Setup and data

To demonstrate the various functionalities of the function we will use the dataset survival::colon.

library(survival)
data(cancer, package="survival")

colon1 <-  colon %>%
  group_by(id) %>%
  slice(1) %>% # Select the first row within each id group
  ungroup()
  

The dataset colon contains data of 1858 patients from one of the first successful trials of adjuvant chemotherapy for colon cancer.

For simplicity, we focus here on recurrence only, two treatment groups, and four variable:

• the treatment group (rx),
• the sex (Male),
• the age (age) and
• the extent of local spread (extent).

We also add a few missing values for the variable extent.

set.seed(123)
colon2 <- colon1 %>%
  select(rx, sex, age, extent) %>%
  filter(rx != "Lev") %>%
  mutate(rx = if_else(rx == "Obs", "Control", rx),
         extent = if_else(row_number() %in% sample(row_number(), size = round(0.1 * n())), NA, extent)) %>% 
  rename(Male = sex) %>% 
  mutate(extent = as.factor(extent))

head(colon2)
#> # A tibble: 6 × 4
#>   rx       Male   age extent
#>   <chr>   <dbl> <dbl> <fct> 
#> 1 Lev+5FU     1    43 3     
#> 2 Lev+5FU     1    63 3     
#> 3 Control     0    71 2     
#> 4 Lev+5FU     0    66 3     
#> 5 Control     1    69 3     
#> 6 Lev+5FU     0    57 3

Simple table

By default, the function produces a table with all variables present in the dataset.

summaryTable(data = colon2)

If only specific variables are to be included, they need to be entered in the argument vars. The argument group allows the summary statistics to be stratified by this variable.

summaryTable(data = colon2, 
             vars = c("Male", "age", "extent"), 
             group = "rx")

summaryTable(data = colon2, 
             group = "rx",
             labels = list(age = "Age", extent = "Extent"))

Adding number of observations

The number of observations which are not missing values are by default added in a new column. This can be disabled by setting the argument add_n to FALSE.

summaryTable(data = colon2, 
             group = "rx",
            labels = list(rx = "Arm", age = "Age", extent = "Extent"), 
             add_n = FALSE)

Overall column

An “overall” column can be added by setting the argument overall to TRUE.

summaryTable(data = colon2, 
             group = "rx",
             overall = TRUE, 
             labels = list(age = "Age", extent = "Extent"))

Variable types

The function gtsummary::tbl_summary considers numeric variables with fewer than 10 unique values as categorical by default. This is not the case in the function summaryTable.

Per default, all numeric variables are considered as continuous, unless they only have two unique values: 0 and 1. In that case, they are considered as dichotomous. This can be changed by setting the argument continuous_as to categorical.

For dichotomous variables, all levels are displayed by default. To show only one row, use the argument dichotomous_as = dichotomous. The reference level is specified using the argument value = list(variable ~ "level to show").

summaryTable(data = colon2,
             group = "rx",
             vars = "Male",
            labels = list(age = "Age"), 
            dichotomous_as = "dichotomous", 
            value = list(Male ~ "1"),
            missing = FALSE)

By default, the function plots the median and range for continuous variables. A number of other options are available, using the argument stat_cont.

summaryTable(data = colon2, group = "rx", 
             stat_cont = "median_IQR", 
             stat_cat = "n_N",
              labels = list(age = "Age", sex = "Sex", extent = "Extent"))

Tests

By default, no p-value and confidence (CI) are displayed. p-values can be added by setting test to TRUE and CI by setting ci to TRUE.

The default test type for continuous variable is wilcox.test, and fisher.test for categorical variables. This can be changed in test_cont and test_cat, respectively.

The default CI type for continuous variables is wilcox.test and wilson for categorical variables. This can be changed in ci_cont and ci_cat, respectively.

summaryTable(data = colon2, 
             group = "rx", 
             vars = c("age", "extent"), 
             stat_cont = "mean_sd", 
             test = TRUE,
             ci = TRUE,
             labels = list(age = "Age", extent = "Extent")
             )

Missing values

Per default, missing values are shown as a separate category. This can be disabled by setting missing to FALSE.

For missing = TRUE, the percentage are automatically added next to the missing number. This can be disabled by setting the argument missing_percentage to FALSE.

summaryTable(data = colon2, 
             group = "rx", 
             vars = "extent", 
             test = TRUE,
             ci = TRUE,
             missing_percent = FALSE,
             labels = list(extent = "Extent")
             )

summaryTable(data = colon2, 
             group = "rx", 
             vars = "extent", 
             test = TRUE,
             ci = TRUE,
             missing_percent = TRUE,
             labels = list(extent = "Extent")
             )

The tables with and without missing values can also be put next to each other by setting missing to "both".

summaryTable(data = colon2, 
             group = "rx", 
             vars = "extent", 
             missing_percent = "both", 
             test = TRUE,
              labels = list(extent = "Extent")
             )

Further customization

Digits can be customized with the arguments digits_cont and digits_cat. The argument as_flex_table (default to TRUE) converts the gtsummary object to a flextable object, which is better for Word output.

Next steps

The argument type will be introduced in a future release to enable more fine-grained customization of the variables types.