R/metainc.R
In guido-s/meta: General Package for Meta-Analysis
Defines functions
metainc
Documented in
metainc
#' Meta-analysis of incidence rates
#'
#' @description
#' Calculation of common effect and random effects estimates (incidence
#' rate ratio or incidence rate difference) for meta-analyses with
#' event counts. Mantel-Haenszel, Cochran, 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.e Number of events in experimental group.
#' @param time.e Person time at risk in experimental group.
#' @param event.c Number of events in control group.
#' @param time.c Person time at risk in control group.
#' @param studlab An optional vector with study labels.
#' @param data An optional data frame containing the study
#' information, i.e., event.e, time.e, event.c, and time.c.
#' @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 rho Assumed correlation of estimates within a cluster.
#' @param method A character string indicating which method is to be
#' used for pooling of studies. One of \code{"MH"},
#' \code{"Inverse"}, \code{"Cochran"}, or \code{"GLMM"} can be
#' abbreviated.
#' @param sm A character string indicating which summary measure
#' (\code{"IRR"}, \code{"IRD"}, \code{"IRSD"}, or \code{"VE"}) is to
#' be used for pooling of studies, see Details.
#' @param incr A numerical value which is added to cell frequencies
#' for studies with a zero cell count, see Details.
#' @param method.incr A character string indicating which continuity
#' correction method should be used (\code{"only0"},
#' \code{"if0all"}, or \code{"all"}), see Details.
#' @param model.glmm A character string indicating which GLMM should
#' be used. One of \code{"UM.FS"}, \code{"UM.RS"}, and
#' \code{"CM.EL"}, 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 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 n.e Number of observations in experimental group (optional).
#' @param n.c Number of observations in control group (optional).
#' @param backtransf A logical indicating whether results for
#' incidence rate ratio (\code{sm = "IRR"}) and vaccine efficacy or
#' vaccine effectiveness (\code{sm = "VE"}) should be back
#' transformed in printouts and plots. If TRUE (default), results
#' will be presented as incidence rate ratios or vaccine efficacy /
#' effectiveness; otherwise log incidence rate ratios or log vaccine
#' rate ratios will be shown.
#' @param irscale A numeric defining a scaling factor for printing of
#' incidence rate differences.
#' @param irunit A character string specifying the time unit used to
#' calculate rates, e.g. person-years.
#' @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 label.e Label for experimental group.
#' @param label.c Label for control group.
#' @param label.left Graph label on left side of forest plot.
#' @param label.right Graph label on right side of forest plot.
#' @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 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 warnings should be printed
#' (e.g., if \code{incr} is added to studies with zero cell
#' frequencies).
#' @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
#' Calculation of common and random effects estimates for meta-analyses
#' comparing two incidence rates.
#'
#' The following measures of treatment effect are available:
#' \itemize{
#' \item Incidence Rate Ratio (\code{sm = "IRR"})
#' \item Incidence Rate Difference (\code{sm = "IRD"})
#' \item Square root transformed Incidence Rate Difference (\code{sm =
#' "IRSD"})
#' \item Vaccine efficacy or vaccine effectiveness (\code{sm = "VE"})
#' }
#'
#' Note, log incidence rate ratio (logIRR) and log vaccine ratio
#' (logVR) are mathematical identical, however, back-transformed
#' results differ as vaccine efficacy or effectiveness is defined as
#' \code{VE = 100 * (1 - IRR)}.
#'
#' 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{Meta-analysis method}{
#'
#' By default, both common effect and random effects models are
#' considered (see arguments \code{common} and \code{random}). If
#' \code{method} is \code{"MH"} (default), the Mantel-Haenszel method
#' is used to calculate the common effect estimate (Greenland &
#' Robbins, 1985); if \code{method} is \code{"Inverse"}, inverse
#' variance weighting is used for pooling; if \code{method} is
#' \code{"Cochran"}, the Cochran method is used for pooling
#' (Bayne-Jones, 1964, Chapter 8). For these three methods, the random
#' effects estimate is always based on the inverse variance method.
#'
#' A distinctive and frequently overlooked advantage of incidence
#' rates is that individual patient data (IPD) can be extracted from
#' count data. Accordingly, statistical methods for IPD, i.e.,
#' generalised linear mixed models, can be utilised in a meta-analysis
#' of incidence rate ratios (Stijnen et al., 2010). These methods are
#' available (argument \code{method = "GLMM"}) for the common effect
#' and random effects model by calling the
#' \code{\link[metafor]{rma.glmm}} function from R package
#' \bold{metafor} internally.
#'
#' Three different GLMMs are available for meta-analysis of incidence
#' rate ratios using argument \code{model.glmm} (which corresponds to
#' argument \code{model} in the \code{\link[metafor]{rma.glmm}}
#' function):
#' \tabular{cl}{
#' 1. \tab Poisson regression model with fixed study effects (default)
#' \cr
#' \tab (\code{model.glmm = "UM.FS"}, i.e., \bold{U}nconditional
#' \bold{M}odel - \bold{F}ixed \bold{S}tudy effects) \cr
#' 2. \tab Mixed-effects Poisson regression model with random study
#' effects \cr
#' \tab (\code{model.glmm = "UM.RS"}, i.e., \bold{U}nconditional
#' \bold{M}odel - \bold{R}andom \bold{S}tudy effects) \cr
#' 3. \tab Generalised linear mixed model (conditional Poisson-Normal)
#' \cr
#' \tab (\code{model.glmm = "CM.EL"}, i.e., \bold{C}onditional
#' \bold{M}odel - \bold{E}xact \bold{L}ikelihood)
#' }
#'
#' Details on these three GLMMs as well as additional arguments which
#' can be provided using argument '\code{\dots}' in \code{metainc}
#' are described in \code{\link[metafor]{rma.glmm}} where you can also
#' find information on the iterative algorithms used for estimation.
