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Tabulation

2025-06-17

Source: vignettes/junco.Rmd
junco.Rmd

junco Tabulation

The junco R package provides functions to create analyses for clinical trials in R. It is considered as an add-on/alternative to the tern package, which is the first package available in the NEST framework for analysis functions with main focus for clinical trials. The core functionality for tabulation is built on the more general purpose rtables package. New users should first begin by reading the “Introduction to tern” and “Introduction to rtables” vignettes.

The packages used in this vignette are:

The datasets used in this vignette are:

adsl <- ex_adsl
adae <- ex_adae
advs <- ex_advs

Some common data manipulation on/from these datasets, such as adding extra variables and defining some tabulation settings, like treatment variable, control group for the table.


trtvar <- "ARM"
ctrl_grp <- "B: Placebo"

non_ctrl_grp <- setdiff(levels(adsl[[trtvar]]), ctrl_grp)

adsl$colspan_trt <- factor(ifelse(adsl[[trtvar]] == ctrl_grp, " ", "Active Study Agent"),
  levels = c("Active Study Agent", " ")
)

adsl$rrisk_header <- "Risk Difference (%) (95% CI)"
adsl$rrisk_label <- paste(adsl[[trtvar]], paste("vs", ctrl_grp))
adsl$rrisk_header_vs <- "Difference in Mean Change (95% CI)"

# define colspan_trt_map
colspan_trt_map <- create_colspan_map(adsl,
  non_active_grp = ctrl_grp,
  non_active_grp_span_lbl = " ",
  active_grp_span_lbl = "Active Study Agent",
  colspan_var = "colspan_trt",
  trt_var = trtvar
)

# define reference group specification
ref_path <- c("colspan_trt", " ", trtvar, ctrl_grp)

adae[["TRTEMFL"]] <- "Y"

# add adsl variables to adae
adae <- adae %>% left_join(., adsl) 
advs <- advs %>% left_join(., adsl) 

advs[advs[["ABLFL"]] == "Y", "CHG"] <- NA

junco Analysis Functions

The junco analysis functions are used in combination with the rtables layout functions, rtables::analyze and rtables::summarize_row_groups, in the pipeline which creates the rtables table. They apply some statistical logic to the layout of the rtables table. The table layout is materialized with the rtables::build_table function and the data.

The junco analysis functions are functions that can be applied as an afun in either rtables::analyze or as a cfun in rtables::summarize_row_groups function. This is a slightly different approach to tern, where the table layout is constructed using analyze functions, which are wrappers around rtables::analyze.

Just like tern analyze functions, the junco analysis functions offer various methods useful from the perspective of clinical trials and other statistical projects.

Examples of the junco analysis functions are a_freq_j, a_freq_subcol_j, a_summarize_aval_chg_diff_j or a_summarize_ex_j.

A complete list of analysis functions can be found in the junco website functions reference.

Tabulation Examples using a_freq_j

We present an example usage of a_freq_j for the very common AE table in clinical trials.

The standard table of adverse events is a summary by system organ class and preferred term. For frequency counts by preferred term, if there are multiple occurrences of the same AE in an individual we count them only once.

With junco package, this table can be created with several calls to the same a_freq_j function in a tabulation pipeline using rtables::analyze and rtables::summarize_row_groups.

In next paragraph, we’ll describe the difference versus using tern package, for similar AE table. Differences in how to define the layout as well as differences in the resulting table.

## extra args for a_freq_j controlling specification of
## reference group, denominator used to calculate percentages,
## and other details
extra_args_rr <- list(
  denom = "n_altdf",
  riskdiff = TRUE,
  ref_path = ref_path,
  method = "wald",
  .stats = c("count_unique_fraction")
)

Using junco analysis function a_freq_j we define the layout, using 2 analyze calls (for overall summary - TRTEMFL and preferred term - AEDECOD), and 1 summarize_row_groups call (for system organ class - AEBODSYS)

Note that the same specifications, extra_args_rr can be re-used in the first and second analyze call, as well as in the in-between call to summarize_row_groups.

For the overall summary, we add as extra specification to restrict to TRTEMFL = “Y” values and the label to show in the row.

lyt <- basic_table(
  top_level_section_div = " ",
  colcount_format = "N=xx"
) %>%
  ## main columns
  split_cols_by("colspan_trt", split_fun = trim_levels_to_map(map = colspan_trt_map)) %>%
  split_cols_by(trtvar, show_colcounts = TRUE) %>%
  ## risk diff columns, note nested = FALSE
  split_cols_by("rrisk_header", nested = FALSE) %>%
  split_cols_by(trtvar, labels_var = "rrisk_label", split_fun = remove_split_levels(ctrl_grp),
    show_colcounts = FALSE
  ) %>%
  analyze("TRTEMFL", afun = a_freq_j,
    extra_args = append(extra_args_rr, list(val = "Y", label = "Number of subjects with AE"))
  ) %>%   
  split_rows_by("AEBODSYS",
    split_label = "System Organ Class",
    split_fun = trim_levels_in_group("AEDECOD"),
    label_pos = "topleft",
    section_div = c(" "),
    nested = FALSE
  ) %>%
  summarize_row_groups("AEBODSYS", cfun = a_freq_j,
    extra_args = extra_args_rr
  ) %>%
  analyze("AEDECOD", afun = a_freq_j,
    extra_args = extra_args_rr
  )

