Create a summary risk of bias plot

If the rob.data argument has been specified, this function allows to create a summary risk of bias plot for results of the runMetaAnalysis() function.

Usage

createRobSummary(model, 
                 name.low, 
                 name.high, 
                 name.unclear, 
                 which.run = model$which.run[1])

Arguments

model

An object of class runMetaAnalysis, created by the runMetaAnalysis() function.

name.low

A character vector, specifying which code(s) have been used in the original data for studies with a low risk of bias.

name.high

A character vector, specifying which code(s) have been used in the original data for studies with a high risk of bias.

name.unclear

A character vector, specifying which code(s) have been used in the original data for studies with unclear risk of bias.

which.run

The model in model that should be used for the summary risk of bias plot. Uses the default analysis in model if no value is specified by the user. Possible values are "overall", "combined", "lowest", "highest", "outliers", "influence" and "rob".

Value

Creates a RevMan-type risk of bias summary plot.

See also

dmetar::rob.summary, robvis::rob_summary, meta::rob

Author

Mathias Harrer mathias.h.harrer@gmail.com, Paula Kuper paula.r.kuper@gmail.com, Pim Cuijpers p.cuijpers@vu.nl

Examples

if (FALSE) {


# Define ROB data to be added to the models
robData = list(
  # Names of ROB variables included in 'data'
  domains = c("sg", "ac", "ba", "itt"),
  # Long-format labels for each ROB domain
  domain.names = c("Sequence Generation", 
                   "Allocation Concealment", 
                   "Blinding of Assessors", 
                   "ITT Analyses"),
  # Codes used to rate the risk of bias (sr=self-report)
  categories = c("0", "1", "sr"),
  # Symbols that should be used for these codes in forest plots
  symbols = c("-", "+", "s"),
  # Colors to be used in forest plots for each of these codes
  colors = c("red", "green", "yellow"))

# Run meta-analyses with ROB data
res <- depressionPsyCtr %>% 
  filterPoolingData(condition_arm1 %in% c("cbt", "pst", "3rd")) %>% 
  runMetaAnalysis(rob.data = robData)

# Create a summary plot
createRobSummary(res, 
                 name.low = "1", 
                 name.high = "0", 
                 name.unclear = "sr")

# Create a summary plot for the "combined" model
# - Recode 'sr' (self-report) as low risk of bias
createRobSummary(res, 
                 name.low = c("1", "sr"), 
                 name.high = "0", 
                 name.unclear = NULL,
                 which.run = "combined")
                 
                 
}