#' Note, regardless of which value is used for argument
#' \code{model.glmm}, results for two different GLMMs are calculated:
#' common effect model (with fixed treatment effect) and random effects
#' model (with random treatment effects).
#' }
#'
#' \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", default})
#' \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"})
#' }
#'
#' For studies with a zero cell count, by default, 0.5 is added to all
#' cell frequencies of these studies (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 Mantel-Haenszel method, Cochran
#' method, and GLMMs, nothing is added to zero cell counts.
#' Accordingly, estimates for these methods are not defined if the
#' number of events is zero in all studies either in the experimental
#' or control group.
#' }
#'
#' \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{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}.
#'
#' A prediction interval will only be shown if \code{prediction =
#' TRUE}.
#' }
#'
#' @return
#' An object of class \code{c("metainc", "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{metabin}},
#' \code{\link{update.meta}}, \code{\link{print.meta}}
#'
#' @references
#' Bayne-Jones S et al. (1964):
#' Smoking and Health: Report of the Advisory Committee to the Surgeon
#' General of the United States.
#' U-23 Department of Health, Education, and Welfare.
#' Public Health Service Publication No. 1103.
#'
#' Greenland S & Robins JM (1985):
#' Estimation of a common effect parameter from sparse follow-up data.
#' \emph{Biometrics},
#' \bold{41}, 55--68
#'
#' 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
#'
#' @examples
#' data(smoking)
#' m1 <- metainc(d.smokers, py.smokers, d.nonsmokers, py.nonsmokers,
#' data = smoking, studlab = study)
#' print(m1, digits = 2)
#'
#' m2 <- update(m1, method = "Cochran")
#' print(m2, digits = 2)
#'
#' data(lungcancer)
#' m3 <- metainc(d.smokers, py.smokers, d.nonsmokers, py.nonsmokers,
#' data = lungcancer, studlab = study)
#' print(m3, digits = 2)
#'
#' # Redo Cochran meta-analysis with inflated standard errors
#' #
#' # All cause mortality
#' #
#' TEa <- log((smoking$d.smokers/smoking$py.smokers) /
#' (smoking$d.nonsmokers/smoking$py.nonsmokers))
#' seTEa <- sqrt(1 / smoking$d.smokers + 1 / smoking$d.nonsmokers +
#' 2.5 / smoking$d.nonsmokers)
#' metagen(TEa, seTEa, sm = "IRR", studlab = smoking$study)
#'
#' # Lung cancer mortality
#' #
#' TEl <- log((lungcancer$d.smokers/lungcancer$py.smokers) /
#' (lungcancer$d.nonsmokers/lungcancer$py.nonsmokers))
#' seTEl <- sqrt(1 / lungcancer$d.smokers + 1 / lungcancer$d.nonsmokers +
#' 2.25 / lungcancer$d.nonsmokers)
#' metagen(TEl, seTEl, sm = "IRR", studlab = lungcancer$study)
#'
#' \dontrun{
#' # Meta-analysis using generalised linear mixed models
#' # (only if R packages 'metafor' and 'lme4' are available)
#'
#' # Poisson regression model (fixed study effects)
#' #
#' m4 <- metainc(d.smokers, py.smokers, d.nonsmokers, py.nonsmokers,
#' data = smoking, studlab = study, method = "GLMM")
#' m4
#'
#' # Mixed-effects Poisson regression model (random study effects)
#' #
#' update(m4, model.glmm = "UM.RS", nAGQ = 1)
#' #
#' # Generalised linear mixed model (conditional Poisson-Normal)
#' #
#' update(m4, model.glmm = "CM.EL")
#' }
#'
#' @export metainc
metainc <- function(event.e, time.e, event.c, time.c, studlab,
##
data = NULL, subset = NULL, exclude = NULL,
cluster = NULL, rho = 0,
##
method = if (sm == "IRSD") "Inverse" else "MH",
sm = gs("sminc"),
incr = gs("incr"), method.incr = gs("method.incr"),
model.glmm = "UM.FS",
##
level = gs("level"),
common = gs("common"),
random = gs("random") | !is.null(tau.preset),
overall = common | random,
overall.hetstat =
if (is.null(gs("overall.hetstat")))
common | random
else
gs("overall.hetstat"),
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,
##
method.bias = gs("method.bias"),
##
n.e = NULL, n.c = NULL,
##
backtransf = if (sm == "IRSD") FALSE else gs("backtransf"),
irscale = 1, irunit = "person-years",
##
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 = "",
label.e = gs("label.e"), label.c = gs("label.c"),
label.left = gs("label.left"),
label.right = gs("label.right"),
##
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 arguments
##
##
chknumeric(rho, min = -1, max = 1)
##
chknull(sm)
sm <- setchar(sm, gs("sm4inc"))
##
chklevel(level)
##
missing.method.tau <- missing(method.tau)
method.tau <- setchar(method.tau, gs("meth4tau"))
##
if (is.null(method.tau.ci))
method.tau.ci <- if (method.tau == "DL") "J" else "QP"
method.tau.ci <- setchar(method.tau.ci, gs("meth4tau.ci"))