Now we just build and view the resulting table.

tbl <- build_table(lyt, adae, alt_counts_df = adsl)
head(tbl, 10)
#>                                   Active Study Agent                                                                            
#>                               A: Drug X    C: Combination   B: Placebo                 Risk Difference (%) (95% CI)             
#> System Organ Class              N=134          N=132           N=134      A: Drug X vs B: Placebo   C: Combination vs B: Placebo
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Number of subjects with AE   122 (91.0%)    120 (90.9%)     123 (91.8%)      -0.7 (-7.5, 6.0)             -0.9 (-7.6, 5.9)      
#>                                                                                                                                 
#> cl A.1                       78 (58.2%)      89 (67.4%)     75 (56.0%)       2.2 (-9.6, 14.1)            11.5 (-0.1, 23.1)      
#>   dcd A.1.1.1.1              50 (37.3%)      63 (47.7%)     45 (33.6%)       3.7 (-7.7, 15.2)             14.1 (2.5, 25.8)      
#>   dcd A.1.1.1.2              48 (35.8%)      50 (37.9%)     48 (35.8%)       0.0 (-11.5, 11.5)            2.1 (-9.5, 13.7)      
#>                                                                                                                                 
#> cl B.1                       47 (35.1%)      43 (32.6%)     49 (36.6%)      -1.5 (-13.0, 10.0)           -4.0 (-15.4, 7.4)      
#>   dcd B.1.1.1.1              47 (35.1%)      43 (32.6%)     49 (36.6%)      -1.5 (-13.0, 10.0)           -4.0 (-15.4, 7.4)      
#>                                                                                                                                 
#> cl B.2                       79 (59.0%)      85 (64.4%)     74 (55.2%)       3.7 (-8.1, 15.6)             9.2 (-2.6, 20.9)      
#>   dcd B.2.1.2.1              49 (36.6%)      52 (39.4%)     44 (32.8%)       3.7 (-7.7, 15.1)             6.6 (-5.0, 18.1)      
#>   dcd B.2.2.3.1              48 (35.8%)      51 (38.6%)     54 (40.3%)       -4.5 (-16.1, 7.1)           -1.7 (-13.4, 10.1)     
#>                                                                                                                                 
#> cl C.1                       43 (32.1%)      43 (32.6%)     46 (34.3%)       -2.2 (-13.5, 9.0)           -1.8 (-13.1, 9.6)

Minor differences between current table and a similar table using tern analyze functions.

Here is a similar table generated with the tern functions (summarize_occurrences and count_occurrences), as close as possible to our target AE table we produced before in tbl.


lyt_tern <- basic_table(
  top_level_section_div = " ",
  colcount_format = "N=xx"
) %>%
  ## main columns
  split_cols_by(trtvar, 
    show_colcounts = TRUE, 
    split_fun = add_riskdiff(arm_x = ctrl_grp, arm_y = non_ctrl_grp)
  ) %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event"
    ), 
    riskdiff = TRUE
  ) %>%  
  split_rows_by("AEBODSYS",
    split_label = "System Organ Class",
    split_fun = trim_levels_in_group("AEDECOD"),
    label_pos = "topleft",
    section_div = c(" "),
    nested = FALSE
  ) %>%
  summarize_occurrences(var = "AEBODSYS",
    denom = "N_col",
    riskdiff = TRUE,
    .stats = c("count_fraction")
  ) %>%
  count_occurrences(vars = "AEDECOD",  
    denom = "N_col",
    riskdiff = TRUE,
    .stats = c("count_fraction")
  )


