w.random, length = 1)
##
chklogical(keepdata)
##
## Additional arguments / checks for metainc objects
##
fun <- "metaprop"
##
method.ci <- setchar(method.ci, gs("ci4prop"))
chklogical(warn)
##
## Check for deprecated arguments in '...'
##
args <- list(...)
chklogical(warn.deprecated)
##
level.ma <- deprecated(level.ma, missing(level.ma), args, "level.comb",
warn.deprecated)
chklevel(level.ma)
##
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)
##
method.random.ci <-
deprecated2(method.random.ci, missing(method.random.ci),
hakn, missing(hakn),
warn.deprecated)
if (is.logical(method.random.ci))
if (method.random.ci)
method.random.ci <- "HK"
else
method.random.ci <- "classic"
method.random.ci <- setchar(method.random.ci, gs("meth4random.ci"))
##
missing.adhoc.hakn.ci <- missing(adhoc.hakn.ci)
adhoc.hakn.ci <-
deprecated2(adhoc.hakn.ci, missing(adhoc.hakn.ci),
adhoc.hakn, missing(adhoc.hakn), warn.deprecated)
adhoc.hakn.ci <- setchar(adhoc.hakn.ci, gs("adhoc4hakn.ci"))
##
missing.subgroup.name <- missing(subgroup.name)
subgroup.name <-
deprecated(subgroup.name, missing.subgroup.name, args, "bylab",
warn.deprecated)
##
print.subgroup.name <-
deprecated(print.subgroup.name, missing(print.subgroup.name),
args, "print.byvar", warn.deprecated)
print.subgroup.name <-
replaceNULL(print.subgroup.name, gs("print.subgroup.name"))
chklogical(print.subgroup.name)
##
sep.subgroup <-
deprecated(sep.subgroup, missing(sep.subgroup), args, "byseparator",
warn.deprecated)
if (!is.null(sep.subgroup))
chkchar(sep.subgroup, length = 1)
##
addincr <-
deprecated(method.incr, missing.method.incr, args, "addincr",
warn.deprecated)
allincr <-
deprecated(method.incr, missing.method.incr, args, "allincr",
warn.deprecated)
if (missing.method.incr) {
method.incr <- gs("method.incr")
##
if (is.logical(addincr) && addincr)
method.incr <- "all"
else if (is.logical(allincr) && allincr)
method.incr <- "if0all"
}
##
addincr <- allincr <- FALSE
if (method.incr == "all")
addincr <- TRUE
else if (method.incr == "if0all")
allincr <- TRUE
##
## Some more checks
##
chklogical(overall)
chklogical(overall.hetstat)
##
##
## (2) Read data
##
##
nulldata <- is.null(data)
sfsp <- sys.frame(sys.parent())
mc <- match.call()
##
if (nulldata)
data <- sfsp
##
## Catch 'event' and 'n' from data:
##
event <- catch("event", mc, data, sfsp)
chknull(event)
k.All <- length(event)
##
n <- catch("n", mc, data, sfsp)
chknull(n)
##
## Catch 'incr' from data:
##
if (!missing(incr))
incr <- catch("incr", mc, data, sfsp)
chknumeric(incr, min = 0)
##
## Catch 'studlab', 'subgroup', 'subset', 'exclude' and 'cluster'
## from data:
##
studlab <- catch("studlab", mc, data, sfsp)
