Stack tables — tbl_stack • gtsummary

Assists in patching together more complex tables. tbl_stack() appends two or more gtsummary tables.

tbl_stack(
  tbls,
  group_header = NULL,
  quiet = FALSE,
  attr_order = seq_along(tbls),
  tbl_ids = NULL,
  tbl_id_lbls = NULL
)

Arguments

tbls: (list)
List of gtsummary objects
group_header: (character)
Character vector with table headers where length matches the length of tbls
quiet: (scalar logical)
Logical indicating whether to suppress additional messaging. Default is FALSE.
attr_order: (integer)
Set the order table attributes are set. Tables are stacked in the order they are passed in the tbls argument: use attr_order to specify the order the table attributes take precedent. For example, to use the header from the second table specify attr_order=2. Default is to set precedent in the order tables are passed.
tbl_ids: (character)
Optional character vector of IDs that will be assigned to the input tables. The ID is assigned by assigning a name to the tbls list, which is returned in x$tbls.
tbl_id_lbls: (vector)
Optional vector of the same length tbls. When specified a new, hidden column is added to the returned .$table_body with these labels. The most common use case of this argument is for the development of other functions.

Value

A tbl_stack object

Author

Daniel D. Sjoberg

Examples

# Example 1 ----------------------------------
# stacking two tbl_regression objects
t1 <-
  glm(response ~ trt, trial, family = binomial) %>%
  tbl_regression(
    exponentiate = TRUE,
    label = list(trt ~ "Treatment (unadjusted)")
  )

t2 <-
  glm(response ~ trt + grade + stage + marker, trial, family = binomial) %>%
  tbl_regression(
    include = "trt",
    exponentiate = TRUE,
    label = list(trt ~ "Treatment (adjusted)")
  )

tbl_stack(list(t1, t2))


  Characteristic
      OR
      95% CI
      p-value
    
Treatment (unadjusted)





    Drug A
—
—

    Drug B
1.21
0.66, 2.24
0.5
Treatment (adjusted)





    Drug A
—
—

    Drug B
1.48
0.78, 2.86
0.2
Abbreviations: CI = Confidence Interval, OR = Odds Ratio
    

# Example 2 ----------------------------------
# stacking two tbl_merge objects
library(survival)
t3 <-
  coxph(Surv(ttdeath, death) ~ trt, trial) %>%
  tbl_regression(
    exponentiate = TRUE,
    label = list(trt ~ "Treatment (unadjusted)")
  )

t4 <-
  coxph(Surv(ttdeath, death) ~ trt + grade + stage + marker, trial) %>%
  tbl_regression(
    include = "trt",
    exponentiate = TRUE,
    label = list(trt ~ "Treatment (adjusted)")
  )

# first merging, then stacking
row1 <- tbl_merge(list(t1, t3), tab_spanner = c("Tumor Response", "Death"))
row2 <- tbl_merge(list(t2, t4))

tbl_stack(list(row1, row2), group_header = c("Unadjusted Analysis", "Adjusted Analysis"))


  Characteristic
      
        Tumor Response
      
      
        Death
      
    
OR
      95% CI
      p-value
      HR
      95% CI
      p-value
    
Unadjusted Analysis
    
Treatment (unadjusted)











    Drug A
—
—


—
—

    Drug B
1.21
0.66, 2.24
0.5
1.25
0.86, 1.81
0.2
Adjusted Analysis
    
Treatment (adjusted)











    Drug A
—
—


—
—

    Drug B
1.48
0.78, 2.86
0.2
1.30
0.88, 1.92
0.2
Abbreviations: CI = Confidence Interval, HR = Hazard Ratio, OR = Odds Ratio

Characteristic	OR	95% CI	p-value
Treatment (unadjusted)
Drug A	—	—
Drug B	1.21	0.66, 2.24	0.5
Treatment (adjusted)
Drug A	—	—
Drug B	1.48	0.78, 2.86	0.2
Abbreviations: CI = Confidence Interval, OR = Odds Ratio

Characteristic	Tumor Response			Death
Characteristic	OR	95% CI	p-value	HR	95% CI	p-value
Unadjusted Analysis
Treatment (unadjusted)
Drug A	—	—		—	—
Drug B	1.21	0.66, 2.24	0.5	1.25	0.86, 1.81	0.2
Adjusted Analysis
Treatment (adjusted)
Drug A	—	—		—	—
Drug B	1.48	0.78, 2.86	0.2	1.30	0.88, 1.92	0.2
Abbreviations: CI = Confidence Interval, HR = Hazard Ratio, OR = Odds Ratio