AET04_PI

Adverse Events Reported in \(\geq\) 10% of Patients by Highest NCI CTCAE Grade

---

Standard Table

Preview

full_table <- full_table_aet04_pi(adsl, adae_max) %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),
    decreasing = TRUE
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),
    decreasing = TRUE
  )

at_least_10percent_any <- has_fraction_in_any_col(atleast = 0.1, col_indices = c(1, 4, 7))

result <- full_table %>%
  trim_rows(criteria = criteria_fun) %>%
  prune_table(keep_rows(at_least_10percent_any))

result

                                             A: Drug X                                    B: Placebo                                  C: Combination               
MedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)
  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
cl A.1                                                                                                                                                             
dcd A.1.1.1.1                    37               0              0             34               0              0             48               0              0     
dcd A.1.1.1.2                    36               0              0             36               0              0             38               0              0     
cl B.2                                                                                                                                                             
dcd B.2.2.3.1                    36               0              0             40               0              0             39               0              0     
dcd B.2.1.2.1                    37              37              0             33              33              0             39              39              0     
cl D.1                                                                                                                                                             
dcd D.1.1.1.1                    37               0             37             31               0             31             39               0             39     
dcd D.1.1.4.2                    36              36              0             31              31              0             38              38              0     
cl D.2                                                                                                                                                             
dcd D.2.1.5.3                    35               0              0             43               0              0             43               0              0     
cl B.1                                                                                                                                                             
dcd B.1.1.1.1                    35               0             35             37               0             37             33               0             33     
cl C.2                                                                                                                                                             
dcd C.2.1.2.1                    26               0              0             36               0              0             42               0              0     
cl C.1                                                                                                                                                             
dcd C.1.1.1.3                    32              32              0             34              34              0             33              33              0     

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Table with Patients Treated with a Particular Treatment by Highest NCI CTCAE Grade (specifying a treatment for selecting preferred terms)

Note: User needs to specify the column index for filtering the table. The current example uses the “Any Grade” column for treatment A: Drug X with a filtering threshold at 0.37; AEs reported in greater than 37% of patients in treatment A: Drug X are shown. This can be changed by varying the parameter values in the has_fraction_in_cols function.

Preview

full_table <- full_table_aet04_pi(adsl, adae_max) %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),
    decreasing = TRUE
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),
    decreasing = TRUE
  )

at_least_37percent_any_drugx <- has_fraction_in_cols(atleast = 0.37, col_indices = 1)

result <- full_table %>%
  trim_rows(criteria = criteria_fun) %>%
  prune_table(keep_rows(at_least_37percent_any_drugx))

result

                                             A: Drug X                                    B: Placebo                                  C: Combination               
MedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)
  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
cl A.1                                                                                                                                                             
dcd A.1.1.1.1                    37               0              0             34               0              0             48               0              0     
cl D.1                                                                                                                                                             
dcd D.1.1.1.1                    37               0             37             31               0             31             39               0             39     

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Adverse Events Reported in \(\geq\) 5% of Patients by Highest NCI CTCAE Grade (changing the threshold)

Note: User needs to specify the column index for filtering the table. The current example uses column indices 1, 4, and 7 with a filtering threshold at 0.40 to demonstrate the filtering ability; AEs reported in greater than 40% of patients are shown. This can be changed by varying the parameter values in the has_fraction_in_any_col function.

Preview

full_table <- full_table_aet04_pi(adsl, adae_max) %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),
    decreasing = TRUE
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),
    decreasing = TRUE
  )

at_least_40percent_any <- has_fraction_in_any_col(atleast = 0.40, col_indices = c(1, 4, 7))

result <- full_table %>%
  trim_rows(criteria = criteria_fun) %>%
  prune_table(keep_rows(at_least_40percent_any))

result

                                             A: Drug X                                    B: Placebo                                  C: Combination               
MedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)
  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
cl A.1                                                                                                                                                             
dcd A.1.1.1.1                    37               0              0             34               0              0             48               0              0     
cl B.2                                                                                                                                                             
dcd B.2.2.3.1                    36               0              0             40               0              0             39               0              0     
cl D.2                                                                                                                                                             
dcd D.2.1.5.3                    35               0              0             43               0              0             43               0              0     
cl C.2                                                                                                                                                             
dcd C.2.1.2.1                    26               0              0             36               0              0             42               0              0     