studlab <- setstudlab(studlab, k.All)
##
missing.subgroup <- missing(subgroup)
subgroup <- catch("subgroup", mc, data, sfsp)
missing.byvar <- missing(byvar)
byvar <- catch("byvar", mc, data, sfsp)
##
subgroup <- deprecated2(subgroup, missing.subgroup, byvar, missing.byvar,
warn.deprecated)
by <- !is.null(subgroup)
##
subset <- catch("subset", mc, data, sfsp)
missing.subset <- is.null(subset)
##
exclude <- catch("exclude", mc, data, sfsp)
missing.exclude <- is.null(exclude)
##
cluster <- catch("cluster", mc, data, sfsp)
with.cluster <- !is.null(cluster)
##
##
## (3) Check length of essential variables
##
##
chklength(n, k.All, fun)
chklength(studlab, k.All, fun)
if (with.cluster)
chklength(cluster, k.All, fun)
##
if (length(incr) > 1)
chklength(incr, k.All, fun)
##
if (by) {
chklength(subgroup, k.All, fun)
chklogical(test.subgroup)
chklogical(prediction.subgroup)
}
##
## Additional checks
##
if (!by & tau.common) {
warning("Value for argument 'tau.common' set to FALSE as ",
"argument 'subgroup' is missing.",
call. = FALSE)
tau.common <- FALSE
}
if (by & !tau.common & !is.null(tau.preset)) {
warning("Argument 'tau.common' set to TRUE as ",
"argument tau.preset is not NULL.",
call. = FALSE)
tau.common <- TRUE
}
##
##
## (4) Subset, exclude studies, and subgroups
##
##
if (!missing.subset)
if ((is.logical(subset) & (sum(subset) > k.All)) ||
(length(subset) > k.All))
stop("Length of subset is larger than number of studies.")
##
if (!missing.exclude) {
if ((is.logical(exclude) & (sum(exclude) > k.All)) ||
(length(exclude) > k.All))
stop("Length of argument 'exclude' is larger than number of studies.")
##
exclude2 <- rep(FALSE, k.All)
exclude2[exclude] <- TRUE
exclude <- exclude2
}
else
exclude <- rep(FALSE, k.All)
##
##
## (5) Store complete dataset in list object data
## (if argument keepdata is TRUE)
##
##
if (keepdata) {
if (nulldata)
data <- data.frame(.event = event)
else
data$.event <- event
##
data$.n <- n
data$.studlab <- studlab
##
data$.incr <- incr
##
if (by)
data$.subgroup <- subgroup
##
if (!missing.subset) {
if (length(subset) == dim(data)[1])
data$.subset <- subset
else {
data$.subset <- FALSE
data$.subset[subset] <- TRUE
}
}
##
if (!missing.exclude)
data$.exclude <- exclude
##
if (with.cluster)
data$.id <- data$.cluster <- cluster
}
##
##
## (6) Use subset for analysis
##
##
if (!missing.subset) {
event <- event[subset]
n <- n[subset]
studlab <- studlab[subset]
##
cluster <- cluster[subset]
exclude <- exclude[subset]
##
if (length(incr) > 1)
incr <- incr[subset]
##
if (by)
subgroup <- subgroup[subset]
}
##
## Determine total number of studies
##
k.all <- length(event)
##
if (k.all == 0)
stop("No studies to combine in meta-analysis.")