##
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.predict <- setchar(method.predict, gs("meth4pi"))
##
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"))
##
method.bias <- setmethodbias(method.bias)
##
chklogical(backtransf)
##
chknumeric(irscale, length = 1)
chkchar(irunit)
##
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 <- "metainc"
##
if (sm != "IRD" & irscale != 1) {
warning("Argument 'irscale' only considered for ",
"incidence rate differences.",
call. = FALSE)
irscale <- 1
}
##
missing.method <- missing(method)
method <- setchar(method, gs("meth4inc"))
##
missing.method.incr <- missing(method.incr)
method.incr <- setchar(method.incr, gs("meth4incr"))
##
is.glmm <- method == "GLMM"
##
model.glmm <- setchar(model.glmm, c("UM.FS", "UM.RS", "CM.EL"))
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.e', 'time.e', 'event.c', 'time.c', 'n.e', and 'n.c'
## from data:
##
event.e <- catch("event.e", mc, data, sfsp)
chknull(event.e)
k.All <- length(event.e)
##
time.e <- catch("time.e", mc, data, sfsp)
chknull(time.e)
##
event.c <- catch("event.c", mc, data, sfsp)
chknull(event.c)
##
time.c <- catch("time.c", mc, data, sfsp)
chknull(time.c)
##
n.e <- catch("n.e", mc, data, sfsp)
null.n.e <- is.null(n.e)
##
n.c <- catch("n.c", mc, data, sfsp)
null.n.c <- is.null(n.c)
##
## 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(time.e, k.All, fun)
chklength(event.c, k.All, fun)
chklength(time.c, 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)
}
##
if (!null.n.e)
chklength(n.e, k.All, fun)
if (!is.null(n.c))
chklength(n.c, k.All, fun)
##
## 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.e = event.e)
else
data$.event.e <- event.e
##
data$.time.e <- time.e
data$.event.c <- event.c
data$.time.c <- time.c
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$.exclude <- exclude
##
if (!null.n.e)
data$.n.e <- n.e
if (!null.n.e)
data$.n.c <- n.c
}
##
##
## (6) Use subset for analysis
##
##
if (!missing.subset) {
event.e <- event.e[subset]
time.e <- time.e[subset]
event.c <- event.c[subset]
time.c <- time.c[subset]
studlab <- studlab[subset]
##
cluster <- cluster[subset]
exclude <- exclude[subset]
##
if (length(incr) > 1)
incr <- incr[subset]
##
if (by)
subgroup <- subgroup[subset]
##
if (!null.n.e)
n.e <- n.e[subset]
if (!is.null(n.c))
n.c <- n.c[subset]
}
##
## Determine total number of studies
##
k.all <- length(event.e)
##
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.e, 0)
chknumeric(time.e, 0, zero = TRUE)
chknumeric(event.c, 0)
chknumeric(time.c, zero = TRUE)
##
## Recode integer as numeric:
##
event.e <- int2num(event.e)
time.e <- int2num(time.e)
event.c <- int2num(event.c)
time.c <- int2num(time.c)
##
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,
IRD = event.e == 0 | event.c == 0,
IRR = event.e == 0 | event.c == 0,
VE = event.e == 0 | event.c == 0,
IRSD = event.e == 0 | event.c == 0)
##
## Sparse computation
##
sparse <- any(sel, na.rm = TRUE)
##
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 %in% c("IRR", "VE")) {
TE <- log(((event.e + incr.event) / time.e) /
((event.c + incr.event) / time.c))
seTE <- sqrt(1 / (event.e + incr.event) + 1 / (event.c + incr.event))
}
else if (sm == "IRD") {
TE <- event.e / time.e - event.c / time.c
seTE <- sqrt((event.e + incr.event) / time.e^2 +
(event.c + incr.event) / time.c^2)
}
else if (sm == "IRSD") {
TE <- sqrt(event.e / time.e) - sqrt(event.c / time.c)
seTE <- sqrt(0.25 / time.e + 0.25 / time.c)
}
##
##
## (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,
method, missing.method)
##
common <- FALSE
method <- "Inverse"
is.glmm <- FALSE
##
if (!(method.tau %in% c("REML", "ML")))
method.tau <- "REML"
}
##
##
## (9) Additional checks for GLMMs
##
##
if (is.glmm) {
chkglmm(sm, method.tau, method.random.ci, method.predict,
adhoc.hakn.ci, adhoc.hakn.pi,
c("IRR", "VE"))
##
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
}
##
if (sparse)
if ((!missing(incr) & any(incr != 0)) |
allincr | addincr)
warning("Note, for method = \"GLMM\", continuity correction only ",
"used to calculate individual study results.",
call. = FALSE)
}
##
##
## (10) Do meta-analysis
##
##
k <- sum(!is.na(event.e[!exclude]) & !is.na(event.c[!exclude]) &
!is.na(time.e[!exclude]) & !is.na(time.c[!exclude]))
##
for (i in seq_along(method.random.ci))
if (k == 1 & method.random.ci[i] == "HK")
method.random.ci[i] <- "classic"
##
if (method == "MH") {
##
## Greenland, Robins (1985)
##
x.k <- event.e
y.k <- event.c
n.k <- time.e
m.k <- time.c
##
N.k <- n.k + m.k
t.k <- x.k + y.k
##
if (sm %in% c("IRR", "VE")) {
D <- n.k * m.k * t.k / N.k^2
R <- x.k * m.k / N.k
S <- y.k * n.k / N.k
