R/indirect.R
In RichardBirnie/mautils: Utilities to facilitate an automated workflow for meta-analysis
Defines functions
runIndirect
.buildGraph
.getPaths
.extractPaths
bucher
doBucher
Documented in
bucher
doBucher
runIndirect
#' Run simple indirect meta-analysis for all possible pairwise comparisons
#' in a dataset
#'
#' @param df A \code{data.frame} This should be in one of two formats. Arm level
#' data must contain the columns 'study' and 'treatment' where study is a
#' study id number (1, 2, 3 ...) and treatment is a treatment id number. If
#' the data are binary then the data frame should also contain columns
#' 'responders' and 'sampleSize' reporting the total number of events and
#' total number analysed respectively for each arm. Relative effect data (e.g.
#' log odds ratio, log rate ratio) must contain the same study and treatment
#' columns plus the columns 'diff' and 'std.err'. Set diff=NA for the baseline
#' arm. The column std.err should be the standard error of the relative effect
#' estimate. For trials with more than two arms set std.err as the standard
#' error of the baseline arm. This determines the covariance which is used to
#' adjust for the correlation in multiarm studies. This is used to identify
#' the set of comparisons available in the data set
#' @param data_type A character string specifying which type of data has been
#' provided. Currently only 'treatment difference' or 'binary' are supported
#' @param direct_results A data frame containing the results of direct
#' meta-analysis. These results are required to provide the inputs for the
#' indirect comparisons. The best way to produce this is to use the tools for
#' direct meta-analysis from this package
#' @param continuous Logical (TRUE/FALSE) indicating whether the effect measure
#' is continuous (mean difference) or a ratio measure (odds ratio, hazard
#' ratio etc). This is passed directly to \code{doBucher} and then to
#' \code{bucher}
#' @param effect_type A character string indicating what kind of analysis is
#' required. Set to 'Fixed' for fixed effect, 'Random' for random effects or
#' 'all' to get both (Default).
#' @param back_calc A logical indicating whether results should be back transformed.
#' This is used to set the corresponding \code{backtransf} argument of the
#' underlying functions from the \code{meta} package. If
#' \code{backtransf=TRUE} then log odds ratios (or hazard ratios etc) will
#' be converted to odds ratios on plots and print outs. Default is FALSE
#' @param order_treatments An optional argument to specify the order in which
#' treatment comparisons are sorted in the output. The default is NA in
#' which case comparisons will be sorted alphabetically by intervention. If
#' a specific order is required then this should be provided as a data frame
#' with two columns named 'description' and 'Order'. Note that column
#' headers are specific and case sensitive. The description column should
#' contain the names of the treatments \emph{exactly} as they are specified
#' in the data set. The id column should contain the numbered order of
#' treatments required.
#'
#' @details This function performs indirect meta-analysis using the Bucher
#' method for all possible comparisons in a given data set. This function
#' takes a set of treatment comparisons from one or more studies and
#' identifies all possible indirect comparisons where two treatments can be
#' connected via a common comparator. If there is more than one way to
#' connect two treatments then all possible variations are calculated. This
#' function calls \code{doBucher} internally to calculate the
#' treatment effects
#'
#' The inputs for this function will usually be the results from direct
#' meta-analysis for a given set of treatments. The recommended workflow is
#' to use the tools from this package to perform head to head meta-analysis for
#' a given set of treatments then use the resulting data frame to provide
#' the inputs for this function.
#'
#' @return A data frame containing the results of all possible indirect
#' comparisons in the data set. The help page for \code{\link{doBucher}}
#' provides a detailed description of the columns in the output
#'
#' @seealso \code{\link{doBucher}}
#' @export
runIndirect = function(df, data_type, direct_results, continuous=FALSE,
effect_type = 'all', back_calc = FALSE,
order_treatments = NA) {
message('Run indirect (Bucher) meta-analysis')
#convert data to a suitable format
reDataDirect = formatDataToDirectMA(df, dataType = data_type)
indMA = doBucher(
comparisons = reDataDirect[,1:4], direct = direct_results,
effectType = effect_type, continuous = continuous,
backtransf = back_calc
)
if(is.data.frame(order_treatments)){
#set the order of results
indMA = dplyr::left_join(indMA, order_treatments, by = c('Intervention' = 'description'))
indMA = dplyr::arrange(indMA, Order, Comparator, Common, Model)
} else {
indMA = dplyr::arrange(indMA, Intervention, Model)
}
}
#'Create an igraph object
#' @param edgelist A two column matrix describing the connections _from_ the first
#' column _to_ the second column. The values are the treatment numbers
#' @param Logical. Describes whether all studies of the same treatments should be
#' shown as separate connections in the graph or collapsed into a single edge.