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Adverse Events Reported in \(\geq\) 5% of Patients and \(\geq\) 2% Difference Between Treatments by Highest NCI CTCAE Grade (using more than one condition)

Note: User needs to specify the column index for filtering the table. The current example uses column indices 1, 4, and 7 to filter for AEs reported in \(\geq\) 30% of patients and AEs reported with a \(\geq\) 15% difference between treatments to demonstrate the filtering ability; this can be changed by varying the parameter values in the has_fraction_in_any_col and has_fractions_difference functions.

Preview

full_table <- full_table_aet04_pi(adsl, adae_max) %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),
    decreasing = TRUE
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),
    decreasing = TRUE
  )

at_least_30percent_any <- has_fraction_in_any_col(atleast = 0.3, col_indices = c(1, 4, 7))

at_least_15percent_diff <- has_fractions_difference(atleast = 0.15, col_indices = c(1, 4, 7))

result <- full_table %>%
  trim_rows(criteria = criteria_fun) %>%
  prune_table(keep_rows(at_least_30percent_any & at_least_15percent_diff))

result

                                             A: Drug X                                    B: Placebo                                  C: Combination               
MedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)
  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
cl C.2                                                                                                                                                             
dcd C.2.1.2.1                    26               0              0             36               0              0             42               0              0     

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Adverse Events Reported in \(\geq\) 10% of Patients for Any Grade or \(\geq\) 5% of Patients for Grade 3-4 by Highest NCI CTCAE Grade (using different condition depending on the grade)

Note: User needs to specify the column index for filtering the table. The current example filters using column indices 1, 4, and 7 to include AEs reported in \(\geq\) 40% of patients and using column indices 2, 5, and 8 to include grade 3-4 AEs reported in \(\geq\) 20% of patients. These thresholds are chosen to demonstrate the filtering ability; they can be changed by varying parameter values in the has_fraction_in_any_col function.

Preview

full_table <- full_table_aet04_pi(adsl, adae_max) %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = score_occurrences_cont_cols(col_indices = c(1, 4, 7)),
    decreasing = TRUE
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),
    decreasing = TRUE
  )

at_least_40percent_any <- has_fraction_in_any_col(atleast = 0.4, col_indices = c(1, 4, 7))

at_least_20percent_g34 <- has_fraction_in_any_col(atleast = 0.20, col_indices = c(2, 5, 8))

result <- full_table %>%
  trim_rows(criteria = criteria_fun) %>%
  prune_table(keep_rows(at_least_40percent_any | at_least_20percent_g34))

result

                                             A: Drug X                                    B: Placebo                                  C: Combination               
MedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)
  MedDRA Preferred Term        (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
cl A.1                                                                                                                                                             
dcd A.1.1.1.1                    37               0              0             34               0              0             48               0              0     
cl B.2                                                                                                                                                             
dcd B.2.2.3.1                    36               0              0             40               0              0             39               0              0     
dcd B.2.1.2.1                    37              37              0             33              33              0             39              39              0     
cl D.1                                                                                                                                                             
dcd D.1.1.4.2                    36              36              0             31              31              0             38              38              0     
cl D.2                                                                                                                                                             
dcd D.2.1.5.3                    35               0              0             43               0              0             43               0              0     
cl C.2                                                                                                                                                             
dcd C.2.1.2.1                    26               0              0             36               0              0             42               0              0     
cl C.1                                                                                                                                                             
dcd C.1.1.1.3                    32              32              0             34              34              0             33              33              0     

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Experimental use!