##
## No meta-analysis for a single study
##
if (k.all == 1) {
common <- FALSE
random <- FALSE
prediction <- FALSE
overall <- FALSE
overall.hetstat <- FALSE
}
##
## Check variable values
##
chknumeric(event, 0)
chknumeric(n, 0, zero = TRUE)
##
if (any(event > n, na.rm = TRUE))
stop("Number of events must not be larger than number of observations")
##
## Recode integer as numeric:
##
event <- int2num(event)
n <- int2num(n)
##
## Check for whole numbers
##
if (method.ci != "NAsm") {
if (any(!is_wholenumber(event), na.rm = TRUE)) {
warning("Normal approximation confidence interval ",
"(argument method.ci = \"NAsm\") used as\n",
"at least one number of events contains a non-integer value.",
call. = FALSE)
method.ci <- "NAsm"
}
else if (any(!is_wholenumber(n), na.rm = TRUE)) {
warning("Normal approximation confidence interval ",
"(argument method.ci = \"NAsm\") used as\n",
"at least one sample size contains a non-integer value.",
call. = FALSE)
method.ci <- "NAsm"
}
}
##
if (by) {
chkmiss(subgroup)
##
if (missing.subgroup.name & is.null(subgroup.name)) {
if (!missing.subgroup)
subgroup.name <- byvarname("subgroup", mc)
else if (!missing.byvar)
subgroup.name <- byvarname("byvar", mc)
}
}
##
if (!is.null(subgroup.name))
chkchar(subgroup.name, length = 1)
##
##
## (7) Calculate results for individual studies
##
##
sel <- switch(sm,
PLOGIT = event == 0 | (n - event) == 0,
PAS = rep(FALSE, length(event)),
PFT = rep(FALSE, length(event)),
PLN = event == 0 | (n - event) == 0,
PRAW = event == 0 | (n - event) == 0)
##
sparse <- any(sel, na.rm = TRUE)
##
## No need to add anything to cell counts for arcsine transformation
##
if (addincr)
incr.event <- if (length(incr) == 1) rep(incr, k.all) else incr
else
if (sparse)
if (allincr)
incr.event <- if (length(incr) == 1) rep(incr, k.all) else incr
else
incr.event <- incr * sel
else
incr.event <- rep(0, k.all)
##
if (sm == "PLOGIT") {
TE <- log((event + incr.event) / (n - event + incr.event))
seTE <- sqrt(1 / (event + incr.event) +
1 / ((n - event + incr.event)))
transf.null.effect <- log(null.effect / (1 - null.effect))
}
else if (sm == "PAS") {
TE <- asin(sqrt(event / n))
seTE <- sqrt(1 / (4 * n))
transf.null.effect <- asin(sqrt(null.effect))
}
else if (sm == "PFT") {
TE <- 0.5 * (asin(sqrt(event / (n + 1))) + asin(sqrt((event + 1) / (n + 1))))
seTE <- sqrt(1 / (4 * n + 2))
transf.null.effect <- asin(sqrt(null.effect))
}
else if (sm == "PLN") {
TE <- log((event + incr.event) / (n + incr.event))
## Hartung, Knapp (2001), p. 3880, formula (18):
seTE <- ifelse(event == n,
sqrt(1 / event - 1 / (n + incr.event)),
sqrt(1 / (event + incr.event) - 1 / (n + incr.event))
)
transf.null.effect <- log(null.effect)
}
else if (sm == "PRAW") {
TE <- event / n
seTE <- sqrt((event + incr.event) * (n - event + incr.event) /
(n + 2 * incr.event)^3)
transf.null.effect <- null.effect
}
##
## Calculate confidence intervals
##
if (method.ci == "CP")
ci.study <- ciClopperPearson(event, n, level, null.effect)
else if (method.ci == "WS")
ci.study <- ciWilsonScore(event, n, level)
else if (method.ci == "WSCC")
ci.study <- ciWilsonScore(event, n, level, correct = TRUE)
else if (method.ci == "AC")
ci.study <- ciAgrestiCoull(event, n, level)
else if (method.ci == "SA")
ci.study <- ciSimpleAsymptotic(event, n, level)
else if (method.ci == "SACC")