#' If there are 3 studies comparing the same treatments all=TRUE will show three
#' lines, all=FALSE will only show one(Default)
#' @param plotNetwork Logical indicating whether to draw a plot of the
#' network or not
#'
#' @return An igraph object
.buildGraph = function(edgelist, all = FALSE, plotNetwork = FALSE) {
#collapse the edges unless explicitly requested not to
if (all != TRUE) {
el = as.matrix(dplyr::distinct(edgelist))
} else {
el = as.matrix(edgelist)
}
g = igraph::graph_from_edgelist(el, directed = TRUE)
if (plotNetwork == TRUE) {
plot(g, layout = igraph::layout.fruchterman.reingold)
}
return(g)
}
#' Return all combinations of nodes with a set path length
#'
#' @param g An igraph object
#'
#' @details A function to search an igraph object and return
#' all combinations of nodes connected by a certain path
#' length. The default is to return all combinations of three
#' nodes that can be connected in exactly two steps.
#'
#' @return A data frame
#'
.getPaths = function(g){
nodes = igraph::V(g)
paths = lapply(nodes, .extractPaths, g=g)
paths = dplyr::bind_rows(paths) %>%
dplyr::arrange(from, to, via)
}
.extractPaths = function(g, start) {
paths = igraph::all_simple_paths(g, from = start, mode='all')
pathlength = sapply(paths, length)
paths = paths[pathlength == 3]
paths = lapply(paths, function(x){
x = as.numeric(x)
data.frame(from=x[1], to=x[3], via=x[2])
})
paths = dplyr::bind_rows(paths)
}
#' Simple indirect (Bucher) meta-analysis
#'
#' @param abTE Numeric. The treatment effect for a vs b. e.g. log OR, log HR,
#' mean difference
#' @param se.abTE Numeric. The standard error of the treatment effect for
#' a vs b, e.g. se of log OR
#' @param cbTE Numeric. The treatment effect for c vs b. e.g. log OR, log HR,
#' mean difference
#' @param se.cbTE Numeric. The standard error of the treatment effect for c vs
#' b, e.g. se of log OR
#' @param effect Character string describing the effect measure, e.g. 'Rate
#' Ratio' or 'log Odds Ratio'
#' @param model Character string indicating whether abTE and cbTE come from a
#' fixed effect model or a random effect model
#' @param continuous Logical (TRUE/FALSE) indicating whether the effect measure
#' is continuous (mean difference) or a ratio measure (odds ratio, hazard
#' ratio etc).
#' @param intervention Character string. Name of the intervention treatment
#' @param comparator Character string. Name of the comparator treatment
#' @param common Character string. Name of the common comparator that links the
#' indirect comparison, e.g. placebo
#' @param backtransf Logical indicating whether the results should be
#' exponentiated or not. If abTE and cbTE are on the log scale (e.g. log
#' hazard ratio) set this to TRUE to return the exponentiated results (e.g.
#' hazard ratio). If TRUE this will return both the log estimates and the
#' exponentiated estimates
#' @param ab.studies Character string giving the names of studies in the A vs B
#' comparison separated by commas
#' @param cb.studies Character string giving the names of studies in the C vs B
#' comparison separated by commas
#'
#' @details Calculate an indirect estimate of the relative effect of two
#' treatments using the Bucher method
#'
#' The inputs are the relative treatment effects for two pairs of treatments
#' linked by a common comparator. For example, if you have the relative
#' effects (e.g. log hazard ratio) for treatment A vs placebo and treatment C
#' vs placebo then this function will return the relative effect of treatment
#' A compared to treatment C.
#'
#' This function is mainly intended to be called from \code{\link{doBucher}} but can
#' be used directly if required.
#'
#' \code{effect}, \code{model}, \code{intervention}, \code{comparator} and
#' \code{common} are used for labels only. The results depend only on
#' \code{abTE}, \code{cbTE} and the respective standard errors.