WebR is a tool allowing you to run R code in the web browser. Modify the code below and click run to see the results. Alternatively, copy the code and click here to open WebR in a new tab.

Standard Table with Modified Grade Grouping

Note: User can specify/modify the grouping of grades, as shown in this example. In addition, the user needs to specify the column index for filtering the table. The current example uses the “Any Grade” columns with a filtering threshold at 0.40; this can be changed by varying the parameter values in the has_fraction_in_any_col function.

Preview

grade_groups <- list(
  "Any Grade (%)" = c("1", "2", "3", "4", "5"),
  "Grade 1-2 (%)" = c("1", "2"),
  "Grade 3-4 (%)" = c("3", "4"),
  "Grade 5 (%)" = "5"
)

col_counts <- rep(table(adsl$ACTARM), each = length(grade_groups))

full_table <- basic_table() %>%
  split_cols_by("ACTARM") %>%
  split_cols_by_groups("MAXAETOXGR", groups_list = grade_groups) %>%
  split_rows_by("AEBODSYS",
    child_labels = "visible", nested = FALSE, indent_mod = -1L,
    split_fun = trim_levels_in_group("AEDECOD")
  ) %>%
  append_topleft("MedDRA System Organ Class") %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = "unique",
    .labels = "Total number of patients with at least one adverse event"
  ) %>%
  analyze_vars(
    "AEDECOD",
    na.rm = FALSE,
    denom = "N_col",
    .stats = "count_fraction",
    .formats = c(count_fraction = format_fraction_threshold(0.01))
  ) %>%
  append_topleft("  MedDRA Preferred Term") %>%
  build_table(adae_max, col_counts = col_counts) %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = score_occurrences_cont_cols(col_indices = c(1, 5, 9)),
    decreasing = TRUE
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences_cols(col_indices = c(1, 5, 9)),
    decreasing = TRUE
  )

at_least_40percent_any <- has_fraction_in_any_col(atleast = 0.4, col_indices = c(1, 5, 9))

result <- full_table %>%
  trim_rows(criteria = criteria_fun) %>%
  prune_table(keep_rows(at_least_40percent_any))

result

                                                     A: Drug X                                                    B: Placebo                                                  C: Combination                       
MedDRA System Organ Class   Any Grade (%)   Grade 1-2 (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 1-2 (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 1-2 (%)   Grade 3-4 (%)   Grade 5 (%)
  MedDRA Preferred Term        (N=134)         (N=134)         (N=134)        (N=134)        (N=134)         (N=134)         (N=134)        (N=134)        (N=132)         (N=132)         (N=132)        (N=132)  
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
cl A.1                                                                                                                                                                                                             
dcd A.1.1.1.1                    37              37               0              0             34              34               0              0             48              48               0              0     
cl B.2                                                                                                                                                                                                             
dcd B.2.2.3.1                    36              36               0              0             40              40               0              0             39              39               0              0     
cl D.2                                                                                                                                                                                                             
dcd D.2.1.5.3                    35              35               0              0             43              43               0              0             43              43               0              0     
cl C.2                                                                                                                                                                                                             
dcd C.2.1.2.1                    26              26               0              0             36              36               0              0             42              42               0              0     

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Experimental use!

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Standard Table with Overlapping Grade Groupings

Note: User needs to specify the column index for filtering the table. The current example uses column indices 1, 5, and 9 with a filtering threshold at 0.40 to demonstrate the filtering ability; any grade AEs reported in greater than 40% of patients are shown. This can be changed by varying the parameter values in the has_fraction_in_any_col function.