##
D[exclude] <- R[exclude] <- S[exclude] <- 0
##
w.common <- S
TE.common <- log(sum(R, na.rm = TRUE) / sum(S, na.rm = TRUE))
seTE.common <- sqrt(sum(D, na.rm = TRUE) / (sum(R, na.rm = TRUE) *
sum(S, na.rm = TRUE)))
}
else if (sm == "IRD") {
L <- (x.k * m.k^2 + y.k * n.k^2) / N.k^2
S <- n.k * m.k / N.k
##
L[exclude] <- S[exclude] <- 0
##
w.common <- S
TE.common <- weighted.mean(TE, w.common, na.rm = TRUE)
seTE.common <- sqrt(sum(L, na.rm = TRUE) / sum(S, na.rm = TRUE)^2)
}
}
##
else if (method == "Cochran") {
##
## Smoking and Health - Report of the Advisory Committee to the
## Surgeon General of the Public Health Service,
## Chapter 8
##
if (sm %in% c("IRR", "VE")) {
w.common <- event.c * time.e / time.c
w.common[exclude] <- 0
TE.common <- weighted.mean(TE, w.common)
seTE.common <- sqrt(1 / sum(event.e) + 1 / sum(event.c))
}
else if (sm == "IRD") {
warning("Cochran method only available for ",
"Incidence Rate Ratio (sm = \"IRR\") ",
"and Vaccine Efficacy / Effectiveness (sm = \"VE\")",
call. = FALSE)
return(NULL)
}
}
else if (is.glmm) {
list.inc <- list(x1i = event.e[!exclude], t1i = time.e[!exclude],
x2i = event.c[!exclude], t2i = time.c[!exclude],
measure = "IRR", model = model.glmm)
##
use.random <-
sum(!exclude) > 1 &
!((sum(event.e[!exclude], na.rm = TRUE) == 0 &
sum(event.c[!exclude], na.rm = TRUE) == 0) |
(!any(event.e[!exclude] != time.e[!exclude]) |
!any(event.c[!exclude] != time.c[!exclude])))
##
res.glmm <-
runGLMM(list.inc,
method.tau = method.tau,
method.random.ci = method.random.ci,
level = level.ma,
control = control, use.random = use.random)
##
TE.common <- as.numeric(res.glmm$glmm.common$b)
seTE.common <- as.numeric(res.glmm$glmm.common$se)
##
w.common <- rep(NA, length(event.e))
}
##
m <- metagen(TE, seTE, studlab,
exclude = if (missing.exclude) NULL else exclude,
cluster = cluster, rho = rho,
##
sm = sm,
level = level,
##
common = common,
random = random,
overall = overall,
overall.hetstat = overall.hetstat,
prediction = prediction,
##
method.tau = method.tau, method.tau.ci = method.tau.ci,
tau.preset = tau.preset,
TE.tau = TE.tau,
tau.common = FALSE,
##
level.ma = level.ma,
method.random.ci = method.random.ci,
adhoc.hakn.ci = adhoc.hakn.ci,
##
level.predict = level.predict,
method.predict = method.predict,
adhoc.hakn.pi = adhoc.hakn.pi,
seed.predict = seed.predict,
##
method.bias = method.bias,
##
backtransf = backtransf,
##
text.common = text.common, text.random = text.random,
text.predict = text.predict,
text.w.common = text.w.common, text.w.random = text.w.random,
##
title = title, complab = complab, outclab = outclab,
label.e = label.e, label.c = label.c,
label.left = label.left, label.right = label.right,
##
keepdata = FALSE,
warn = warn,
##
control = control)
##
if (by & tau.common & !is.glmm) {
## Estimate common tau-squared across subgroups
hcc <- hetcalc(TE, seTE, method.tau, "",
if (method == "Inverse") TE.tau else TE.common,
level.ma, subgroup, control)
}
##
##
## (9) Generate R object
##
##
res <- list(event.e = event.e, time.e = time.e,
event.c = event.c, time.c = time.c,
method = method, method.random = method,
incr = if (length(unique(incr)) == 1) unique(incr) else incr,
method.incr = method.incr,
sparse = sparse,
incr.event = incr.event,
k.MH = if (method == "MH") sum(w.common > 0) else NA)
##
## Add meta-analysis results
## (after removing unneeded list elements)
##
m$method <- NULL
m$method.random <- NULL
m$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
##
res <- c(res, m)
##
## Add data
##
res$n.e <- n.e
res$n.c <- n.c
res$TE.tau <- TE.tau
##
res$irscale <- irscale
res$irunit <- irunit
##
res$call <- match.call()
res$allincr <- allincr
res$addincr <- addincr
##
if (method %in% c("MH", "Cochran", "GLMM")) {
res <- ci2meta(res, ci.c = ci(TE.common, seTE.common, level = level.ma))
res$w.common <- w.common
}
##
if (is.glmm) {
res <- addGLMM(res, res.glmm)
res$model.glmm <- model.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.inc,
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)$glmm.random[[1]]
)
}
}
##
if (keepdata) {
res$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.w <- NULL
res$event.w <- NULL
##
if (null.n.e)
res$n.e.w <- NULL
if (null.n.c)
res$n.c.w <- NULL
##
res$n.harmonic.mean.w <- NULL
res$t.harmonic.mean.w <- NULL
##
res <- setNAwithin(res, res$three.level | is.glmm)
}
##
## Mantel-Haenszel and Cochran method are common effect methods
##
if (res$method.random %in% c("MH", "Cochran"))
res$method.random <- "Inverse"
##
## Backward compatibility
##
res <- backward(res)
##
class(res) <- c(fun, "meta")
res
}
guido-s/meta documentation built on April 18, 2024, 7:11 p.m.