#'
#' @return A data frame with the following columns:
#' \itemize{
#' \item \code{Intervention} The name of the intervention
#' \item \code{Comparator} The name of the comparator
#' \item \code{Common} The name of the common treatment linking intervention
#' and comparator
#' \item \code{Effect} The type of effect measure. Takes the
#' value of the \code{effect} argument
#' \item \code{Model} The type of model. Should be 'Fixed', 'Random' or NA
#' \item \code{log.TE.ind} The treatment effect on log scale, e.g. log OR
#' \item \code{log.lower.ind}, \code{log.upper.ind} The upper and lower 95\% confidence
#' intervals for the log treatment effect
#' \item \code{se.log.TE.ind} The standard error for the log treatment effect
#' \item \code{TE.ind}, \code{lower.ind}, \code{upper.ind} The treatment effect with lower
#' and upper confidence intervals backtransformed to a linear scale
#' \item \code{n.studies} The number of studies included in the analysis
#' \item \code{Studies} The names of the studies included in the analysis
#' }
#'
#' @seealso \code{\link{doBucher}}
#' @export
bucher = function(abTE, se.abTE, cbTE, se.cbTE, effect, model, continuous,
intervention, comparator, common, backtransf = FALSE,
ab.studies, cb.studies) {
acTE = abTE - cbTE #indirect treatment effect
se.acTE = sqrt(se.abTE ^ 2 + se.cbTE ^ 2) #standard error - sqrt(sum of the two variances)
lower = acTE - (1.96 * se.acTE) #calculate confidence intervals
upper = acTE + (1.96 * se.acTE)
if(!continuous) {
#if the effect measure is not mean difference then results will be on a log scale
#set column names accordingly
df = data.frame(
'log.TE.ind' = acTE, 'log.lower.ind' = lower, 'log.upper.ind' = upper,
'se.log.TE.ind' = se.acTE
)
} else {
#if the effect measure is a mean difference then results will be on a linear scale
df = data.frame(
'TE.ind' = acTE, 'lower.ind' = lower, 'upper.ind' = upper,
'se.TE.ind' = se.acTE
)
}
#exponentiate if required
if (backtransf == TRUE) {
eTE = exp(df[,1:3])
colnames(eTE) = gsub('log.', '', colnames(eTE), fixed = TRUE)
df = cbind(df, eTE)
}
#identify and count the studies involved
ab.studies = strsplit(as.character(ab.studies), split = ', ')[[1]]
cb.studies = strsplit(as.character(cb.studies), split = ', ')[[1]]
studies = c(ab.studies,cb.studies)
studies = unique(studies)
n.studies = length(studies)
studies = paste0(studies, collapse = '; ')
#tag on some labels
df = data.frame(
'Intervention' = intervention, 'Comparator' = comparator, 'Common' = common,
'Effect' = effect, 'Model' = model, df, 'n.studies' = n.studies,
'Studies' = studies
)
}
#' Perform all possible indirect comparisons for a given data set
#'
#' @param comparisons A data frame with four columns: StudyName, study,
#' comparator, treatment. Describes the treatment comparisons present in the
#' dataset. study, comparator and treatment must be numbers. For example,
#' study = 4, comparator=1, treatment=2 represents the comparison of treatment
#' 2 vs treatment 1 in study 4
#' @param direct A data frame containing the results of direct head-to-head
#' meta-analysis for the treatment comparisons of interest if only one study is
#' available for a given comparison then the result of that study should be
#' used. This data frame can be created by using \code{doDirectMeta} and
#' \code{extractDirectRes} in that order
#' @param effectType Character string indicating what type of results are
#' required. Default is 'all' which will return both fixed effect and random
#' effect results. Alternatives are 'Fixed' or 'Random' (Case sensitive) if
#' only one set of results is required
#' @param continuous Logical (TRUE/FALSE) indicating whether the effect measure
#' is continuous (mean difference) or a ratio measure (odds ratio, hazard
#' ratio etc)
#' @param backtransf Logical indicating whether the results should be
#' exponentiated or not. If abTE and cbTE are on the log scale (e.g. log
#' hazard ratio) set this to TRUE to return the exponentiated results (e.g.
#' hazard ratio). If TRUE this will return both the log estimates and the
#' exponentiated estimates
#'
#' @details This function performs indirect meta-analysis for all possible
#' comparisons in a given data set. This function takes a set of treatment
#' comparisons from one or more studies and identifies all possible indirect
#' comparisons where two treatments can be connected via a common comparator.
#' If there is more than one way to connect two treatments then all possible
#' variations are calculated. This function calls \code{\link{bucher}} internally
#' to calculate the treatment effects
#'
#' The inputs for this function will usually be the results from direct
#' meta-analysis for a given set of treatments. The recommended workflow is
#' to use \code{\link{doDirectMeta}} to perform head to head meta-analysis for
#' a given set of treatments, extract the results as a data frame using
#' \code{\link{extractDirectRes}} then use that data frame to provide the inputs
#' for this function.
#'
#' @return A data frame with the following columns:
#' \itemize{
#' \item \code{Intervention} The name of the intervention
#' \item \code{Comparator} The name of the comparator
#' \item \code{Common} The name of the common treatment linking intervention
#' and comparator
#' \item \code{Effect} The type of effect measure. Takes the
#' value of the \code{effect} argument
#' \item \code{Model} The type of model. Fixed effect or Random Effects.
