Change from Baseline

Source: R/tbl_baseline_chg.R

Typical use is tabulating changes from baseline measurement of an Analysis Variable.

tbl_baseline_chg(
  data,
  baseline_level,
  denominator,
  by = NULL,
  digits = NULL,
  statistic = gtsummary::all_continuous() ~ c("{mean} ({sd})", "{median}",
    "{min} - {max}"),
  id = "USUBJID",
  visit = "AVISIT",
  visit_number = "AVISITN",
  analysis_variable = "AVAL",
  change_variable = "CHG"
)

# S3 method for class 'tbl_baseline_chg'
add_overall(
  x,
  last = FALSE,
  col_label = "All Participants  \n(N = {style_roche_number(n)})",
  ...
)

Arguments

data

(data.frame)
A data frame.

baseline_level

(string)
String identifying baseline level in the visit variable.

denominator

(data.frame)
Data set used to compute the header counts (typically ADSL).

by

(tidy-select)
A single column from data. Summary statistics will be stratified by this variable. Default is NULL.

digits

(formula-list-selector)
Specifies how summary statistics are rounded. Values may be either integer(s) or function(s). If not specified, default formatting is assigned via assign_summary_digits(). See below for details.

statistic

(formula)
Formula or list of formulas specifying the summary statistics to display. Default is all_continuous() ~ c("{mean} ({sd})","{median}","{min} - {max}")

id

(tidy-select)
String identifying the unique subjects. Default is 'USUBJID'.

visit

(tidy-select)
String for the visit variable. Default is 'AVISIT'. If there are more than one entry for each visit and subject, only the first row is kept.

visit_number

(tidy-select)
String identifying the visit or analysis sequence number. Default is 'AVISITN'.

analysis_variable

(tidy-select)
String identifying the analysis values. Default is 'AVAL'.

change_variable

(tidy-select)
String identifying the change from baseline values. Default is 'CHG'.

x

(tbl_summary, tbl_svysummary, tbl_continuous, tbl_custom_summary)
A stratified 'gtsummary' table

last

(scalar logical)
Logical indicator to display overall column last in table. Default is FALSE, which will display overall column first.

col_label

(string)
String indicating the column label. Default is "**Overall** \nN = {style_number(N)}"

...

These dots are for future extensions and must be empty.

Value

A gtsummary table.

Examples

theme_gtsummary_roche()
#> Setting theme "Roche"

df <- cards::ADLB |>
  dplyr::mutate(AVISIT = trimws(AVISIT)) |>
  dplyr::filter(
    AVISIT != "End of Treatment",
    PARAMCD %in% c("SODIUM", "K")
  )

tbl_baseline_chg(
  data = df |> dplyr::filter(PARAMCD == "SODIUM"),
  baseline_level = "Baseline",
  by = "TRTA",
  denominator = cards::ADSL,
  statistic = everything() ~ c("{mean} ({sd})", "{median} ({p25}, {p75})")
)
#> ℹ Converting column "TRTA" to a factor.



  
Visit
      
        
Placebo

(N = 86)

      		
      
        
Xanomeline High Dose

(N = 84)

      		
      
        
Xanomeline Low Dose

(N = 84)

      		
    
Value at Visit
      Change from Baseline
      Value at Visit
      Change from Baseline
      Value at Visit
      Change from Baseline
    
Baseline











    n
7


7


6

    Mean (SD)
141.1 (1.5)


139.1 (2.2)


139.5 (4.2)

    Median (Q1, Q3)
141.0 (140.0, 142.0)


139.0 (138.0, 142.0)


138.5 (137.0, 144.0)

Week 2











    n
7
7
6
6
6
6
    Mean (SD)
140.6 (1.6)
-0.6 (1.9)
138.8 (3.3)
0.2 (3.5)
139.5 (2.3)
0.0 (3.7)
    Median (Q1, Q3)
141.0 (139.0, 142.0)
-1.0 (-2.0, 1.0)
140.5 (137.0, 141.0)
0.5 (-2.0, 2.0)
139.5 (138.0, 142.0)
-0.5 (-2.0, 3.0)
Week 4











    n
7
7
6
6
5
5
    Mean (SD)
139.3 (2.8)
-1.9 (2.5)
139.8 (3.5)
1.2 (2.0)
139.4 (1.3)
-0.6 (3.5)
    Median (Q1, Q3)
140.0 (137.0, 141.0)
-1.0 (-3.0, 0.0)
138.5 (138.0, 139.0)
0.0 (0.0, 2.0)
140.0 (138.0, 140.0)
0.0 (-3.0, 1.0)
Week 6











    n
5
5
6
6
4
4
    Mean (SD)
140.0 (1.9)
-0.4 (1.7)
139.7 (2.0)
1.0 (2.3)
140.5 (2.6)
1.8 (5.4)
    Median (Q1, Q3)
140.0 (139.0, 140.0)
0.0 (-2.0, 0.0)
139.0 (138.0, 141.0)
0.5 (-1.0, 2.0)
140.0 (138.5, 142.5)
2.5 (-2.5, 6.0)
Week 8











