Create study table

This function creates an overview table containing selected study information.

Usage

createStudyTable(.data, ...,
                 .round.by.digits = NULL,
                 .column.names = NULL,
                 .na.replace = "nr",
                 .html = TRUE)

Arguments

.data: Meta-analysis study data; typically data created by the expandMultiarmTrials or calculateEffectSizes function. Trial arm-specific information (e.g. sample size in each group) can be added via addTrialArmInfo. See 'Details'.
...: <dplyr_data_masking>. The name of several columns (included in .data) that should be added to the study table. Also allows to alter individual values/factor labels within a variable. See 'Details'.
.round.by.digits: named list. Should contain the number of digits by which to round a numeric column in .data. The name of the column must be specified in the list element's name. Set to NULL if no rounding should be performed (default).
.column.names: named list. If variable names should be renamed when producing the study table, the new name should be included in this list. The original column name must be specified as the name of the list element. Set to NULL if no renaming should be performed (default).
.na.replace: character to replace NA values with; "nr" by default.
.html: logical. Should an HTML table be produced? TRUE by default. See 'Details'.

Value

Returns a data.frame with all the selected variables. If .html is TRUE, an HTML table will also be produced.

Details

General Purpose: This function allows to select variables to be included in a study table. Such study tables are typically part of a meta-analysis report/article. Variables are included by adding their names to the function call and separating them with commas. The columns will appear in the exact same order as specified in the function.

Trial-Arm Variables: Before producing the final table, createStudyTable will filter out all redundant rows based on the selected variables. If you want to include information that differs between the (two or more) trial arms (e.g. the sample size of each group) as separate columns, you have to use addTrialArmInfo first. This ensures that individual columns are created for both the intervention and control group (e.g. N_ig and N_cg), which can then be included in the call to createStudyTable.

Changing Values: The function also allows to change specified values within a variable. Factor levels encoded as numbers, for example (e.g. country = 1 for European studies, and so forth) can be changed by adding a concatenated (c) vector to the name of the variable. This vector should contain the new value as a character on the left side, and the old value on the right side, separated by '=' (e.g. country = c("Europe" = "1")). The values will then be recoded before producing the table.

HTML Table: By default, createStudyTable produces an HTML table using kable. The HTML table makes copy & paste easier, particularly when working with MS Word, since the table formatting is kept.

For more details see the help vignette: vignette("metapsyTools").

Author

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

Examples

if (FALSE) {
# Filter out all primary outcomes, check data,
# calculate effect sizes, then produce study table 
# using selected information.
data("depressionPsyCtr")

depressionPsyCtr %>%
 filterPriorityRule(
   instrument = c("phq-9", "bdi-1", 
                  "hdrs", "ces-d")) %>%
 checkDataFormat() %>%
 checkConflicts() %>%
 calculateEffectSizes() %>%
 createStudyTable(
   study,
   diagnosis = c("Cutoff" = "3", "Mood" = "2",
                 "MDD" = "1"),
   age_group, instrument,
   mean_age, percent_women,
   condition_arm1 = c("CBT" = "cbt", "PST" = "pst",
                      "BA" = "bat", "LR" = "lrt",
                      "PDT" = "dyn", "IPT" = "ipt"),
   condition_arm2, n_arm1, n_arm2,
   country = c("Canada" = "4", "Europe" = "3", "USA" = "1",
               "Middle East" = "7"),
   sg, ac, ba, itt,
   .round.by.digits = list(mean_age = 0, n_arm1 = 0, 
                           n_arm2 = 0),
   .column.names = list(age_group = "age group",
                        n_arm1 = "N (arm1)",
                        n_arm2 = "N (arm2)",
                        percent_women = "% female")) -> table
}