#' \item \code{log.TE.ind} The treatment effect on log scale, e.g. log OR
#' \item \code{log.lower.ind}, \code{log.upper.ind} The upper and lower 95\% confidence
#' intervals for the log treatment effect
#' \item \code{se.log.TE.ind} The standard error for the log treatment effect
#' \item \code{TE.ind}, \code{lower.ind}, \code{upper.ind} The treatment effect with lower
#' and upper confidence intervals backtransformed to a linear scale
#' \item \code{n.studies} The number of studies included in the analysis
#' \item \code{Studies} The names of the studies included in the analysis
#' }
#' @seealso \code{\link{bucher}}, \code{\link{doDirectMeta}},
#' \code{\link{extractDirectRes}}
#' @export
doBucher = function(comparisons, direct, effectType = 'all',
continuous, backtransf = FALSE) {
#get all the pairs of comparisons
connections = dplyr::select(comparisons, from = treatment, to = comparator)
#build a graph
g = .buildGraph(connections)
#get the set of possible Bucher comparisons
#all possible paths with exactly 3 nodes
bucherSet = .getPaths(g)
for (i in 1:nrow(bucherSet)) {
triplet = bucherSet[i,]
#identify the corresponding comparisons in the direct results
#get the data related to the current indirect comparison
currComp = dplyr::filter(direct,
(direct$InterventionCode %in% triplet & direct$ComparatorCode %in% triplet)
)
#check for the direct comparison and remove it
removeDirect = !(
(currComp$InterventionCode == triplet$from & currComp$ComparatorCode == triplet$to) |
(currComp$InterventionCode == triplet$to & currComp$ComparatorCode == triplet$from)
)
indAll = currComp[removeDirect,]
#control which set of results we want. Default is all
if(!all(is.na(indAll$Model))){
if (effectType == 'all') {
mod = c('Fixed', 'Random')
} else {
mod = effectType
}
} else {
mod = NA
}
for (k in 1:length(mod)) {
#get the data for the preferred model for current indirect comparison
#Fixed Effect or Random effect
indComp = dplyr::filter(indAll, (Model == mod[k] | is.na(Model)) )
#if the common treatment is the intervention rather than the comparator
#then we need to invert the treatment effect
ix = indComp$InterventionCode == triplet$via
if (any(ix) == TRUE) {
#reverse the treatment codes
indComp[ix, c('InterventionCode', 'ComparatorCode')] = indComp[ix, c('ComparatorCode', 'InterventionCode')]
indComp[ix, c('Intervention', 'Comparator')] = indComp[ix, c('Comparator', 'Intervention')]
if(continuous) {
#invert treatment effect for continuous data
#linear scale
indComp$TE[ix] =-indComp$TE[ix]
} else {
#invert treatment effect for other effect measures
#log scale
indComp$log.TE[ix] =-indComp$log.TE[ix]
}
}
#run the comparison
if(continuous) {
abTE = indComp$TE[indComp$InterventionCode == triplet$from]
se.abTE = indComp$seTE[indComp$InterventionCode == triplet$from]
cbTE = indComp$TE[indComp$InterventionCode == triplet$to]
se.cbTE = indComp$seTE[indComp$InterventionCode == triplet$to]
} else {
abTE = indComp$log.TE[indComp$InterventionCode == triplet$from]
se.abTE = indComp$seTE.log[indComp$InterventionCode == triplet$from]
cbTE = indComp$log.TE[indComp$InterventionCode == triplet$to]
se.cbTE = indComp$seTE.log[indComp$InterventionCode == triplet$to]
}
e = indComp$Effect[1]
int = indComp$Intervention[indComp$InterventionCode == triplet$from]
com = indComp$Intervention[indComp$InterventionCode == triplet$to]
common = indComp$Comparator[indComp$ComparatorCode == triplet$via][1]
indMA = bucher(
abTE = abTE, se.abTE = se.abTE,
cbTE = cbTE, se.cbTE = se.cbTE,
backtransf = backtransf, effect = e,
model = mod[k], intervention = int,
comparator = com, common = common,
ab.studies = indComp$studies[1],
cb.studies = indComp$studies[2],
continuous = continuous
)
#collect FE and RE results
if (k == 1) {
fere = indMA
} else {
fere = rbind(fere, indMA)
}
} #end k
if (i == 1) {
df = fere
} else {
df = rbind(df, fere)
}
} #end i
df = dplyr::arrange(df, dplyr::desc(Intervention), dplyr::desc(Comparator))
#fix variable types
df = factorToCharacter(df)
#rearrange the columns into a more report friendly format
#depending on the effect measure a different set of columns
#will be present
if(!continuous & backtransf == TRUE) {
#if the effect measure is not a mean difference and backtransf is TRUE
#then the results table will include the log effect estimates
df = dplyr::select(
df, Effect, Intervention, Comparator, Common, TE.ind, lower.ind, upper.ind,
n.studies, Studies, Model, log.TE.ind, log.lower.ind, log.upper.ind,
se.log.TE.ind
)
} else {
#if the effect_measure is a mean difference then there will be no log estimates
df = dplyr::select(
df, Effect, Intervention, Comparator, Common, TE.ind, lower.ind, upper.ind,
n.studies, Studies, Model
)
}
}
RichardBirnie/mautils documentation built on July 12, 2019, 8:56 p.m.