Preview

grade_groups <- list(
  "Any Grade (%)" = c("1", "2", "3", "4", "5"),
  "Grade 3-4 (%)" = c("3", "4"),
  "Grade 3-5 (%)" = c("3", "4", "5"),
  "Grade 5 (%)" = "5"
)

col_counts <- rep(table(adsl$ACTARM), each = length(grade_groups))
full_table <- basic_table() %>%
  split_cols_by("ACTARM") %>%
  split_cols_by_groups("MAXAETOXGR", groups_list = grade_groups) %>%
  split_rows_by("AEBODSYS",
    child_labels = "visible", nested = FALSE, indent_mod = -1L,
    split_fun = trim_levels_in_group("AEDECOD")
  ) %>%
  append_topleft("MedDRA System Organ Class") %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = "unique",
    .labels = "Total number of patients with at least one adverse event"
  ) %>%
  analyze_vars(
    "AEDECOD",
    na.rm = FALSE,
    denom = "N_col",
    .stats = "count_fraction",
    .formats = c(count_fraction = format_fraction_threshold(0.01))
  ) %>%
  append_topleft("  MedDRA Preferred Term") %>%
  build_table(adae_max, col_counts = col_counts) %>%
  sort_at_path(
    path = c("AEBODSYS"),
    scorefun = score_occurrences_cont_cols(col_indices = c(1, 5, 9)),
    decreasing = TRUE
  ) %>%
  sort_at_path(
    path = c("AEBODSYS", "*", "AEDECOD"),
    scorefun = score_occurrences_cols(col_indices = c(1, 5, 9)),
    decreasing = TRUE
  )

at_least_40percent_any <- has_fraction_in_any_col(atleast = 0.4, col_indices = c(1, 5, 9))

result <- full_table %>%
  trim_rows(criteria = criteria_fun) %>%
  prune_table(keep_rows(at_least_40percent_any))

result

                                                     A: Drug X                                                    B: Placebo                                                  C: Combination                       
MedDRA System Organ Class   Any Grade (%)   Grade 3-4 (%)   Grade 3-5 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 3-5 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 3-5 (%)   Grade 5 (%)
  MedDRA Preferred Term        (N=134)         (N=134)         (N=134)        (N=134)        (N=134)         (N=134)         (N=134)        (N=134)        (N=132)         (N=132)         (N=132)        (N=132)  
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
cl A.1                                                                                                                                                                                                             
dcd A.1.1.1.1                    37               0               0              0             34               0               0              0             48               0               0              0     
cl B.2                                                                                                                                                                                                             
dcd B.2.2.3.1                    36               0               0              0             40               0               0              0             39               0               0              0     
cl D.2                                                                                                                                                                                                             
dcd D.2.1.5.3                    35               0               0              0             43               0               0              0             43               0               0              0     
cl C.2                                                                                                                                                                                                             
dcd C.2.1.2.1                    26               0               0              0             36               0               0              0             42               0               0              0     

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Experimental use!

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Standard Table without SOCs

Note: User needs to specify the column index for filtering the table. The current example uses column indices 1, 4, and 7 with a filtering threshold at 0.20 to demonstrate the filtering ability; any grade AEs (Preferred Terms Only) reported in greater than 20% of patients are shown. This can be changed by varying the parameter values in the has_fraction_in_any_col function.

Preview

grade_groups <- list(
  "Any Grade (%)" = c("1", "2", "3", "4", "5"),
  "Grade 3-4 (%)" = c("3", "4"),
  "Grade 5 (%)" = "5"
)

col_counts <- rep(table(adsl$ACTARM), each = length(grade_groups))

full_table <- basic_table() %>%
  split_cols_by("ACTARM") %>%
  split_cols_by_groups("MAXAETOXGR", groups_list = grade_groups) %>%
  analyze_vars(
    "AEDECOD",
    na.rm = FALSE,
    denom = "N_col",
    .stats = "count_fraction",
    .formats = c(count_fraction = format_fraction_threshold(0.01))
  ) %>%
  append_topleft("MedDRA Preferred Term") %>%
  build_table(adae_max, col_counts = col_counts)

at_least_20percent_any <- has_fraction_in_any_col(atleast = 0.2, col_indices = c(1, 4, 7))