tbl_tern <- build_table(lyt_tern, adae, alt_counts_df = adsl)
head(tbl_tern, 10)
#>                                                                                                         Risk Difference (%) (95% CI)   Risk Difference (%) (95% CI) 
#>                                                             A: Drug X    B: Placebo    C: Combination     B: Placebo vs. A: Drug X     B: Placebo vs. C: Combination
#> System Organ Class                                            N=134         N=134          N=132                   N=268                           N=266            
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Total number of patients with at least one adverse event   122 (91.0%)   123 (91.8%)    120 (90.9%)           0.7 (-6.0 - 7.5)               0.9 (-5.9 - 7.6)       
#>                                                                                                                                                                     
#> cl A.1                                                     78 (58.2%)     75 (56%)       89 (67.4%)          -2.2 (-14.1 - 9.6)             -11.5 (-23.1 - 0.1)     
#>   dcd A.1.1.1.1                                            50 (37.3%)    45 (33.6%)      63 (47.7%)          -3.7 (-15.2 - 7.7)            -14.1 (-25.8 - -2.5)     
#>   dcd A.1.1.1.2                                            48 (35.8%)    48 (35.8%)      50 (37.9%)          0.0 (-11.5 - 11.5)             -2.1 (-13.7 - 9.5)      
#>                                                                                                                                                                     
#> cl B.1                                                     47 (35.1%)    49 (36.6%)      43 (32.6%)          1.5 (-10.0 - 13.0)              4.0 (-7.4 - 15.4)      
#>   dcd B.1.1.1.1                                            47 (35.1%)    49 (36.6%)      43 (32.6%)          1.5 (-10.0 - 13.0)              4.0 (-7.4 - 15.4)      
#>                                                                                                                                                                     
#> cl B.2                                                      79 (59%)     74 (55.2%)      85 (64.4%)          -3.7 (-15.6 - 8.1)             -9.2 (-20.9 - 2.6)      
#>   dcd B.2.1.2.1                                            49 (36.6%)    44 (32.8%)      52 (39.4%)          -3.7 (-15.1 - 7.7)             -6.6 (-18.1 - 5.0)      
#>   dcd B.2.2.3.1                                            48 (35.8%)    54 (40.3%)      51 (38.6%)          4.5 (-7.1 - 16.1)              1.7 (-10.1 - 13.4)      
#>                                                                                                                                                                     
#> cl C.1                                                     43 (32.1%)    46 (34.3%)      43 (32.6%)          2.2 (-9.0 - 13.5)               1.8 (-9.6 - 13.1)

The main differences in the resulting table are

  • the comparison against the reference group is reversed (ie B: Placebo vs. A: Drug X instead of A: Drug X vs. B: Placebo).
  • the extra column spanner for active treatment group is not present in the tern version, and cannot be added.

a_freq_j supports various methods for the risk difference column.

One of the statistical options to control with the usage of a_freq_j is the method for the risk difference calculation for the risk difference columns. All methods available in ´tern::estimate_proportion_diff´ can be supported. Eg, switching to waldcc, or others can be done. There is no option to switch methods using tern count_occurrences layouts, only wald method is available.

extra_args_rr[["method"]] <- "waldcc"

tbl2 <- basic_table(
  top_level_section_div = " ",
  colcount_format = "N=xx"
) %>%
  ## main columns
  split_cols_by("colspan_trt", split_fun = trim_levels_to_map(map = colspan_trt_map)) %>%
  split_cols_by(trtvar, show_colcounts = TRUE) %>%
  ## risk diff columns, note nested = FALSE
  split_cols_by("rrisk_header", nested = FALSE) %>%
  split_cols_by(trtvar, labels_var = "rrisk_label", split_fun = remove_split_levels(ctrl_grp),
    show_colcounts = FALSE) %>%
  split_rows_by("AEBODSYS",
    split_label = "System Organ Class",
    split_fun = trim_levels_in_group("AEDECOD"),
    label_pos = "topleft",
    section_div = c(" "),
    nested = FALSE
  ) %>%
  summarize_row_groups("AEBODSYS", cfun = a_freq_j,
    extra_args = extra_args_rr
  ) %>%
  analyze("AEDECOD", afun = a_freq_j,
    extra_args = extra_args_rr
  ) %>% 
  build_table(adae, alt_counts_df = adsl)

head(tbl2, 10)
#>                          Active Study Agent                                                                           
#>                      A: Drug X    C: Combination   B: Placebo                Risk Difference (%) (95% CI)             
#> System Organ Class     N=134          N=132          N=134      A: Drug X vs B: Placebo   C: Combination vs B: Placebo
#> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> cl A.1               78 (58.2%)     89 (67.4%)     75 (56.0%)      2.2 (-10.4, 14.8)           11.5 (-0.9, 23.8)      
#>   dcd A.1.1.1.1      50 (37.3%)     63 (47.7%)     45 (33.6%)      3.7 (-8.5, 15.9)             14.1 (1.7, 26.6)      
#>   dcd A.1.1.1.2      48 (35.8%)     50 (37.9%)     48 (35.8%)      0.0 (-12.2, 12.2)           2.1 (-10.3, 14.4)      
#>                                                                                                                       
#> cl B.1               47 (35.1%)     43 (32.6%)     49 (36.6%)     -1.5 (-13.7, 10.7)           -4.0 (-16.2, 8.2)      
#>   dcd B.1.1.1.1      47 (35.1%)     43 (32.6%)     49 (36.6%)     -1.5 (-13.7, 10.7)           -4.0 (-16.2, 8.2)      
#>                                                                                                                       
#> cl B.2               79 (59.0%)     85 (64.4%)     74 (55.2%)      3.7 (-8.9, 16.3)             9.2 (-3.3, 21.7)      
#>   dcd B.2.1.2.1      49 (36.6%)     52 (39.4%)     44 (32.8%)      3.7 (-8.4, 15.9)             6.6 (-5.7, 18.8)      
#>   dcd B.2.2.3.1      48 (35.8%)     51 (38.6%)     54 (40.3%)      -4.5 (-16.8, 7.9)           -1.7 (-14.2, 10.8)     
#>                                                                                                                       
#> cl C.1               43 (32.1%)     43 (32.6%)     46 (34.3%)      -2.2 (-14.3, 9.8)           -1.8 (-13.8, 10.3)     
#>   dcd C.1.1.1.3      43 (32.1%)     43 (32.6%)     46 (34.3%)      -2.2 (-14.3, 9.8)           -1.8 (-13.8, 10.3)