    n
5
5
4
4
3
3
    Mean (SD)
140.6 (1.5)
0.2 (1.6)
139.0 (2.2)
-0.5 (2.4)
139.0 (2.6)
0.0 (3.5)
    Median (Q1, Q3)
141.0 (139.0, 142.0)
1.0 (-1.0, 1.0)
139.5 (137.5, 140.5)
-0.5 (-2.5, 1.5)
140.0 (136.0, 141.0)
2.0 (-4.0, 2.0)
Week 12











    n
5
5
4
4
2
2
    Mean (SD)
141.0 (1.6)
0.6 (1.1)
140.8 (3.0)
1.3 (1.3)
139.0 (0.0)
0.0 (7.1)
    Median (Q1, Q3)
141.0 (140.0, 142.0)
1.0 (0.0, 1.0)
140.0 (139.0, 142.5)
1.0 (0.5, 2.0)
139.0 (139.0, 139.0)
0.0 (-5.0, 5.0)
Week 16











    n
5
5
4
4
2
2
    Mean (SD)
140.8 (1.1)
0.4 (0.9)
142.0 (3.6)
2.5 (1.9)
139.5 (0.7)
0.5 (6.4)
    Median (Q1, Q3)
141.0 (141.0, 141.0)
1.0 (0.0, 1.0)
141.0 (139.5, 144.5)
2.0 (1.0, 4.0)
139.5 (139.0, 140.0)
0.5 (-4.0, 5.0)
Week 20











    n
5
5
4
4
2
2
    Mean (SD)
141.6 (1.7)
1.2 (1.9)
138.8 (3.6)
-0.8 (2.2)
137.5 (0.7)
-1.5 (6.4)
    Median (Q1, Q3)
142.0 (141.0, 143.0)
2.0 (1.0, 2.0)
137.5 (136.5, 141.0)
-1.0 (-2.5, 1.0)
137.5 (137.0, 138.0)
-1.5 (-6.0, 3.0)
Week 24











    n
5
5
4
4
2
2
    Mean (SD)
140.4 (1.1)
0.0 (1.2)
139.3 (3.6)
-0.3 (2.6)
138.0 (2.8)
-1.0 (4.2)
    Median (Q1, Q3)
140.0 (140.0, 141.0)
0.0 (0.0, 1.0)
140.5 (137.0, 141.5)
0.5 (-2.0, 1.5)
138.0 (136.0, 140.0)
-1.0 (-4.0, 2.0)
Week 26











    n
5
5
4
4
2
2
    Mean (SD)
140.6 (2.5)
0.2 (2.8)
138.8 (4.6)
-0.8 (2.9)
142.5 (2.1)
3.5 (4.9)
    Median (Q1, Q3)
140.0 (139.0, 140.0)
-1.0 (-1.0, 0.0)
138.0 (136.0, 141.5)
-1.0 (-2.5, 1.0)
142.5 (141.0, 144.0)
3.5 (0.0, 7.0)

tbl_baseline_chg(
  data = df |> dplyr::filter(PARAMCD == "K"),
  baseline_level = "Baseline",
  by = "TRTA",
  denominator = cards::ADSL
) |>
  add_overall(last = TRUE, col_label = "All Participants")
#> ℹ Converting column "TRTA" to a factor.



  
Visit
      
        
Placebo

(N = 86)

      		
      
        
Xanomeline High Dose

(N = 84)

      		
      
        
Xanomeline Low Dose

(N = 84)

      		
      
        
All Participants

      		
    
Value at Visit
      Change from Baseline
      Value at Visit
      Change from Baseline
      Value at Visit
      Change from Baseline
      Value at Visit
      Change from Baseline
    
Baseline















    n
7


7


6


20

    Mean (SD)
4.34 (0.40)


4.30 (0.18)


4.23 (0.37)


4.30 (0.31)

    Median
4.30


4.40


4.20


4.30

    Min - Max
3.7 - 4.9


4.0 - 4.5


3.8 - 4.7


3.7 - 4.9

Week 2















    n
7
7
6
6
6
6
19
19
    Mean (SD)
4.47 (0.42)
0.13 (0.46)
4.35 (0.27)
0.07 (0.12)
4.57 (0.38)
0.33 (0.40)
4.46 (0.36)
0.17 (0.36)
    Median
4.50
-0.10
4.40
0.05
4.60
0.25
4.50
0.10
    Min - Max
3.9 - 5.1
-0.4 - 1.0
3.9 - 4.6
-0.1 - 0.2
4.1 - 5.1
-0.2 - 0.8
3.9 - 5.1
-0.4 - 1.0
Week 4















    n
7
7
6
6
5
5
18
18
    Mean (SD)
4.44 (0.38)
0.10 (0.37)
4.32 (0.46)
-0.02 (0.32)
4.46 (0.39)
0.16 (0.26)
4.41 (0.39)
0.08 (0.32)
    Median
4.60
0.00
4.45
0.05
4.70
0.20
4.55
0.05
    Min - Max
3.9 - 4.8
-0.4 - 0.7
3.4 - 4.6
-0.6 - 0.3
3.9 - 4.8
-0.2 - 0.4
3.4 - 4.8
-0.6 - 0.7
Week 6