R Package Documentation
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Defines functions metainc
Documented in metainc
#' Meta-analysis of incidence rates
#'
#' @description
#' Calculation of common effect and random effects estimates (incidence
#' rate ratio or incidence rate difference) for meta-analyses with
#' event counts. Mantel-Haenszel, Cochran, 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.e Number of events in experimental group.
#' @param time.e Person time at risk in experimental group.
#' @param event.c Number of events in control group.
#' @param time.c Person time at risk in control group.
#' @param studlab An optional vector with study labels.
#' @param data An optional data frame containing the study
#' information, i.e., event.e, time.e, event.c, and time.c.
#' @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 rho Assumed correlation of estimates within a cluster.
#' @param method A character string indicating which method is to be
#' used for pooling of studies. One of \code{"MH"},
#' \code{"Inverse"}, \code{"Cochran"}, or \code{"GLMM"} can be
#' abbreviated.
#' @param sm A character string indicating which summary measure
#' (\code{"IRR"}, \code{"IRD"}, \code{"IRSD"}, or \code{"VE"}) is to
#' be used for pooling of studies, see Details.
#' @param incr A numerical value which is added to cell frequencies
#' for studies with a zero cell count, see Details.
#' @param method.incr A character string indicating which continuity
#' correction method should be used (\code{"only0"},
#' \code{"if0all"}, or \code{"all"}), see Details.
#' @param model.glmm A character string indicating which GLMM should
#' be used. One of \code{"UM.FS"}, \code{"UM.RS"}, and
#' \code{"CM.EL"}, 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 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 n.e Number of observations in experimental group (optional).
#' @param n.c Number of observations in control group (optional).
#' @param backtransf A logical indicating whether results for
#' incidence rate ratio (\code{sm = "IRR"}) and vaccine efficacy or
#' vaccine effectiveness (\code{sm = "VE"}) should be back
#' transformed in printouts and plots. If TRUE (default), results
#' will be presented as incidence rate ratios or vaccine efficacy /
#' effectiveness; otherwise log incidence rate ratios or log vaccine
#' rate ratios will be shown.
#' @param irscale A numeric defining a scaling factor for printing of
#' incidence rate differences.
#' @param irunit A character string specifying the time unit used to
#' calculate rates, e.g. person-years.
#' @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 label.e Label for experimental group.
#' @param label.c Label for control group.
#' @param label.left Graph label on left side of forest plot.
#' @param label.right Graph label on right side of forest plot.
#' @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 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 warnings should be printed
#' (e.g., if \code{incr} is added to studies with zero cell
#' frequencies).
#' @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
#' Calculation of common and random effects estimates for meta-analyses
#' comparing two incidence rates.
#'
#' The following measures of treatment effect are available:
#' \itemize{
#' \item Incidence Rate Ratio (\code{sm = "IRR"})
#' \item Incidence Rate Difference (\code{sm = "IRD"})
#' \item Square root transformed Incidence Rate Difference (\code{sm =
#' "IRSD"})
#' \item Vaccine efficacy or vaccine effectiveness (\code{sm = "VE"})
#' }
#'
#' Note, log incidence rate ratio (logIRR) and log vaccine ratio
#' (logVR) are mathematical identical, however, back-transformed
#' results differ as vaccine efficacy or effectiveness is defined as
#' \code{VE = 100 * (1 - IRR)}.
#'
#' 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{Meta-analysis method}{
#'
#' By default, both common effect and random effects models are
#' considered (see arguments \code{common} and \code{random}). If
#' \code{method} is \code{"MH"} (default), the Mantel-Haenszel method
#' is used to calculate the common effect estimate (Greenland &
#' Robbins, 1985); if \code{method} is \code{"Inverse"}, inverse
#' variance weighting is used for pooling; if \code{method} is
#' \code{"Cochran"}, the Cochran method is used for pooling
#' (Bayne-Jones, 1964, Chapter 8). For these three methods, the random
#' effects estimate is always based on the inverse variance method.
#'
#' A distinctive and frequently overlooked advantage of incidence
#' rates is that individual patient data (IPD) can be extracted from
#' count data. Accordingly, statistical methods for IPD, i.e.,
#' generalised linear mixed models, can be utilised in a meta-analysis
#' of incidence rate ratios (Stijnen et al., 2010). These methods are
#' available (argument \code{method = "GLMM"}) for the common effect
#' and random effects model by calling the
#' \code{\link[metafor]{rma.glmm}} function from R package
#' \bold{metafor} internally.
#'
#' Three different GLMMs are available for meta-analysis of incidence
#' rate ratios using argument \code{model.glmm} (which corresponds to
#' argument \code{model} in the \code{\link[metafor]{rma.glmm}}
#' function):
#' \tabular{cl}{
#' 1. \tab Poisson regression model with fixed study effects (default)
#' \cr
#' \tab (\code{model.glmm = "UM.FS"}, i.e., \bold{U}nconditional
#' \bold{M}odel - \bold{F}ixed \bold{S}tudy effects) \cr
#' 2. \tab Mixed-effects Poisson regression model with random study
#' effects \cr
#' \tab (\code{model.glmm = "UM.RS"}, i.e., \bold{U}nconditional
#' \bold{M}odel - \bold{R}andom \bold{S}tudy effects) \cr
#' 3. \tab Generalised linear mixed model (conditional Poisson-Normal)
#' \cr
#' \tab (\code{model.glmm = "CM.EL"}, i.e., \bold{C}onditional
#' \bold{M}odel - \bold{E}xact \bold{L}ikelihood)
#' }
#'
#' Details on these three GLMMs as well as additional arguments which
#' can be provided using argument '\code{\dots}' in \code{metainc}
#' are described in \code{\link[metafor]{rma.glmm}} where you can also
#' find information on the iterative algorithms used for estimation.