Creation of Subgroup tables with a_freq_j

The junco function a_freq_j also supports the creation of subgroup tables, which is demonstrated in below table.


extra_args_rr_common <- list(
  denom = "n_altdf",
  denom_by = "SEX"
)

extra_args_rr <- append(
  extra_args_rr_common, 
  list(
    riskdiff = FALSE,
    extrablankline = TRUE,
    .stats = c("n_altdf"),
    label_fstr = "Gender: %s"
  )
)

extra_args_rr2 <- append(
  extra_args_rr_common, 
  list(
    riskdiff = TRUE,
    ref_path = ref_path,
    method = "wald",
    .stats = c("count_unique_denom_fraction"),
    na_str = rep("NA", 3)
  )
)

tbl <- basic_table(
  top_level_section_div = " ",
  colcount_format = "N=xx"
) %>%
  ## main columns
  split_cols_by("colspan_trt", split_fun = trim_levels_to_map(map = colspan_trt_map)) %>%
  split_cols_by(trtvar, show_colcounts = TRUE) %>%
  ## risk diff columns, note nested = FALSE
  split_cols_by("rrisk_header", nested = FALSE) %>%
  split_cols_by(trtvar, labels_var = "rrisk_label", split_fun = remove_split_levels(ctrl_grp),
    show_colcounts = FALSE) %>%
  split_rows_by("SEX", split_fun = drop_split_levels) %>% 
  summarize_row_groups("SEX", cfun = a_freq_j,
    extra_args = extra_args_rr
  ) %>%
  split_rows_by("TRTEMFL",
    split_fun = keep_split_levels("Y"),
    indent_mod = -1L,
    section_div = c(" ")
  ) %>%
  summarize_row_groups("TRTEMFL",
    cfun = a_freq_j,
    extra_args = append(extra_args_rr2, list(label =  "Subjects with >=1 AE", extrablankline = TRUE))
  ) %>%    
   split_rows_by("AEBODSYS",
    split_label = "System Organ Class",
    split_fun = trim_levels_in_group("AEDECOD"),
    label_pos = "topleft",
    section_div = c(" "),
    nested = TRUE
  ) %>%
  summarize_row_groups("AEBODSYS", cfun = a_freq_j,
    extra_args = extra_args_rr2
  ) %>%
  analyze("AEDECOD", afun = a_freq_j,
    extra_args = extra_args_rr2
  ) %>% 
  build_table(adae, alt_counts_df = adsl)