ci.study <- ciSimpleAsymptotic(event, n, level, correct = TRUE)
else if (method.ci == "NAsm")
ci.study <- ci(TE, seTE, level, null.effect = null.effect)
##
lower.study <- ci.study$lower
upper.study <- ci.study$upper
##
if (method.ci == "NAsm") {
if (sm == "PLOGIT") {
lower.study <- logit2p(lower.study)
upper.study <- logit2p(upper.study)
}
##
else if (sm == "PAS") {
lower.study <- asin2p(lower.study, value = "lower")
upper.study <- asin2p(upper.study, value = "upper")
}
##
else if (sm == "PFT") {
lower.study <- asin2p(lower.study, n, value = "lower")
upper.study <- asin2p(upper.study, n, value = "upper")
}
##
else if (sm == "PLN") {
lower.study <- exp(lower.study)
upper.study <- exp(upper.study)
}
##
lower.study[lower.study < 0] <- 0
upper.study[upper.study > 1] <- 1
}
##
##
## (8) Additional checks for three-level model
##
##
three.level <- FALSE
sel.ni <- !is.infinite(TE) & !is.infinite(seTE)
##
## Only conduct three-level meta-analysis if variable 'cluster'
## contains duplicate values after removing inestimable study
## results standard errors
##
if (with.cluster &&
length(unique(cluster[sel.ni])) != length(cluster[sel.ni]))
three.level <- TRUE
##
if (three.level) {
chkmlm(method.tau, missing.method.tau, method.predict,
by, tau.common, missing.tau.common,
method, missing.method)
##
common <- FALSE
method <- "Inverse"
is.glmm <- FALSE
##
if (!(method.tau %in% c("REML", "ML")))
method.tau <- "REML"
}
##
##
## (9) Additional checks for GLMM
##
##
if (is.glmm) {
chkglmm(sm, method.tau, method.random.ci, method.predict,
adhoc.hakn.ci, adhoc.hakn.pi,
"PLOGIT")
##
if (!is.null(TE.tau)) {
if (warn)
warning("Argument 'TE.tau' not considered for GLMM.",
call. = FALSE)
TE.tau <- NULL
}
##
if (!is.null(tau.preset)) {
if (warn)
warning("Argument 'tau.preset' not considered for GLMM.",
call. = FALSE)
tau.preset <- NULL
}
}
##
##
## (10) Do meta-analysis
##
##
k <- sum(!is.na(event[!exclude]) & !is.na(n[!exclude]))
##
for (i in seq_along(method.random.ci))
if (k == 1 & method.random.ci[i] == "HK")
method.random.ci[i] <- "classic"
##
m <- metagen(TE, seTE, studlab,
exclude = if (missing.exclude) NULL else exclude,
cluster = cluster,
##
sm = sm,
level = level,
##
common = common,
random = random,
overall = overall,
overall.hetstat = overall.hetstat,
prediction = prediction,
##
method.tau = method.tau, method.tau.ci = method.tau.ci,
tau.preset = tau.preset,
TE.tau = TE.tau,
tau.common = FALSE,
##
level.ma = level.ma,
method.random.ci = method.random.ci,
adhoc.hakn.ci = adhoc.hakn.ci,
##
level.predict = level.predict,
method.predict = method.predict,
adhoc.hakn.pi = adhoc.hakn.pi,
seed.predict = seed.predict,
##
null.effect = transf.null.effect,
##
method.bias = method.bias,
##
backtransf = backtransf,
##
text.common = text.common, text.random = text.random,
text.predict = text.predict,
text.w.common = text.w.common, text.w.random = text.w.random,
##
title = title, complab = complab, outclab = outclab,
##
keepdata = FALSE,
warn = warn,
##
control = control)
##
if (by & tau.common & !is.glmm) {
## Estimate common tau-squared across subgroups
hcc <- hetcalc(TE, seTE, method.tau, "",
TE.tau, level.ma, subgroup, control)
}
##
##
## (9) Generate R object
##
##
res <- list(event = event, n = n,
incr = if (length(unique(incr)) == 1) unique(incr) else incr,
method.incr = method.incr,
sparse = sparse,