#' Note, regardless of which value is used for argument
#' \code{model.glmm}, results for two different GLMMs are calculated:
#' common effect model (with fixed treatment effect) and random effects
#' model (with random treatment effects).
#' }
#'
#' \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", default})
#' \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"})
#' }
#'
#' For studies with a zero cell count, by default, 0.5 is added to all
#' cell frequencies of these studies (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 Mantel-Haenszel method, Cochran
#' method, and GLMMs, nothing is added to zero cell counts.
#' Accordingly, estimates for these methods are not defined if the
#' number of events is zero in all studies either in the experimental
#' or control group.
#' }
#'
#' \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{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}.
#'
#' A prediction interval will only be shown if \code{prediction =
#' TRUE}.
#' }
#'
#' @return
#' An object of class \code{c("metainc", "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{metabin}},
#' \code{\link{update.meta}}, \code{\link{print.meta}}
#'
#' @references
#' Bayne-Jones S et al. (1964):
#' Smoking and Health: Report of the Advisory Committee to the Surgeon
#' General of the United States.
#' U-23 Department of Health, Education, and Welfare.
#' Public Health Service Publication No. 1103.
#'
#' Greenland S & Robins JM (1985):
#' Estimation of a common effect parameter from sparse follow-up data.
#' \emph{Biometrics},
#' \bold{41}, 55--68
#'
#' 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
#'
#' @examples
#' data(smoking)
#' m1 <- metainc(d.smokers, py.smokers, d.nonsmokers, py.nonsmokers,
#' data = smoking, studlab = study)
#' print(m1, digits = 2)
#'
#' m2 <- update(m1, method = "Cochran")
#' print(m2, digits = 2)
#'
#' data(lungcancer)
#' m3 <- metainc(d.smokers, py.smokers, d.nonsmokers, py.nonsmokers,
#' data = lungcancer, studlab = study)
#' print(m3, digits = 2)
#'
#' # Redo Cochran meta-analysis with inflated standard errors
#' #
#' # All cause mortality
#' #
#' TEa <- log((smoking$d.smokers/smoking$py.smokers) /
#' (smoking$d.nonsmokers/smoking$py.nonsmokers))
#' seTEa <- sqrt(1 / smoking$d.smokers + 1 / smoking$d.nonsmokers +
#' 2.5 / smoking$d.nonsmokers)
#' metagen(TEa, seTEa, sm = "IRR", studlab = smoking$study)
#'
#' # Lung cancer mortality
#' #
#' TEl <- log((lungcancer$d.smokers/lungcancer$py.smokers) /
#' (lungcancer$d.nonsmokers/lungcancer$py.nonsmokers))
#' seTEl <- sqrt(1 / lungcancer$d.smokers + 1 / lungcancer$d.nonsmokers +
#' 2.25 / lungcancer$d.nonsmokers)
#' metagen(TEl, seTEl, sm = "IRR", studlab = lungcancer$study)
#'
#' \dontrun{
#' # Meta-analysis using generalised linear mixed models
#' # (only if R packages 'metafor' and 'lme4' are available)
#'
#' # Poisson regression model (fixed study effects)
#' #
#' m4 <- metainc(d.smokers, py.smokers, d.nonsmokers, py.nonsmokers,
#' data = smoking, studlab = study, method = "GLMM")
#' m4
#'
#' # Mixed-effects Poisson regression model (random study effects)
#' #
#' update(m4, model.glmm = "UM.RS", nAGQ = 1)
#' #
#' # Generalised linear mixed model (conditional Poisson-Normal)
#' #
#' update(m4, model.glmm = "CM.EL")
#' }
#'
#' @export metainc
metainc <- function(event.e, time.e, event.c, time.c, studlab,
##
data = NULL, subset = NULL, exclude = NULL,
cluster = NULL, rho = 0,
##
method = if (sm == "IRSD") "Inverse" else "MH",
sm = gs("sminc"),
incr = gs("incr"), method.incr = gs("method.incr"),
model.glmm = "UM.FS",
##
level = gs("level"),
common = gs("common"),
random = gs("random") | !is.null(tau.preset),
overall = common | random,
overall.hetstat =
if (is.null(gs("overall.hetstat")))
common | random
else
gs("overall.hetstat"),
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,
##
method.bias = gs("method.bias"),
##
n.e = NULL, n.c = NULL,
##
backtransf = if (sm == "IRSD") FALSE else gs("backtransf"),
irscale = 1, irunit = "person-years",
##
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 = "",
label.e = gs("label.e"), label.c = gs("label.c"),
label.left = gs("label.left"),
label.right = gs("label.right"),
##
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 arguments
##
##
chknumeric(rho, min = -1, max = 1)
##
chknull(sm)
sm <- setchar(sm, gs("sm4inc"))
##
chklevel(level)
##
missing.method.tau <- missing(method.tau)
method.tau <- setchar(method.tau, gs("meth4tau"))
##
if (is.null(method.tau.ci))
method.tau.ci <- if (method.tau == "DL") "J" else "QP"
method.tau.ci <- setchar(method.tau.ci, gs("meth4tau.ci"))