 head(tbl, 30)
#>                              Active Study Agent                                                                               
#>                          A: Drug X     C: Combination    B: Placebo                  Risk Difference (%) (95% CI)             
#> System Organ Class         N=134           N=132            N=134       A: Drug X vs B: Placebo   C: Combination vs B: Placebo
#> ——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Gender: F                   79               66              77                                                               
#>                                                                                                                               
#> Subjects with >=1 AE   72/79 (91.1%)   61/66 (92.4%)    73/77 (94.8%)      -3.7 (-11.7, 4.3)           -2.4 (-10.5, 5.7)      
#>                                                                                                                               
#>   cl A.1               53/79 (67.1%)   42/66 (63.6%)    48/77 (62.3%)      4.8 (-10.2, 19.7)           1.3 (-14.6, 17.2)      
#>     dcd A.1.1.1.1      34/79 (43.0%)   32/66 (48.5%)    30/77 (39.0%)      4.1 (-11.3, 19.5)            9.5 (-6.7, 25.8)      
#>     dcd A.1.1.1.2      32/79 (40.5%)   24/66 (36.4%)    31/77 (40.3%)      0.2 (-15.2, 15.6)           -3.9 (-19.9, 12.1)     
#>                                                                                                                               
#>   cl B.1               28/79 (35.4%)   21/66 (31.8%)    32/77 (41.6%)      -6.1 (-21.4, 9.1)           -9.7 (-25.5, 6.0)      
#>     dcd B.1.1.1.1      28/79 (35.4%)   21/66 (31.8%)    32/77 (41.6%)      -6.1 (-21.4, 9.1)           -9.7 (-25.5, 6.0)      
#>                                                                                                                               
#>   cl B.2               46/79 (58.2%)   40/66 (60.6%)    43/77 (55.8%)      2.4 (-13.1, 17.9)           4.8 (-11.4, 20.9)      
#>     dcd B.2.1.2.1      29/79 (36.7%)   19/66 (28.8%)    30/77 (39.0%)     -2.3 (-17.5, 13.0)           -10.2 (-25.6, 5.3)     
#>     dcd B.2.2.3.1      30/79 (38.0%)   24/66 (36.4%)    30/77 (39.0%)     -1.0 (-16.3, 14.3)           -2.6 (-18.5, 13.3)     
#>                                                                                                                               
#>   cl C.1               30/79 (38.0%)   25/66 (37.9%)    35/77 (45.5%)      -7.5 (-22.9, 8.0)           -7.6 (-23.7, 8.6)      
#>     dcd C.1.1.1.3      30/79 (38.0%)   25/66 (37.9%)    35/77 (45.5%)      -7.5 (-22.9, 8.0)           -7.6 (-23.7, 8.6)      
#>                                                                                                                               
#>   cl C.2               23/79 (29.1%)   28/66 (42.4%)    33/77 (42.9%)     -13.7 (-28.7, 1.2)           -0.4 (-16.7, 15.8)     
#>     dcd C.2.1.2.1      23/79 (29.1%)   28/66 (42.4%)    33/77 (42.9%)     -13.7 (-28.7, 1.2)           -0.4 (-16.7, 15.8)     
#>                                                                                                                               
#>   cl D.1               45/79 (57.0%)   39/66 (59.1%)    38/77 (49.4%)      7.6 (-8.0, 23.2)             9.7 (-6.6, 26.0)      
#>     dcd D.1.1.1.1      25/79 (31.6%)   26/66 (39.4%)    28/77 (36.4%)     -4.7 (-19.6, 10.1)           3.0 (-12.9, 19.0)      
#>     dcd D.1.1.4.2      30/79 (38.0%)   26/66 (39.4%)    21/77 (27.3%)      10.7 (-3.9, 25.3)           12.1 (-3.3, 27.5)      
#>                                                                                                                               
#>   cl D.2               26/79 (32.9%)   32/66 (48.5%)    40/77 (51.9%)     -19.0 (-34.3, -3.8)          -3.5 (-19.9, 13.0)     
#>     dcd D.2.1.5.3      26/79 (32.9%)   32/66 (48.5%)    40/77 (51.9%)     -19.0 (-34.3, -3.8)          -3.5 (-19.9, 13.0)     
#>                                                                                                                               
#> Gender: M                   51               60              55                                                               
#>                                                                                                                               
#> Subjects with >=1 AE   46/51 (90.2%)   53/60 (88.3%)    48/55 (87.3%)      2.9 (-9.1, 14.9)            1.1 (-10.9, 13.0)      
#>                                                                                                                               
#>   cl A.1               24/51 (47.1%)   41/60 (68.3%)    25/55 (45.5%)      1.6 (-17.4, 20.6)            22.9 (5.2, 40.5)      
#>     dcd A.1.1.1.1      16/51 (31.4%)   29/60 (48.3%)    15/55 (27.3%)      4.1 (-13.2, 21.4)            21.1 (3.8, 38.3)      
#>     dcd A.1.1.1.2      15/51 (29.4%)   22/60 (36.7%)    15/55 (27.3%)      2.1 (-15.0, 19.3)            9.4 (-7.6, 26.3)      
#>                                                                                                                               
#>   cl B.1               18/51 (35.3%)   21/60 (35.0%)    16/55 (29.1%)      6.2 (-11.6, 24.0)           5.9 (-11.1, 22.9)      
#>     dcd B.1.1.1.1      18/51 (35.3%)   21/60 (35.0%)    16/55 (29.1%)      6.2 (-11.6, 24.0)           5.9 (-11.1, 22.9)

This table cannot be generated using the tern count_occurrences methods.

Other junco features : Extra Statistics have been added to some tern statistical functions 

tern::get_stats("summarize_ancova")
#> [1] "n"              "lsmean"         "lsmean_diff"    "lsmean_diff_ci"
#> [5] "pval"
tern::get_stats("analyze_vars_numeric")
#>  [1] "n"               "sum"             "mean"            "sd"             
#>  [5] "se"              "mean_sd"         "mean_se"         "mean_ci"        
#>  [9] "mean_sei"        "mean_sdi"        "mean_pval"       "median"         
#> [13] "mad"             "median_ci"       "quantiles"       "iqr"            
#> [17] "range"           "min"             "max"             "median_range"   
#> [21] "cv"              "geom_mean"       "geom_sd"         "geom_mean_sd"   
#> [25] "geom_mean_ci"    "geom_cv"         "median_ci_3d"    "mean_ci_3d"     
#> [29] "geom_mean_ci_3d"