    n
5
5
6
6
4
4
15
15
    Mean (SD)
4.50 (0.35)
0.26 (0.26)
4.20 (0.23)
-0.13 (0.12)
4.53 (0.59)
0.18 (0.59)
4.39 (0.39)
0.08 (0.36)
    Median
4.60
0.10
4.25
-0.15
4.45
0.05
4.30
0.00
    Min - Max
4.0 - 4.8
0.1 - 0.7
3.8 - 4.4
-0.3 - 0.0
3.9 - 5.3
-0.4 - 1.0
3.8 - 5.3
-0.4 - 1.0
Week 8















    n
5
5
4
4
3
3
12
12
    Mean (SD)
4.18 (0.23)
-0.06 (0.33)
4.18 (0.56)
-0.13 (0.41)
4.73 (0.25)
0.50 (0.17)
4.32 (0.42)
0.06 (0.40)
    Median
4.10
0.00
4.20
-0.20
4.70
0.40
4.35
0.10
    Min - Max
3.9 - 4.5
-0.4 - 0.3
3.5 - 4.8
-0.5 - 0.4
4.5 - 5.0
0.4 - 0.7
3.5 - 5.0
-0.5 - 0.7
Week 12















    n
5
5
4
4
2
2
11
11
    Mean (SD)
4.06 (0.31)
-0.18 (0.50)
4.13 (0.22)
-0.18 (0.24)
4.10 (0.14)
-0.10 (0.42)
4.09 (0.24)
-0.16 (0.37)
    Median
4.20
0.10
4.10
-0.10
4.10
-0.10
4.20
0.00
    Min - Max
3.6 - 4.4
-0.9 - 0.2
3.9 - 4.4
-0.5 - 0.0
4.0 - 4.2
-0.4 - 0.2
3.6 - 4.4
-0.9 - 0.2
Week 16















    n
5
5
4
4
2
2
11
11
    Mean (SD)
4.24 (0.47)
0.00 (0.47)
4.35 (0.51)
0.05 (0.35)
4.35 (0.49)
0.15 (0.07)
4.30 (0.44)
0.05 (0.36)
    Median
4.10
-0.20
4.45
0.05
4.35
0.15
4.20
0.00
    Min - Max
3.7 - 4.9
-0.4 - 0.8
3.7 - 4.8
-0.3 - 0.4
4.0 - 4.7
0.1 - 0.2
3.7 - 4.9
-0.4 - 0.8
Week 20















    n
5
5
4
4
2
2
11
11
    Mean (SD)
4.26 (0.39)
0.02 (0.50)
4.15 (0.26)
-0.15 (0.26)
4.10 (0.14)
-0.10 (0.42)
4.19 (0.30)
-0.06 (0.38)
    Median
4.20
-0.20
4.10
-0.10
4.10
-0.10
4.20
-0.20
    Min - Max
3.9 - 4.9
-0.5 - 0.8
3.9 - 4.5
-0.5 - 0.1
4.0 - 4.2
-0.4 - 0.2
3.9 - 4.9
-0.5 - 0.8
Week 24















    n
5
5
4
4
2
2
11
11
    Mean (SD)
4.26 (0.32)
0.02 (0.29)
4.20 (0.41)
-0.10 (0.32)
4.00 (0.14)
-0.20 (0.42)
4.19 (0.32)
-0.06 (0.30)
    Median
4.30
0.10
4.20
-0.05
4.00
-0.20
4.10
0.10
    Min - Max
3.8 - 4.6
-0.4 - 0.4
3.8 - 4.6
-0.5 - 0.2
3.9 - 4.1
-0.5 - 0.1
3.8 - 4.6
-0.5 - 0.4
Week 26















    n
5
5
4
4
2
2
11
11
    Mean (SD)
4.18 (0.37)
-0.06 (0.36)
4.28 (0.46)
-0.03 (0.31)
4.50 (0.71)
0.30 (0.14)
4.27 (0.43)
0.02 (0.32)
    Median
4.20
0.00
4.30
-0.10
4.50
0.30
4.20
0.00
    Min - Max
3.6 - 4.5
-0.6 - 0.4
3.7 - 4.8
-0.3 - 0.4
4.0 - 5.0
0.2 - 0.4
3.6 - 5.0
-0.6 - 0.4

# Split by PARAM
tbl_strata(
  data = df,
  strata = PARAMCD,
  .tbl_fun = ~ tbl_baseline_chg(
    data = .x,
    baseline_level = "Baseline",
    by = "TRTA",
    denominator = cards::ADSL
  ),
  .combine_with = "tbl_stack",
  .combine_args = list(group_header = NULL, quiet = TRUE)
) |>
  tbl_split_by_rows(variable_level = ends_with("lbl"))
#> ℹ Converting column "TRTA" to a factor.
#> ℹ Converting column "TRTA" to a factor.
#> 
#> ── Printing 2 Tables ──
#> 
#> ── Table 1 of 2: K 
#> 
#> ── Table 2 of 2: SODIUM