##
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.predict <- setchar(method.predict, gs("meth4pi"))
##
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"))
##
method.bias <- setmethodbias(method.bias)
##
chklogical(backtransf)
##
chknumeric(irscale, length = 1)
chkchar(irunit)
##
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 <- "metainc"
##
if (sm != "IRD" & irscale != 1) {
warning("Argument 'irscale' only considered for ",
"incidence rate differences.",
call. = FALSE)
irscale <- 1
}
##
missing.method <- missing(method)
method <- setchar(method, gs("meth4inc"))
##
missing.method.incr <- missing(method.incr)
method.incr <- setchar(method.incr, gs("meth4incr"))
##
is.glmm <- method == "GLMM"
##
model.glmm <- setchar(model.glmm, c("UM.FS", "UM.RS", "CM.EL"))
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.e', 'time.e', 'event.c', 'time.c', 'n.e', and 'n.c'
## from data:
##
event.e <- catch("event.e", mc, data, sfsp)
chknull(event.e)
k.All <- length(event.e)
##
time.e <- catch("time.e", mc, data, sfsp)
chknull(time.e)
##
event.c <- catch("event.c", mc, data, sfsp)
chknull(event.c)
##
time.c <- catch("time.c", mc, data, sfsp)
chknull(time.c)
##
n.e <- catch("n.e", mc, data, sfsp)
null.n.e <- is.null(n.e)
##
n.c <- catch("n.c", mc, data, sfsp)
null.n.c <- is.null(n.c)
##
## 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(time.e, k.All, fun)
chklength(event.c, k.All, fun)
chklength(time.c, 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)
}
##
if (!null.n.e)
chklength(n.e, k.All, fun)
if (!is.null(n.c))
chklength(n.c, k.All, fun)
##
## 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.e = event.e)
else
data$.event.e <- event.e
##
data$.time.e <- time.e
data$.event.c <- event.c
data$.time.c <- time.c
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$.exclude <- exclude
##
if (!null.n.e)
data$.n.e <- n.e
if (!null.n.e)
data$.n.c <- n.c
}
##
##
## (6) Use subset for analysis
##
##
if (!missing.subset) {
event.e <- event.e[subset]
time.e <- time.e[subset]
event.c <- event.c[subset]
time.c <- time.c[subset]
studlab <- studlab[subset]
##
cluster <- cluster[subset]
exclude <- exclude[subset]
##
if (length(incr) > 1)
incr <- incr[subset]
##
if (by)
subgroup <- subgroup[subset]
##
if (!null.n.e)
n.e <- n.e[subset]
if (!is.null(n.c))
n.c <- n.c[subset]
}
##
## Determine total number of studies
##
k.all <- length(event.e)
##
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.e, 0)
chknumeric(time.e, 0, zero = TRUE)
chknumeric(event.c, 0)
chknumeric(time.c, zero = TRUE)
##
## Recode integer as numeric:
##
event.e <- int2num(event.e)
time.e <- int2num(time.e)
event.c <- int2num(event.c)
time.c <- int2num(time.c)
##
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,
IRD = event.e == 0 | event.c == 0,
IRR = event.e == 0 | event.c == 0,
VE = event.e == 0 | event.c == 0,
IRSD = event.e == 0 | event.c == 0)
##
## Sparse computation
##
sparse <- any(sel, na.rm = TRUE)
##
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 %in% c("IRR", "VE")) {
TE <- log(((event.e + incr.event) / time.e) /
((event.c + incr.event) / time.c))
seTE <- sqrt(1 / (event.e + incr.event) + 1 / (event.c + incr.event))
}
else if (sm == "IRD") {
TE <- event.e / time.e - event.c / time.c
seTE <- sqrt((event.e + incr.event) / time.e^2 +
(event.c + incr.event) / time.c^2)
}
else if (sm == "IRSD") {
TE <- sqrt(event.e / time.e) - sqrt(event.c / time.c)
seTE <- sqrt(0.25 / time.e + 0.25 / time.c)
}
##
##
## (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,
method, missing.method)
##
common <- FALSE
method <- "Inverse"
is.glmm <- FALSE
##
if (!(method.tau %in% c("REML", "ML")))
method.tau <- "REML"
}
##
##
## (9) Additional checks for GLMMs
##
##
if (is.glmm) {
chkglmm(sm, method.tau, method.random.ci, method.predict,
adhoc.hakn.ci, adhoc.hakn.pi,
c("IRR", "VE"))
##
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
}
##
if (sparse)
if ((!missing(incr) & any(incr != 0)) |
allincr | addincr)
warning("Note, for method = \"GLMM\", continuity correction only ",
"used to calculate individual study results.",
call. = FALSE)
}
##
##
## (10) Do meta-analysis
##
##
k <- sum(!is.na(event.e[!exclude]) & !is.na(event.c[!exclude]) &
!is.na(time.e[!exclude]) & !is.na(time.c[!exclude]))
##
for (i in seq_along(method.random.ci))
if (k == 1 & method.random.ci[i] == "HK")
method.random.ci[i] <- "classic"
##
if (method == "MH") {
##
## Greenland, Robins (1985)
##
x.k <- event.e
y.k <- event.c
n.k <- time.e
m.k <- time.c
##
N.k <- n.k + m.k
t.k <- x.k + y.k
##
if (sm %in% c("IRR", "VE")) {
D <- n.k * m.k * t.k / N.k^2
R <- x.k * m.k / N.k
S <- y.k * n.k / N.k