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Defines functions runIndirect .buildGraph .getPaths .extractPaths bucher doBucher
Documented in bucher doBucher runIndirect
#' Run simple indirect meta-analysis for all possible pairwise comparisons
#' in a dataset
#'
#' @param df A \code{data.frame} This should be in one of two formats. Arm level
#' data must contain the columns 'study' and 'treatment' where study is a
#' study id number (1, 2, 3 ...) and treatment is a treatment id number. If
#' the data are binary then the data frame should also contain columns
#' 'responders' and 'sampleSize' reporting the total number of events and
#' total number analysed respectively for each arm. Relative effect data (e.g.
#' log odds ratio, log rate ratio) must contain the same study and treatment
#' columns plus the columns 'diff' and 'std.err'. Set diff=NA for the baseline
#' arm. The column std.err should be the standard error of the relative effect
#' estimate. For trials with more than two arms set std.err as the standard
#' error of the baseline arm. This determines the covariance which is used to
#' adjust for the correlation in multiarm studies. This is used to identify
#' the set of comparisons available in the data set
#' @param data_type A character string specifying which type of data has been
#' provided. Currently only 'treatment difference' or 'binary' are supported
#' @param direct_results A data frame containing the results of direct
#' meta-analysis. These results are required to provide the inputs for the
#' indirect comparisons. The best way to produce this is to use the tools for
#' direct meta-analysis from this package
#' @param continuous Logical (TRUE/FALSE) indicating whether the effect measure
#' is continuous (mean difference) or a ratio measure (odds ratio, hazard
#' ratio etc). This is passed directly to \code{doBucher} and then to
#' \code{bucher}
#' @param effect_type A character string indicating what kind of analysis is
#' required. Set to 'Fixed' for fixed effect, 'Random' for random effects or
#' 'all' to get both (Default).
#' @param back_calc A logical indicating whether results should be back transformed.
#' This is used to set the corresponding \code{backtransf} argument of the
#' underlying functions from the \code{meta} package. If
#' \code{backtransf=TRUE} then log odds ratios (or hazard ratios etc) will
#' be converted to odds ratios on plots and print outs. Default is FALSE
#' @param order_treatments An optional argument to specify the order in which
#' treatment comparisons are sorted in the output. The default is NA in
#' which case comparisons will be sorted alphabetically by intervention. If
#' a specific order is required then this should be provided as a data frame
#' with two columns named 'description' and 'Order'. Note that column
#' headers are specific and case sensitive. The description column should
#' contain the names of the treatments \emph{exactly} as they are specified
#' in the data set. The id column should contain the numbered order of
#' treatments required.
#'
#' @details This function performs indirect meta-analysis using the Bucher
#' method for all possible comparisons in a given data set. This function
#' takes a set of treatment comparisons from one or more studies and
#' identifies all possible indirect comparisons where two treatments can be
#' connected via a common comparator. If there is more than one way to
#' connect two treatments then all possible variations are calculated. This
#' function calls \code{doBucher} internally to calculate the
#' treatment effects
#'
#' The inputs for this function will usually be the results from direct
#' meta-analysis for a given set of treatments. The recommended workflow is
#' to use the tools from this package to perform head to head meta-analysis for
#' a given set of treatments then use the resulting data frame to provide
#' the inputs for this function.
#'
#' @return A data frame containing the results of all possible indirect
#' comparisons in the data set. The help page for \code{\link{doBucher}}
#' provides a detailed description of the columns in the output
#'
#' @seealso \code{\link{doBucher}}
#' @export
runIndirect = function(df, data_type, direct_results, continuous=FALSE,
effect_type = 'all', back_calc = FALSE,
order_treatments = NA) {
message('Run indirect (Bucher) meta-analysis')
#convert data to a suitable format
reDataDirect = formatDataToDirectMA(df, dataType = data_type)
indMA = doBucher(
comparisons = reDataDirect[,1:4], direct = direct_results,
effectType = effect_type, continuous = continuous,
backtransf = back_calc
)
if(is.data.frame(order_treatments)){
#set the order of results
indMA = dplyr::left_join(indMA, order_treatments, by = c('Intervention' = 'description'))
indMA = dplyr::arrange(indMA, Order, Comparator, Common, Model)
} else {
indMA = dplyr::arrange(indMA, Intervention, Model)
}
}
#'Create an igraph object
#' @param edgelist A two column matrix describing the connections _from_ the first
#' column _to_ the second column. The values are the treatment numbers
#' @param Logical. Describes whether all studies of the same treatments should be
#' shown as separate connections in the graph or collapsed into a single edge.