In junco, we have added extra stats for

  • ancova: se for lsmean, combined lsmean-CI (3d stat), combined lsmean_diff-CI (3d stat)
  • analyze_vars_numeric: combined version of mean-CI, median-CI, geom_mean-CI (available in tern >= 0.9.6)
  • similar extra stats for Kaplan-Meier/survival and other methods

Tabulation Examples using a_summarize_aval_chg_diff_j 

multivars <- c("AVAL", "AVAL", "CHG")

extra_args_3col <- list(
  format_na_str = rep(NA, 3), 
  ref_path = ref_path,
  ancova = FALSE, 
  comp_btw_group = TRUE,
  multivars = multivars
)


lyt_vs_p1 <- basic_table(
  show_colcounts = FALSE, 
  colcount_format = "N=xx"
) %>%
  ### first columns
  split_cols_by("colspan_trt", 
    split_fun = trim_levels_to_map(map = colspan_trt_map), 
    show_colcounts = FALSE
  ) %>%
  split_cols_by(trtvar,
    show_colcounts = TRUE, colcount_format = "N=xx"
  ) %>%
  ## set up a 3 column split
  split_cols_by_multivar(multivars, 
    varlabels = c("n/N (%)", "Mean (95% CI)", "Mean Change From Baseline (95% CI)")
  ) %>%
  split_rows_by("PARAM", 
    label_pos = "topleft", 
    split_label = "Parameter", 
    section_div = " ", 
    split_fun = drop_split_levels
  ) %>%
  ## note the child_labels = hidden for AVISIT, these labels will be taken care off by
  ## applying function summarize_aval_chg_diff further in the layout
  split_rows_by("AVISIT", 
    label_pos = "topleft", 
    split_label = "Study Visit", 
    split_fun = drop_split_levels, 
    child_labels = "hidden"
  )


lyt_vs <- lyt_vs_p1 %>% 
  ### restart for the rrisk_header columns - note the nested = FALSE option
  ### also note the child_labels = "hidden" in both PARAM and AVISIT
  split_cols_by("rrisk_header_vs", nested = FALSE) %>%
  split_cols_by(trtvar,
    split_fun = remove_split_levels(ctrl_grp), 
    labels_var = "rrisk_label",
    show_colcounts = TRUE, 
    colcount_format = "N=xx"
  ) %>%
  ### difference columns : just 1 column & analysis needs to be done on change
  split_cols_by_multivar(multivars[3], 
    varlabels = c(" ")
  ) %>%
  ### the variable passed here in analyze is not used (STUDYID), it is a dummy var passing,
  ### the function a_summarize_aval_chg_diff_j grabs the required vars from cols_by_multivar calls
  analyze("STUDYID", 
    afun = a_summarize_aval_chg_diff_j, 
    extra_args = extra_args_3col
  )

result_vs <- build_table(lyt_vs, advs, alt_counts_df = adsl)

This is the resulting table.