##
D[exclude] <- R[exclude] <- S[exclude] <- 0
##
w.common <- S
TE.common <- log(sum(R, na.rm = TRUE) / sum(S, na.rm = TRUE))
seTE.common <- sqrt(sum(D, na.rm = TRUE) / (sum(R, na.rm = TRUE) *
sum(S, na.rm = TRUE)))
}
else if (sm == "IRD") {
L <- (x.k * m.k^2 + y.k * n.k^2) / N.k^2
S <- n.k * m.k / N.k
##
L[exclude] <- S[exclude] <- 0
##
w.common <- S
TE.common <- weighted.mean(TE, w.common, na.rm = TRUE)
seTE.common <- sqrt(sum(L, na.rm = TRUE) / sum(S, na.rm = TRUE)^2)
}
}
##
else if (method == "Cochran") {
##
## Smoking and Health - Report of the Advisory Committee to the
## Surgeon General of the Public Health Service,
## Chapter 8
##
if (sm %in% c("IRR", "VE")) {
w.common <- event.c * time.e / time.c
w.common[exclude] <- 0
TE.common <- weighted.mean(TE, w.common)
seTE.common <- sqrt(1 / sum(event.e) + 1 / sum(event.c))
}
else if (sm == "IRD") {
warning("Cochran method only available for ",
"Incidence Rate Ratio (sm = \"IRR\") ",
"and Vaccine Efficacy / Effectiveness (sm = \"VE\")",
call. = FALSE)
return(NULL)
}
}
else if (is.glmm) {
list.inc <- list(x1i = event.e[!exclude], t1i = time.e[!exclude],
x2i = event.c[!exclude], t2i = time.c[!exclude],
measure = "IRR", model = model.glmm)
##
use.random <-
sum(!exclude) > 1 &
!((sum(event.e[!exclude], na.rm = TRUE) == 0 &
sum(event.c[!exclude], na.rm = TRUE) == 0) |
(!any(event.e[!exclude] != time.e[!exclude]) |
!any(event.c[!exclude] != time.c[!exclude])))
##
res.glmm <-
runGLMM(list.inc,
method.tau = method.tau,
method.random.ci = method.random.ci,
level = level.ma,
control = control, use.random = use.random)
##
TE.common <- as.numeric(res.glmm$glmm.common$b)
seTE.common <- as.numeric(res.glmm$glmm.common$se)
##
w.common <- rep(NA, length(event.e))
}
##
m <- metagen(TE, seTE, studlab,
exclude = if (missing.exclude) NULL else exclude,
cluster = cluster, rho = rho,
##
sm = sm,
level = level,
##
common = common,
random = random,
overall = overall,
overall.hetstat = overall.hetstat,
prediction = prediction,
##
method.tau = method.tau, method.tau.ci = method.tau.ci,
tau.preset = tau.preset,
TE.tau = TE.tau,
tau.common = FALSE,
##
level.ma = level.ma,
method.random.ci = method.random.ci,
adhoc.hakn.ci = adhoc.hakn.ci,
##
level.predict = level.predict,
method.predict = method.predict,
adhoc.hakn.pi = adhoc.hakn.pi,
seed.predict = seed.predict,
##
method.bias = method.bias,
##
backtransf = backtransf,
##
text.common = text.common, text.random = text.random,
text.predict = text.predict,
text.w.common = text.w.common, text.w.random = text.w.random,
##
title = title, complab = complab, outclab = outclab,
label.e = label.e, label.c = label.c,
label.left = label.left, label.right = label.right,
##
keepdata = FALSE,
warn = warn,
##
control = control)
##
if (by & tau.common & !is.glmm) {
## Estimate common tau-squared across subgroups
hcc <- hetcalc(TE, seTE, method.tau, "",
if (method == "Inverse") TE.tau else TE.common,
level.ma, subgroup, control)
}
##
##
## (9) Generate R object
##
##
res <- list(event.e = event.e, time.e = time.e,
event.c = event.c, time.c = time.c,
method = method, method.random = method,
incr = if (length(unique(incr)) == 1) unique(incr) else incr,
method.incr = method.incr,
sparse = sparse,
incr.event = incr.event,
k.MH = if (method == "MH") sum(w.common > 0) else NA)
##
## Add meta-analysis results
## (after removing unneeded list elements)
##
m$method <- NULL
m$method.random <- NULL
m$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
##
res <- c(res, m)
##
## Add data
##
res$n.e <- n.e
res$n.c <- n.c
res$TE.tau <- TE.tau
##
res$irscale <- irscale
res$irunit <- irunit
##
res$call <- match.call()
res$allincr <- allincr
res$addincr <- addincr
##
if (method %in% c("MH", "Cochran", "GLMM")) {
res <- ci2meta(res, ci.c = ci(TE.common, seTE.common, level = level.ma))
res$w.common <- w.common
}
##
if (is.glmm) {
res <- addGLMM(res, res.glmm)
res$model.glmm <- model.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.inc,
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)$glmm.random[[1]]
)
}
}
##
if (keepdata) {
res$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.w <- NULL
res$event.w <- NULL
##
if (null.n.e)
res$n.e.w <- NULL
if (null.n.c)
res$n.c.w <- NULL
##
res$n.harmonic.mean.w <- NULL
res$t.harmonic.mean.w <- NULL
##
res <- setNAwithin(res, res$three.level | is.glmm)
}
##
## Mantel-Haenszel and Cochran method are common effect methods
##
if (res$method.random %in% c("MH", "Cochran"))
res$method.random <- "Inverse"
##
## Backward compatibility
##
res <- backward(res)
##
class(res) <- c(fun, "meta")
res
}
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