#' If there are 3 studies comparing the same treatments all=TRUE will show three
#' lines, all=FALSE will only show one(Default)
#' @param plotNetwork Logical indicating whether to draw a plot of the
#' network or not
#'
#' @return An igraph object
.buildGraph = function(edgelist, all = FALSE, plotNetwork = FALSE) {
#collapse the edges unless explicitly requested not to
if (all != TRUE) {
el = as.matrix(dplyr::distinct(edgelist))
} else {
el = as.matrix(edgelist)
}
g = igraph::graph_from_edgelist(el, directed = TRUE)
if (plotNetwork == TRUE) {
plot(g, layout = igraph::layout.fruchterman.reingold)
}
return(g)
}
#' Return all combinations of nodes with a set path length
#'
#' @param g An igraph object
#'
#' @details A function to search an igraph object and return
#' all combinations of nodes connected by a certain path
#' length. The default is to return all combinations of three
#' nodes that can be connected in exactly two steps.
#'
#' @return A data frame
#'
.getPaths = function(g){
nodes = igraph::V(g)
paths = lapply(nodes, .extractPaths, g=g)
paths = dplyr::bind_rows(paths) %>%
dplyr::arrange(from, to, via)
}
.extractPaths = function(g, start) {
paths = igraph::all_simple_paths(g, from = start, mode='all')
pathlength = sapply(paths, length)
paths = paths[pathlength == 3]
paths = lapply(paths, function(x){
x = as.numeric(x)
data.frame(from=x[1], to=x[3], via=x[2])
})
paths = dplyr::bind_rows(paths)
}
#' Simple indirect (Bucher) meta-analysis
#'
#' @param abTE Numeric. The treatment effect for a vs b. e.g. log OR, log HR,
#' mean difference
#' @param se.abTE Numeric. The standard error of the treatment effect for
#' a vs b, e.g. se of log OR
#' @param cbTE Numeric. The treatment effect for c vs b. e.g. log OR, log HR,
#' mean difference
#' @param se.cbTE Numeric. The standard error of the treatment effect for c vs
#' b, e.g. se of log OR
#' @param effect Character string describing the effect measure, e.g. 'Rate
#' Ratio' or 'log Odds Ratio'
#' @param model Character string indicating whether abTE and cbTE come from a
#' fixed effect model or a random effect model
#' @param continuous Logical (TRUE/FALSE) indicating whether the effect measure
#' is continuous (mean difference) or a ratio measure (odds ratio, hazard
#' ratio etc).
#' @param intervention Character string. Name of the intervention treatment
#' @param comparator Character string. Name of the comparator treatment
#' @param common Character string. Name of the common comparator that links the
#' indirect comparison, e.g. placebo
#' @param backtransf Logical indicating whether the results should be
#' exponentiated or not. If abTE and cbTE are on the log scale (e.g. log
#' hazard ratio) set this to TRUE to return the exponentiated results (e.g.
#' hazard ratio). If TRUE this will return both the log estimates and the
#' exponentiated estimates
#' @param ab.studies Character string giving the names of studies in the A vs B
#' comparison separated by commas
#' @param cb.studies Character string giving the names of studies in the C vs B
#' comparison separated by commas
#'
#' @details Calculate an indirect estimate of the relative effect of two
#' treatments using the Bucher method
#'
#' The inputs are the relative treatment effects for two pairs of treatments
#' linked by a common comparator. For example, if you have the relative
#' effects (e.g. log hazard ratio) for treatment A vs placebo and treatment C
#' vs placebo then this function will return the relative effect of treatment
#' A compared to treatment C.
#'
#' This function is mainly intended to be called from \code{\link{doBucher}} but can
#' be used directly if required.
#'
#' \code{effect}, \code{model}, \code{intervention}, \code{comparator} and
#' \code{common} are used for labels only. The results depend only on
#' \code{abTE}, \code{cbTE} and the respective standard errors.