result <- full_table %>%
  prune_table(keep_rows(at_least_20percent_any)) %>%
  sort_at_path(
    path = c("AEDECOD"),
    scorefun = score_occurrences_cols(col_indices = c(1, 4, 7)),
    decreasing = TRUE
  )

result

                                         A: Drug X                                    B: Placebo                                  C: Combination               
                        Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)   Any Grade (%)   Grade 3-4 (%)   Grade 5 (%)
MedDRA Preferred Term      (N=134)         (N=134)        (N=134)        (N=134)         (N=134)        (N=134)        (N=132)         (N=132)        (N=132)  
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
dcd D.2.1.5.3                35               0              0             43               0              0             43               0              0     
dcd A.1.1.1.1                37               0              0             34               0              0             48               0              0     
dcd B.2.2.3.1                36               0              0             40               0              0             39               0              0     
dcd A.1.1.1.2                36               0              0             36               0              0             38               0              0     
dcd B.2.1.2.1                37              37              0             33              33              0             39              39              0     
dcd D.1.1.1.1                37               0             37             31               0             31             39               0             39     
dcd D.1.1.4.2                36              36              0             31              31              0             38              38              0     
dcd B.1.1.1.1                35               0             35             37               0             37             33               0             33     
dcd C.2.1.2.1                26               0              0             36               0              0             42               0              0     
dcd C.1.1.1.3                32              32              0             34              34              0             33              33              0     

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Data Setup

library(dplyr)
library(tern)

preprocess_adae <- function(adae) {
  adae %>%
    dplyr::group_by(ACTARM, USUBJID, AEBODSYS, AEDECOD) %>%
    dplyr::summarize(
      MAXAETOXGR = max(as.numeric(AETOXGR)),
      .groups = "drop"
    ) %>%
    dplyr::ungroup() %>%
    dplyr::mutate(
      MAXAETOXGR = factor(MAXAETOXGR),
      AEDECOD = droplevels(as.factor(AEDECOD))
    )
}

full_table_aet04_pi <- function(adsl, adae_max) {
  grade_groups <- list(
    "Any Grade (%)" = c("1", "2", "3", "4", "5"),
    "Grade 3-4 (%)" = c("3", "4"),
    "Grade 5 (%)" = "5"
  )

  col_counts <- rep(table(adsl$ACTARM), each = length(grade_groups))
  basic_table() %>%
    split_cols_by("ACTARM") %>%
    split_cols_by_groups("MAXAETOXGR", groups_list = grade_groups) %>%
    split_rows_by("AEBODSYS",
      child_labels = "visible", nested = FALSE, indent_mod = -1L,
      split_fun = trim_levels_in_group("AEDECOD")
    ) %>%
    append_topleft("MedDRA System Organ Class") %>%
    summarize_num_patients(
      var = "USUBJID",
      .stats = "unique",
      .labels = "Total number of patients with at least one adverse event"
    ) %>%
    analyze_vars(
      "AEDECOD",
      na.rm = FALSE,
      denom = "N_col",
      .stats = "count_fraction",
      .formats = c(count_fraction = format_fraction_threshold(0.01))
    ) %>%
    append_topleft("  MedDRA Preferred Term") %>%
    build_table(adae_max, col_counts = col_counts)
}

criteria_fun <- function(tr) {
  is(tr, "ContentRow")
}

adsl <- random.cdisc.data::cadsl
adae_max <- random.cdisc.data::cadae %>%
  preprocess_adae() %>%
  df_explicit_na()

teal App

Preview

library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- random.cdisc.data::cadsl
  ADAE <- random.cdisc.data::cadae
})
datanames <- c("ADSL", "ADAE")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADAE <- data[["ADAE"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_events_by_grade(
      label = "Adverse Events by Grade Table",
      dataname = "ADAE",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      col_by_grade = TRUE,
      llt = choices_selected(
        choices = variable_choices(ADAE, c("AETERM", "AEDECOD")),
        selected = c("AEDECOD")
      ),
      hlt = choices_selected(
        choices = variable_choices(ADAE, c("AEBODSYS", "AESOC")),
        selected = "AEBODSYS"
      ),
      grade = choices_selected(
        choices = variable_choices(ADAE, c("AETOXGR", "AESEV")),
        selected = "AETOXGR"
      ),
      # List of column groups for grades used when `col_by_grade` = TRUE.
      grading_groups = list(
        `Any Grade (%)` = c("1", "2", "3", "4", "5"),
        `Grade 3-4 (%)` = c("3", "4"),
        `Grade 5 (%)` = "5"
      )
    )
  )
)

shinyApp(app$ui, app$server)