 head(result_vs, 15)
#>                                                                                             Active Study Agent                                                                                                                                                           Difference in Mean Change (95% CI)          
#>                                                            A: Drug X                                                                C: Combination                                                                B: Placebo                                   A: Drug X vs B: Placebo   C: Combination vs B: Placebo
#> Parameter                                                    N=134                                                                       N=132                                                                       N=134                                              N=134                       N=132            
#>   Study Visit                  n/N (%)          Mean (95% CI)     Mean Change From Baseline (95% CI)       n/N (%)          Mean (95% CI)     Mean Change From Baseline (95% CI)       n/N (%)          Mean (95% CI)     Mean Change From Baseline (95% CI)                                                         
#> —————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Diastolic Blood Pressure                                                                                                                                                                                                                                                                                             
#>   SCREENING                134/134 (100.0%)   50.0 (48.7, 51.2)                                        132/132 (100.0%)   50.2 (48.9, 51.5)                                        134/134 (100.0%)   50.8 (49.3, 52.2)                                                                                              
#>   BASELINE                 134/134 (100.0%)   48.6 (47.2, 50.0)                                        132/132 (100.0%)   51.1 (49.8, 52.4)                                        134/134 (100.0%)   50.4 (49.1, 51.8)                                                                                              
#>   WEEK 1 DAY 8             134/134 (100.0%)   50.3 (49.0, 51.5)            1.7 (-0.2, 3.5)             132/132 (100.0%)   48.9 (47.5, 50.2)           -2.3 (-4.0, -0.5)            134/134 (100.0%)   49.7 (48.4, 51.0)            -0.8 (-2.6, 1.1)                2.4 (-0.2, 5.0)             -1.5 (-4.1, 1.1)      
#>   WEEK 2 DAY 15            134/134 (100.0%)   50.8 (49.5, 52.2)             2.2 (0.2, 4.2)             132/132 (100.0%)   50.0 (48.5, 51.4)            -1.1 (-3.1, 0.9)            134/134 (100.0%)   49.7 (48.3, 51.2)            -0.7 (-2.9, 1.4)                3.0 (0.0, 5.9)              -0.4 (-3.3, 2.5)      
#>   WEEK 3 DAY 22            134/134 (100.0%)   50.7 (49.4, 52.0)             2.1 (0.3, 4.0)             132/132 (100.0%)   49.9 (48.6, 51.3)            -1.2 (-3.0, 0.7)            134/134 (100.0%)   49.1 (47.7, 50.4)            -1.3 (-3.2, 0.5)                3.5 (0.8, 6.1)              0.2 (-2.5, 2.8)       
#>   WEEK 4 DAY 29            134/134 (100.0%)   50.1 (48.7, 51.5)            1.5 (-0.4, 3.3)             132/132 (100.0%)   49.7 (48.3, 51.1)            -1.4 (-3.3, 0.5)            134/134 (100.0%)   49.6 (48.4, 50.8)            -0.8 (-2.6, 1.0)                2.3 (-0.3, 4.9)             -0.6 (-3.2, 2.0)      
#>   WEEK 5 DAY 36            134/134 (100.0%)   50.6 (49.3, 51.9)             2.0 (0.1, 3.9)             132/132 (100.0%)   49.1 (47.8, 50.4)           -2.0 (-3.9, -0.2)            134/134 (100.0%)   48.4 (47.0, 49.7)           -2.1 (-4.1, -0.1)                4.0 (1.3, 6.8)              0.1 (-2.6, 2.8)       
#>                                                                                                                                                                                                                                                                                                                      
#> Pulse Rate                                                                                                                                                                                                                                                                                                           
#>   SCREENING                134/134 (100.0%)   49.6 (48.1, 51.1)                                        132/132 (100.0%)   49.3 (47.8, 50.8)                                        134/134 (100.0%)   49.4 (48.0, 50.8)                                                                                              
#>   BASELINE                 134/134 (100.0%)   51.9 (50.5, 53.2)                                        132/132 (100.0%)   50.3 (48.6, 51.9)                                        134/134 (100.0%)   50.3 (48.8, 51.8)                                                                                              
#>   WEEK 1 DAY 8             134/134 (100.0%)   50.1 (48.6, 51.5)            -1.8 (-3.8, 0.2)            132/132 (100.0%)   49.8 (48.5, 51.1)            -0.5 (-2.6, 1.7)            134/134 (100.0%)   49.3 (48.0, 50.6)            -1.0 (-3.0, 1.0)               -0.8 (-3.6, 2.0)             0.5 (-2.4, 3.5)       
#>   WEEK 2 DAY 15            134/134 (100.0%)   49.7 (48.2, 51.2)            -2.2 (-4.3, 0.0)            132/132 (100.0%)   49.1 (47.7, 50.4)            -1.2 (-3.1, 0.8)            134/134 (100.0%)   50.8 (49.5, 52.1)            0.6 (-1.5, 2.6)                -2.7 (-5.6, 0.2)             -1.8 (-4.5, 1.0)      
#>   WEEK 3 DAY 22            134/134 (100.0%)   50.5 (49.2, 51.7)            -1.4 (-3.3, 0.5)            132/132 (100.0%)   49.8 (48.6, 51.0)            -0.5 (-2.5, 1.6)            134/134 (100.0%)   49.9 (48.6, 51.3)            -0.4 (-2.2, 1.5)               -1.1 (-3.7, 1.6)             -0.1 (-2.9, 2.7)      
#>   WEEK 4 DAY 29            134/134 (100.0%)   49.0 (47.5, 50.4)           -2.9 (-4.8, -1.0)            132/132 (100.0%)   51.0 (49.7, 52.3)            0.7 (-1.3, 2.7)             134/134 (100.0%)   50.0 (48.5, 51.4)            -0.3 (-2.4, 1.7)               -2.6 (-5.4, 0.2)             1.0 (-1.8, 3.9)

The columns for the comparison between reference group is optional. The same table without these extra columns can be produced by specifying the argument comp_btw_group = FALSE and leaving out the 3 split_cols_by calls

  • split_cols_by(“rrisk_header_vs”)
  • split_cols_by(trtvar)
  • split_cols_by_multivar(multivars[3])
multivars <- c("AVAL", "AVAL", "CHG")

extra_args_3col <- list(
  format_na_str = rep(NA, 3), 
  ancova = FALSE, 
  comp_btw_group = FALSE,
  multivars = multivars
)

lyt_vs2 <-  lyt_vs_p1 %>% 
  ### the variable passed here in analyze is not used (STUDYID), it is a dummy var passing,
  ### the function a_summarize_aval_chg_diff_j grabs the required vars from cols_by_multivar calls
  analyze("STUDYID", 
    afun = a_summarize_aval_chg_diff_j, 
    extra_args = extra_args_3col
  )

result_vs2 <- build_table(lyt_vs2, advs, alt_counts_df = adsl)

This is the resulting table without the difference between treatment group columns.