method.ci = method.ci,
incr.event = incr.event)
##
## Add meta-analysis results
## (after removing unneeded list elements)
##
m$n.e <- NULL
m$n.c <- NULL
m$pscale <- NULL
m$irscale <- NULL
m$irunit <- NULL
m$method.ci <- NULL
m$method.mean <- NULL
m$approx.TE <- NULL
m$approx.seTE <- NULL
##
m$label.e <- ""
m$label.c <- ""
m$label.left <- ""
m$label.right <- ""
##
if (method.ci == "CP") {
m$statistic <- rep(NA, length(m$statistic))
m$pval <- ci.study$p
}
else if (method.ci != "NAsm") {
m$statistic <- rep(NA, length(m$statistic))
m$pval <- rep(NA, length(m$pval))
}
##
if (is.glmm | three.level) {
m$seTE.hakn.ci <- m$seTE.hakn.adhoc.ci <-
m$seTE.hakn.pi <- m$seTE.hakn.adhoc.pi <-
m$seTE.kero <- NA
##
m$text.random <- gsub("(HK)", "(T)", m$text.random, fixed = TRUE)
}
##
res <- c(res, m)
res$null.effect <- null.effect
##
## Run GLMM and add data
##
if (is.glmm & k > 0) {
res$method <- "GLMM"
res$method.random <- "GLMM"
##
list.prop <- list(xi = event[!exclude], ni = n[!exclude], measure = "PLO")
##
use.random <-
sum(!exclude) > 1 &
sum(event[!exclude], na.rm = TRUE) > 0 &
any(event[!exclude] != n[!exclude])
##
res.glmm <-
runGLMM(list.prop,
method.tau = method.tau,
method.random.ci = method.random.ci,
level = level.ma,
control = control, use.random = use.random)
##
res <- addGLMM(res, res.glmm)
##
if (by) {
n.subgroups <- length(unique(subgroup[!exclude]))
if (n.subgroups > 1)
subgroup.glmm <-
factor(subgroup[!exclude], bylevs(subgroup[!exclude]))
##
hcc <-
hccGLMM(
res,
runGLMM(list.prop,
method.tau = method.tau,
method.random.ci = method.random.ci,
level = level.ma,
data =
if (n.subgroups > 1)
list(data = data.frame(subgroup.glmm))
else
NULL,
mods =
if (n.subgroups > 1)
as.call(~ subgroup.glmm)
else
NULL,
control = control, use.random = use.random)$glmm.random[[1]]
)
}
}
##
res$lower <- lower.study
res$upper <- upper.study
##
res$pscale <- pscale
##
res$call <- match.call()
res$allincr <- allincr
res$addincr <- addincr
##
if (keepdata) {
res$data <- data
if (!missing.subset)
res$subset <- subset
}
##
class(res) <- c(fun, "meta")
##
## Add results from subgroup analysis
##
if (by) {
res$subgroup <- subgroup
res$subgroup.name <- subgroup.name
res$print.subgroup.name <- print.subgroup.name
res$sep.subgroup <- sep.subgroup
res$test.subgroup <- test.subgroup
res$prediction.subgroup <- prediction.subgroup
res$tau.common <- tau.common
##
if (!tau.common) {
res <- c(res, subgroup(res, seed = seed.predict.subgroup))
if (res$three.level)
res <- setNA3(res)
}
else if (!is.null(tau.preset))
res <-
c(res, subgroup(res, tau.preset, seed = seed.predict.subgroup))
else {
if (is.glmm)
res <- c(res,
subgroup(res, NULL,
factor(res$subgroup, bylevs(res$subgroup)), ...))
else if (res$three.level)
res <- c(res,
subgroup(res, NULL,
factor(res$subgroup, bylevs(res$subgroup))))
else
res <-
c(res, subgroup(res, hcc$tau.resid, seed = seed.predict.subgroup))
}
##
if (tau.common && is.null(tau.preset))
res <- addHet(res, hcc, !is.glmm)
##
res$n.e.w <- NULL
res$n.c.w <- NULL
res$event.e.w <- NULL
res$event.c.w <- NULL
##
res$time.e.w <- NULL
res$time.c.w <- NULL
res$t.harmonic.mean.w <- NULL
##
res <- setNAwithin(res, res$three.level | is.glmm)
}
##
## Backward compatibility
##
res <- backward(res)
##
class(res) <- c(fun, "meta")
res
}
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