Try this using shinylive

Experimental use!

shinylive allow you to modify to run shiny application entirely in the web browser. Modify the code below and click re-run the app to see the results. The performance is slighly worse and some of the features (e.g. downloading) might not work at all.

#| standalone: true
#| viewerHeight: 800
#| editorHeight: 200
#| components: [viewer, editor]
#| layout: vertical

# -- WEBR HELPERS --
options(webr_pkg_repos = c("r-universe" = "https://pharmaverse.r-universe.dev", getOption("webr_pkg_repos")))

# -- APP CODE --
library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- random.cdisc.data::cadsl
  ADAE <- random.cdisc.data::cadae
})
datanames <- c("ADSL", "ADAE")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

## Reusable Configuration For Modules
ADAE <- data[["ADAE"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_events_by_grade(
      label = "Adverse Events by Grade Table",
      dataname = "ADAE",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      col_by_grade = TRUE,
      llt = choices_selected(
        choices = variable_choices(ADAE, c("AETERM", "AEDECOD")),
        selected = c("AEDECOD")
      ),
      hlt = choices_selected(
        choices = variable_choices(ADAE, c("AEBODSYS", "AESOC")),
        selected = "AEBODSYS"
      ),
      grade = choices_selected(
        choices = variable_choices(ADAE, c("AETOXGR", "AESEV")),
        selected = "AETOXGR"
      ),
      # List of column groups for grades used when `col_by_grade` = TRUE.
      grading_groups = list(
        `Any Grade (%)` = c("1", "2", "3", "4", "5"),
        `Grade 3-4 (%)` = c("3", "4"),
        `Grade 5 (%)` = "5"
      )
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 18:12:15 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14)
 os       Ubuntu 22.04.5 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2024-11-02
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version     date (UTC) lib source
 backports               1.5.0       2024-05-23 [1] CRAN (R 4.4.1)
 brio                    1.1.5       2024-04-24 [1] CRAN (R 4.4.1)
 broom                   1.0.7       2024-09-26 [1] CRAN (R 4.4.1)
 bslib                   0.8.0       2024-07-29 [1] CRAN (R 4.4.1)
 cachem                  1.1.0       2024-05-16 [1] CRAN (R 4.4.1)
 callr                   3.7.6       2024-03-25 [1] CRAN (R 4.4.1)
 checkmate               2.3.2       2024-07-29 [1] CRAN (R 4.4.1)
 chromote                0.3.1       2024-08-30 [1] CRAN (R 4.4.1)
 cli                     3.6.3       2024-06-21 [1] CRAN (R 4.4.1)
 coda                    0.19-4.1    2024-01-31 [1] CRAN (R 4.4.1)
 codetools               0.2-20      2024-03-31 [2] CRAN (R 4.4.1)
 colorspace              2.1-1       2024-07-26 [1] CRAN (R 4.4.1)
 digest                  0.6.37      2024-08-19 [1] CRAN (R 4.4.1)
 dplyr                 * 1.1.4       2023-11-17 [1] CRAN (R 4.4.1)
 emmeans                 1.10.5      2024-10-14 [1] CRAN (R 4.4.1)
 estimability            1.5.1       2024-05-12 [1] CRAN (R 4.4.1)
 evaluate                1.0.1       2024-10-10 [1] CRAN (R 4.4.1)
 fansi                   1.0.6       2023-12-08 [1] CRAN (R 4.4.1)