#'
#' @return A data frame with the following columns:
#' \itemize{
#' \item \code{Intervention} The name of the intervention
#' \item \code{Comparator} The name of the comparator
#' \item \code{Common} The name of the common treatment linking intervention
#' and comparator
#' \item \code{Effect} The type of effect measure. Takes the
#' value of the \code{effect} argument
#' \item \code{Model} The type of model. Should be 'Fixed', 'Random' or NA
#' \item \code{log.TE.ind} The treatment effect on log scale, e.g. log OR
#' \item \code{log.lower.ind}, \code{log.upper.ind} The upper and lower 95\% confidence
#' intervals for the log treatment effect
#' \item \code{se.log.TE.ind} The standard error for the log treatment effect
#' \item \code{TE.ind}, \code{lower.ind}, \code{upper.ind} The treatment effect with lower
#' and upper confidence intervals backtransformed to a linear scale
#' \item \code{n.studies} The number of studies included in the analysis
#' \item \code{Studies} The names of the studies included in the analysis
#' }
#'
#' @seealso \code{\link{doBucher}}
#' @export
bucher = function(abTE, se.abTE, cbTE, se.cbTE, effect, model, continuous,
intervention, comparator, common, backtransf = FALSE,
ab.studies, cb.studies) {
acTE = abTE - cbTE #indirect treatment effect
se.acTE = sqrt(se.abTE ^ 2 + se.cbTE ^ 2) #standard error - sqrt(sum of the two variances)
lower = acTE - (1.96 * se.acTE) #calculate confidence intervals
upper = acTE + (1.96 * se.acTE)
if(!continuous) {
#if the effect measure is not mean difference then results will be on a log scale
#set column names accordingly
df = data.frame(
'log.TE.ind' = acTE, 'log.lower.ind' = lower, 'log.upper.ind' = upper,
'se.log.TE.ind' = se.acTE
)
} else {
#if the effect measure is a mean difference then results will be on a linear scale
df = data.frame(
'TE.ind' = acTE, 'lower.ind' = lower, 'upper.ind' = upper,
'se.TE.ind' = se.acTE
)
}
#exponentiate if required
if (backtransf == TRUE) {
eTE = exp(df[,1:3])
colnames(eTE) = gsub('log.', '', colnames(eTE), fixed = TRUE)
df = cbind(df, eTE)
}
#identify and count the studies involved
ab.studies = strsplit(as.character(ab.studies), split = ', ')[[1]]
cb.studies = strsplit(as.character(cb.studies), split = ', ')[[1]]
studies = c(ab.studies,cb.studies)
studies = unique(studies)
n.studies = length(studies)
studies = paste0(studies, collapse = '; ')
#tag on some labels
df = data.frame(
'Intervention' = intervention, 'Comparator' = comparator, 'Common' = common,
'Effect' = effect, 'Model' = model, df, 'n.studies' = n.studies,
'Studies' = studies
)
}
#' Perform all possible indirect comparisons for a given data set
#'
#' @param comparisons A data frame with four columns: StudyName, study,
#' comparator, treatment. Describes the treatment comparisons present in the
#' dataset. study, comparator and treatment must be numbers. For example,
#' study = 4, comparator=1, treatment=2 represents the comparison of treatment
#' 2 vs treatment 1 in study 4
#' @param direct A data frame containing the results of direct head-to-head
#' meta-analysis for the treatment comparisons of interest if only one study is
#' available for a given comparison then the result of that study should be
#' used. This data frame can be created by using \code{doDirectMeta} and
#' \code{extractDirectRes} in that order
#' @param effectType Character string indicating what type of results are
#' required. Default is 'all' which will return both fixed effect and random
#' effect results. Alternatives are 'Fixed' or 'Random' (Case sensitive) if
#' only one set of results is required
#' @param continuous Logical (TRUE/FALSE) indicating whether the effect measure
#' is continuous (mean difference) or a ratio measure (odds ratio, hazard
#' ratio etc)
#' @param backtransf Logical indicating whether the results should be
#' exponentiated or not. If abTE and cbTE are on the log scale (e.g. log
#' hazard ratio) set this to TRUE to return the exponentiated results (e.g.
#' hazard ratio). If TRUE this will return both the log estimates and the
#' exponentiated estimates
#'
#' @details This function performs indirect meta-analysis for all possible
#' comparisons in a given data set. This function takes a set of treatment
#' comparisons from one or more studies and identifies all possible indirect
#' comparisons where two treatments can be connected via a common comparator.
#' If there is more than one way to connect two treatments then all possible
#' variations are calculated. This function calls \code{\link{bucher}} internally
#' to calculate the treatment effects
#'
#' The inputs for this function will usually be the results from direct
#' meta-analysis for a given set of treatments. The recommended workflow is
#' to use \code{\link{doDirectMeta}} to perform head to head meta-analysis for
#' a given set of treatments, extract the results as a data frame using
#' \code{\link{extractDirectRes}} then use that data frame to provide the inputs
#' for this function.
#'
#' @return A data frame with the following columns:
#' \itemize{
#' \item \code{Intervention} The name of the intervention
#' \item \code{Comparator} The name of the comparator
#' \item \code{Common} The name of the common treatment linking intervention
#' and comparator
#' \item \code{Effect} The type of effect measure. Takes the
#' value of the \code{effect} argument
#' \item \code{Model} The type of model. Fixed effect or Random Effects.