 head(result_vs2, 15)
#>                                                                                             Active Study Agent                                                                                                                                              
#>                                                            A: Drug X                                                                C: Combination                                                                B: Placebo                                
#> Parameter                                                    N=134                                                                       N=132                                                                       N=134                                  
#>   Study Visit                  n/N (%)          Mean (95% CI)     Mean Change From Baseline (95% CI)       n/N (%)          Mean (95% CI)     Mean Change From Baseline (95% CI)       n/N (%)          Mean (95% CI)     Mean Change From Baseline (95% CI)
#> ————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
#> Diastolic Blood Pressure                                                                                                                                                                                                                                    
#>   SCREENING                134/134 (100.0%)   50.0 (48.7, 51.2)                                        132/132 (100.0%)   50.2 (48.9, 51.5)                                        134/134 (100.0%)   50.8 (49.3, 52.2)                                     
#>   BASELINE                 134/134 (100.0%)   48.6 (47.2, 50.0)                                        132/132 (100.0%)   51.1 (49.8, 52.4)                                        134/134 (100.0%)   50.4 (49.1, 51.8)                                     
#>   WEEK 1 DAY 8             134/134 (100.0%)   50.3 (49.0, 51.5)            1.7 (-0.2, 3.5)             132/132 (100.0%)   48.9 (47.5, 50.2)           -2.3 (-4.0, -0.5)            134/134 (100.0%)   49.7 (48.4, 51.0)            -0.8 (-2.6, 1.1)         
#>   WEEK 2 DAY 15            134/134 (100.0%)   50.8 (49.5, 52.2)             2.2 (0.2, 4.2)             132/132 (100.0%)   50.0 (48.5, 51.4)            -1.1 (-3.1, 0.9)            134/134 (100.0%)   49.7 (48.3, 51.2)            -0.7 (-2.9, 1.4)         
#>   WEEK 3 DAY 22            134/134 (100.0%)   50.7 (49.4, 52.0)             2.1 (0.3, 4.0)             132/132 (100.0%)   49.9 (48.6, 51.3)            -1.2 (-3.0, 0.7)            134/134 (100.0%)   49.1 (47.7, 50.4)            -1.3 (-3.2, 0.5)         
#>   WEEK 4 DAY 29            134/134 (100.0%)   50.1 (48.7, 51.5)            1.5 (-0.4, 3.3)             132/132 (100.0%)   49.7 (48.3, 51.1)            -1.4 (-3.3, 0.5)            134/134 (100.0%)   49.6 (48.4, 50.8)            -0.8 (-2.6, 1.0)         
#>   WEEK 5 DAY 36            134/134 (100.0%)   50.6 (49.3, 51.9)             2.0 (0.1, 3.9)             132/132 (100.0%)   49.1 (47.8, 50.4)           -2.0 (-3.9, -0.2)            134/134 (100.0%)   48.4 (47.0, 49.7)           -2.1 (-4.1, -0.1)         
#>                                                                                                                                                                                                                                                             
#> Pulse Rate                                                                                                                                                                                                                                                  
#>   SCREENING                134/134 (100.0%)   49.6 (48.1, 51.1)                                        132/132 (100.0%)   49.3 (47.8, 50.8)                                        134/134 (100.0%)   49.4 (48.0, 50.8)                                     
#>   BASELINE                 134/134 (100.0%)   51.9 (50.5, 53.2)                                        132/132 (100.0%)   50.3 (48.6, 51.9)                                        134/134 (100.0%)   50.3 (48.8, 51.8)                                     
#>   WEEK 1 DAY 8             134/134 (100.0%)   50.1 (48.6, 51.5)            -1.8 (-3.8, 0.2)            132/132 (100.0%)   49.8 (48.5, 51.1)            -0.5 (-2.6, 1.7)            134/134 (100.0%)   49.3 (48.0, 50.6)            -1.0 (-3.0, 1.0)         
#>   WEEK 2 DAY 15            134/134 (100.0%)   49.7 (48.2, 51.2)            -2.2 (-4.3, 0.0)            132/132 (100.0%)   49.1 (47.7, 50.4)            -1.2 (-3.1, 0.8)            134/134 (100.0%)   50.8 (49.5, 52.1)            0.6 (-1.5, 2.6)          
#>   WEEK 3 DAY 22            134/134 (100.0%)   50.5 (49.2, 51.7)            -1.4 (-3.3, 0.5)            132/132 (100.0%)   49.8 (48.6, 51.0)            -0.5 (-2.5, 1.6)            134/134 (100.0%)   49.9 (48.6, 51.3)            -0.4 (-2.2, 1.5)         
#>   WEEK 4 DAY 29            134/134 (100.0%)   49.0 (47.5, 50.4)           -2.9 (-4.8, -1.0)            132/132 (100.0%)   51.0 (49.7, 52.3)            0.7 (-1.3, 2.7)             134/134 (100.0%)   50.0 (48.5, 51.4)            -0.3 (-2.4, 1.7)