 fastmap                 1.2.0       2024-05-15 [1] CRAN (R 4.4.1)
 fontawesome             0.5.2       2023-08-19 [1] CRAN (R 4.4.1)
 forcats                 1.0.0       2023-01-29 [1] CRAN (R 4.4.1)
 formatR                 1.14        2023-01-17 [1] CRAN (R 4.4.1)
 formatters            * 0.5.9.9003  2024-11-02 [1] git2r (https://github.com/insightsengineering/formatters.git@84e8c3e)
 geepack                 1.3.12      2024-09-23 [1] CRAN (R 4.4.1)
 generics                0.1.3       2022-07-05 [1] CRAN (R 4.4.1)
 ggplot2                 3.5.1       2024-04-23 [1] CRAN (R 4.4.1)
 glue                    1.8.0       2024-09-30 [1] CRAN (R 4.4.1)
 gtable                  0.3.5       2024-04-22 [1] CRAN (R 4.4.1)
 htmltools               0.5.8.1     2024-04-04 [1] CRAN (R 4.4.1)
 htmlwidgets             1.6.4       2023-12-06 [1] CRAN (R 4.4.1)
 httpuv                  1.6.15      2024-03-26 [1] CRAN (R 4.4.1)
 jquerylib               0.1.4       2021-04-26 [1] CRAN (R 4.4.1)
 jsonlite                1.8.9       2024-09-20 [1] CRAN (R 4.4.1)
 knitr                   1.48        2024-07-07 [1] CRAN (R 4.4.1)
 later                   1.3.2       2023-12-06 [1] CRAN (R 4.4.1)
 lattice                 0.22-6      2024-03-20 [2] CRAN (R 4.4.1)
 lifecycle               1.0.4       2023-11-07 [1] CRAN (R 4.4.1)
 logger                  0.3.0       2024-03-05 [1] CRAN (R 4.4.1)
 magrittr              * 2.0.3       2022-03-30 [1] CRAN (R 4.4.1)
 MASS                    7.3-61      2024-06-13 [2] CRAN (R 4.4.1)
 Matrix                  1.7-0       2024-04-26 [1] CRAN (R 4.4.1)
 memoise                 2.0.1       2021-11-26 [1] CRAN (R 4.4.1)
 mime                    0.12        2021-09-28 [1] CRAN (R 4.4.1)
 multcomp                1.4-26      2024-07-18 [1] CRAN (R 4.4.1)
 munsell                 0.5.1       2024-04-01 [1] CRAN (R 4.4.1)
 mvtnorm                 1.3-1       2024-09-03 [1] CRAN (R 4.4.1)
 nestcolor               0.1.2.9017  2024-11-02 [1] git2r (https://github.com/insightsengineering/nestcolor.git@3d2ce62)
 nlme                    3.1-166     2024-08-14 [2] CRAN (R 4.4.1)
 pillar                  1.9.0       2023-03-22 [1] CRAN (R 4.4.1)
 pkgconfig               2.0.3       2019-09-22 [1] CRAN (R 4.4.1)
 processx                3.8.4       2024-03-16 [1] CRAN (R 4.4.1)
 promises                1.3.0       2024-04-05 [1] CRAN (R 4.4.1)
 ps                      1.8.0       2024-09-12 [1] CRAN (R 4.4.1)
 purrr                   1.0.2       2023-08-10 [1] CRAN (R 4.4.1)
 R6                      2.5.1       2021-08-19 [1] CRAN (R 4.4.1)
 random.cdisc.data       0.3.16.9001 2024-11-02 [1] git2r (https://github.com/insightsengineering/random.cdisc.data.git@a65bfbe)
 rbibutils               2.3         2024-10-04 [1] CRAN (R 4.4.1)
 Rcpp                    1.0.13      2024-07-17 [1] CRAN (R 4.4.1)
 Rdpack                  2.6.1       2024-08-06 [1] CRAN (R 4.4.1)
 rlang                   1.1.4       2024-06-04 [1] CRAN (R 4.4.1)
 rmarkdown               2.28        2024-08-17 [1] CRAN (R 4.4.1)
 rtables               * 0.6.10.9002 2024-11-02 [1] git2r (https://github.com/insightsengineering/rtables.git@d2cc7a0)
 sandwich                3.1-1       2024-09-15 [1] CRAN (R 4.4.1)