#' \item \code{log.TE.ind} The treatment effect on log scale, e.g. log OR
#' \item \code{log.lower.ind}, \code{log.upper.ind} The upper and lower 95\% confidence
#' intervals for the log treatment effect
#' \item \code{se.log.TE.ind} The standard error for the log treatment effect
#' \item \code{TE.ind}, \code{lower.ind}, \code{upper.ind} The treatment effect with lower
#' and upper confidence intervals backtransformed to a linear scale
#' \item \code{n.studies} The number of studies included in the analysis
#' \item \code{Studies} The names of the studies included in the analysis
#' }
#' @seealso \code{\link{bucher}}, \code{\link{doDirectMeta}},
#' \code{\link{extractDirectRes}}
#' @export
doBucher = function(comparisons, direct, effectType = 'all',
continuous, backtransf = FALSE) {
#get all the pairs of comparisons
connections = dplyr::select(comparisons, from = treatment, to = comparator)
#build a graph
g = .buildGraph(connections)
#get the set of possible Bucher comparisons
#all possible paths with exactly 3 nodes
bucherSet = .getPaths(g)
for (i in 1:nrow(bucherSet)) {
triplet = bucherSet[i,]
#identify the corresponding comparisons in the direct results
#get the data related to the current indirect comparison
currComp = dplyr::filter(direct,
(direct$InterventionCode %in% triplet & direct$ComparatorCode %in% triplet)
)
#check for the direct comparison and remove it
removeDirect = !(
(currComp$InterventionCode == triplet$from & currComp$ComparatorCode == triplet$to) |
(currComp$InterventionCode == triplet$to & currComp$ComparatorCode == triplet$from)
)
indAll = currComp[removeDirect,]
#control which set of results we want. Default is all
if(!all(is.na(indAll$Model))){
if (effectType == 'all') {
mod = c('Fixed', 'Random')
} else {
mod = effectType
}
} else {
mod = NA
}
for (k in 1:length(mod)) {
#get the data for the preferred model for current indirect comparison
#Fixed Effect or Random effect
indComp = dplyr::filter(indAll, (Model == mod[k] | is.na(Model)) )
#if the common treatment is the intervention rather than the comparator
#then we need to invert the treatment effect
ix = indComp$InterventionCode == triplet$via
if (any(ix) == TRUE) {
#reverse the treatment codes
indComp[ix, c('InterventionCode', 'ComparatorCode')] = indComp[ix, c('ComparatorCode', 'InterventionCode')]
indComp[ix, c('Intervention', 'Comparator')] = indComp[ix, c('Comparator', 'Intervention')]
if(continuous) {
#invert treatment effect for continuous data
#linear scale
indComp$TE[ix] =-indComp$TE[ix]
} else {
#invert treatment effect for other effect measures
#log scale
indComp$log.TE[ix] =-indComp$log.TE[ix]
}
}
#run the comparison
if(continuous) {
abTE = indComp$TE[indComp$InterventionCode == triplet$from]
se.abTE = indComp$seTE[indComp$InterventionCode == triplet$from]
cbTE = indComp$TE[indComp$InterventionCode == triplet$to]
se.cbTE = indComp$seTE[indComp$InterventionCode == triplet$to]
} else {
abTE = indComp$log.TE[indComp$InterventionCode == triplet$from]
se.abTE = indComp$seTE.log[indComp$InterventionCode == triplet$from]
cbTE = indComp$log.TE[indComp$InterventionCode == triplet$to]
se.cbTE = indComp$seTE.log[indComp$InterventionCode == triplet$to]
}
e = indComp$Effect[1]
int = indComp$Intervention[indComp$InterventionCode == triplet$from]
com = indComp$Intervention[indComp$InterventionCode == triplet$to]
common = indComp$Comparator[indComp$ComparatorCode == triplet$via][1]
indMA = bucher(
abTE = abTE, se.abTE = se.abTE,
cbTE = cbTE, se.cbTE = se.cbTE,
backtransf = backtransf, effect = e,
model = mod[k], intervention = int,
comparator = com, common = common,
ab.studies = indComp$studies[1],
cb.studies = indComp$studies[2],
continuous = continuous
)
#collect FE and RE results
if (k == 1) {
fere = indMA
} else {
fere = rbind(fere, indMA)
}
} #end k
if (i == 1) {
df = fere
} else {
df = rbind(df, fere)
}
} #end i
df = dplyr::arrange(df, dplyr::desc(Intervention), dplyr::desc(Comparator))
#fix variable types
df = factorToCharacter(df)
#rearrange the columns into a more report friendly format
#depending on the effect measure a different set of columns
#will be present
if(!continuous & backtransf == TRUE) {
#if the effect measure is not a mean difference and backtransf is TRUE
#then the results table will include the log effect estimates
df = dplyr::select(
df, Effect, Intervention, Comparator, Common, TE.ind, lower.ind, upper.ind,
n.studies, Studies, Model, log.TE.ind, log.lower.ind, log.upper.ind,
se.log.TE.ind
)
} else {
#if the effect_measure is a mean difference then there will be no log estimates
df = dplyr::select(
df, Effect, Intervention, Comparator, Common, TE.ind, lower.ind, upper.ind,
n.studies, Studies, Model
)
}
}
R Package Documentation
Browse R Packages
We want your feedback!
Note that we can't provide technical support on individual packages. You should contact the package authors for that.
Embedding an R snippet on your website
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.