Create a RevMan-style risk of bias summary chart

This function generates summary plots for study quality assessments using the Cochrance Risk of Bias Tool. Summary plots follow the style of RevMan Risk of Bias (RoB) summary charts.

rob.summary(data, name.high="High", name.unclear="Unclear",
    name.low="Low", studies, name.missing, table = FALSE)

Arguments

data	A data.frame containing a column for each risk of bias criterion, where rows represent each individual studies. The risk of bias assessment for each criterion in each study must be coded as a character string. Up to four codes can be used, referring to low risk of bias, unclear risk of bias, high risk of bias, or missing information. The string used to specify the categories must be specified in name.high, name.unclear, name.low and/or name.missing, unless defaults for those parameters are used.
name.high	Character specifying how the "high risk of bias" category was coded in data (e.g., name.high = "high"). Default is "High".
name.unclear	Character specifying how the "unclear risk of bias" category was coded in data (e.g., name.unclear = "unclear"). Default is "Unclear".
name.low	Character specifying how the "low risk of bias" category was coded in data (e.g., name.low = "low"). Default is "Low".
studies	A vector of the same length as the number of rows in data specifying the study labels for the risk of bias ratings. Only has to be specified when table = TRUE.
name.missing	Character specifying how missing information was coded in data (e.g., name.missing = "missing"). Default is "Missing". All ratings, including missing information, must be coded as strings, so using NA in data to signify missing information is not valid.
table	Should an additional RevMan style risk of bias table be produced? If set to TRUE, studies must be specified. FALSE by default.

Details

The function automatically removes separators like "-" or "." from column names/risk of bias criteria. To produce a "clean" plot, you may therefore separate words in the column names of the data data frame using these symbols (e.g. "Allocation_Concealment" to return "Allocation Concealment").

References

Harrer, M., Cuijpers, P., Furukawa, T.A, & Ebert, D. D. (2019). Doing Meta-Analysis in R: A Hands-on Guide. DOI: 10.5281/zenodo.2551803. Chapter 10

See also

direct.evidence.plot

Examples

# Example 1: No missing information, only produce summary plot
data = data.frame(
       "study" = c("Higgins et al., 2011", "Borenstein et al., 2008", "Holm, 1971",
                   "Zajonc et al., 2005", "Viechtbauer, 2014"),
       "Allocation_concealment" = c("Low", "High", "High", "Unclear", "High"),
       "Randomization" = c("Low", "High", "Unclear", "Low", "High"),
       "Sequence_generation" = c("Low", "High", "Unclear", "Unclear", "High"),
       "ITT.Analyses" = c("Low", "High", "Unclear", "Unclear", "Unclear"),
       "Selective_outcome_reporting" = c("Low", "High", "High", "High", "Unclear")
       )
rob.summary(data)
#> Column 'study' removed from plot because it did not contain the specified RoB ratings (only). 
#> 

# Example 2: Missing information, produce additional summary table
data2 = data.frame(
        "study" = c("Higgins et al., 2011", "Borenstein et al., 2008", "Holm, 1971",
                    "Zajonc et al., 2005", "Cuijpers, 2014"),
        "Allocation_concealment" = c("low", "high", "high", "uc", "high"),
        "Randomization" = c("low", "high", "miss", "low", "high"),
        "Sequence_generation" = c("low", "high", "uc", "uc", "high"),
        "ITT.Analyses" = c("low", "high", "uc", "uc", "uc"),
        "Selective_outcome_reporting" = c("low", "high", "high", "high", "uc")
       )
rob.summary(data2, name.high = "high", name.unclear = "uc", name.low = "low",
    name.missing = "miss", studies = data2$study, table = TRUE)
#> Column 'study' removed from plot because it did not contain the specified RoB ratings (only). 
#>