 sass                    0.4.9       2024-03-15 [1] CRAN (R 4.4.1)
 scales                  1.3.0       2023-11-28 [1] CRAN (R 4.4.1)
 sessioninfo             1.2.2       2021-12-06 [1] CRAN (R 4.4.1)
 shiny                 * 1.9.1       2024-08-01 [1] CRAN (R 4.4.1)
 shinycssloaders         1.1.0       2024-07-30 [1] CRAN (R 4.4.1)
 shinyjs                 2.1.0       2021-12-23 [1] CRAN (R 4.4.1)
 shinyvalidate           0.1.3       2023-10-04 [1] CRAN (R 4.4.1)
 shinyWidgets            0.8.7       2024-09-23 [1] CRAN (R 4.4.1)
 stringi                 1.8.4       2024-05-06 [1] CRAN (R 4.4.1)
 stringr                 1.5.1       2023-11-14 [1] CRAN (R 4.4.1)
 survival                3.7-0       2024-06-05 [2] CRAN (R 4.4.1)
 teal                  * 0.15.2.9079 2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.git@53c423d)
 teal.code             * 0.5.0.9012  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.code.git@86aa265)
 teal.data             * 0.6.0.9015  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.data.git@eca9695)
 teal.logger             0.3.0.9000  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.logger.git@cd9cc7e)
 teal.modules.clinical * 0.9.1.9030  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.modules.clinical.git@5cf76ac)
 teal.reporter           0.3.1.9016  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.reporter.git@4fefa7e)
 teal.slice            * 0.5.1.9015  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.slice.git@25f4101)
 teal.transform        * 0.5.0.9015  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.transform.git@0b855e3)
 teal.widgets            0.4.2.9022  2024-11-02 [1] git2r (https://github.com/insightsengineering/teal.widgets.git@8a39dd4)
 tern                  * 0.9.6.9013  2024-11-02 [1] git2r (https://github.com/insightsengineering/tern.git@435fd51)
 tern.gee                0.1.5.9004  2024-11-02 [1] git2r (https://github.com/insightsengineering/tern.gee.git@458172d)
 testthat                3.2.1.1     2024-04-14 [1] CRAN (R 4.4.1)
 TH.data                 1.1-2       2023-04-17 [1] CRAN (R 4.4.1)
 tibble                  3.2.1       2023-03-20 [1] CRAN (R 4.4.1)
 tidyr                   1.3.1       2024-01-24 [1] CRAN (R 4.4.1)
 tidyselect              1.2.1       2024-03-11 [1] CRAN (R 4.4.1)
 utf8                    1.2.4       2023-10-22 [1] CRAN (R 4.4.1)
 vctrs                   0.6.5       2023-12-01 [1] CRAN (R 4.4.1)
 webshot                 0.5.5       2023-06-26 [1] CRAN (R 4.4.1)
 webshot2                0.1.1       2023-08-11 [1] CRAN (R 4.4.1)
 websocket               1.4.2       2024-07-22 [1] CRAN (R 4.4.1)
 withr                   3.0.1       2024-07-31 [1] CRAN (R 4.4.1)
 xfun                    0.48        2024-10-03 [1] CRAN (R 4.4.1)
 xtable                  1.8-4       2019-04-21 [1] CRAN (R 4.4.1)
 yaml                    2.3.10      2024-07-26 [1] CRAN (R 4.4.1)
 zoo                     1.8-12      2023-04-13 [1] CRAN (R 4.4.1)

 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

──────────────────────────────────────────────────────────────────────────────

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