DISCLOSUREST01

Disclosures Outputs

---

Patient Disposition Table (CTgov & EudraCT)

Data Setup

library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)

set.seed(1, kind = "Mersenne-Twister")

# Add additional disposition variables to adsl.
adsl0 <- adsl %>%
  mutate(
    STSTFL = case_when(
      is.na(RANDDT) ~ "N",
      TRUE ~ "Y"
    ) %>% as.factor(),
    COMPSTUD = sample(
      c("Y", "N"),
      size = nrow(adsl),
      replace = TRUE
    ) %>% as.factor(),
    STUDONS = sample(
      c("Alive: On Treatment", "Alive: In Follow-up", NA),
      size = nrow(adsl),
      replace = TRUE
    ) %>% as.factor(),
    STDDRS = sample(
      c(
        "Death", "Lost To Follow-Up",
        "Protocol Violation", "Withdrawal By Subject",
        "Other"
      ),
      size = nrow(adsl),
      replace = TRUE
    ) %>% as.factor(),
    GOTTRT = ifelse(!is.na(ACTARMCD), "Y", "N") %>%
      as.factor(),
    DISTRTFL = sample(
      c("Y", "N"),
      size = nrow(adsl),
      replace = TRUE
    ) %>% as.factor(),
    TRTDRS = sample(
      c(
        "ADVERSE EVENT", "PROGRESSIVE DISEASE",
        "PHYSICIAN DECISION", "LACK OF EFFICACY",
        "OTHER"
      ),
      size = nrow(adsl),
      replace = TRUE
    ) %>% as.factor(),
    STUDONS = case_when(COMPSTUD == "N" ~ STUDONS),
    STDDRS = case_when(COMPSTUD == "N" & is.na(STUDONS) ~ STDDRS),
    DISSTDFL = case_when(!is.na(STDDRS) ~ "Y"),
    DISTRTFL = case_when(GOTTRT == "Y" ~ DISTRTFL),
    TRTDRS = case_when(DISTRTFL == "Y" ~ TRTDRS),
    DRSCAT = case_when(
      TRTDRS %in% c("ADVERSE EVENT", "PHYSICIAN DECISION") ~ "Safety",
      !is.na(TRTDRS) ~ "Other"
    )
  ) %>%
  var_relabel(
    STSTFL = "Started Study",
    COMPSTUD = "Complete Study",
    STUDONS = "On-study Status",
    DISSTDFL = "Discontinued Study",
    STDDRS = "Reason for Study Discontinuation",
    GOTTRT = "Received Treatment",
    DISTRTFL = "Discontinued Treatment",
    TRTDRS = "Reason for Treatment Discontinuation",
    DRSCAT = "Subcategory for Treatment Discontinuation"
  )

Patient Disposition Table

Preview

# Define the split function
split_fun <- keep_split_levels("Y")

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(
    "ARM",
    split_fun = add_overall_level("All Patients", first = FALSE)
  ) %>%
  count_values("STSTFL",
    values = "Y",
    .labels = c(count_fraction = "Started Study")
  ) %>%
  count_values("COMPSTUD",
    values = "Y",
    .labels = c(count_fraction = "Completed Study")
  ) %>%
  split_rows_by(
    "DISSTDFL",
    split_fun = split_fun
  ) %>%
  summarize_row_groups(label_fstr = "Discontinued Study") %>%
  analyze_vars(
    "STDDRS",
    .stats = "count_fraction"
  ) %>%
  append_topleft("Status")

result <- build_table(lyt = lyt, df = adsl0)
result

                            A: Drug X      B: Placebo     C: Combination   All Patients 
Status                       (N=134)         (N=134)         (N=132)          (N=400)   
————————————————————————————————————————————————————————————————————————————————————————
Started Study             134 (100.00%)   134 (100.00%)   132 (100.00%)    400 (100.00%)
Completed Study            65 (48.51%)     67 (50.00%)     69 (52.27%)     201 (50.25%) 
Discontinued Study         15 (11.2%)      28 (20.9%)       24 (18.2%)      67 (16.8%)  
  Death                      6 (40%)        5 (17.9%)       4 (16.7%)       15 (22.4%)  
  Lost To Follow-Up         1 (6.7%)        3 (10.7%)       8 (33.3%)       12 (17.9%)  
  Other                     2 (13.3%)       6 (21.4%)       5 (20.8%)       13 (19.4%)  
  Protocol Violation        4 (26.7%)       4 (14.3%)       4 (16.7%)       12 (17.9%)  
  Withdrawal By Subject     2 (13.3%)      10 (35.7%)       3 (12.5%)       15 (22.4%)  

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Demographic Table (CTgov & EudraCT)

Data Setup

library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)

# Change description in variable SEX.
adsl <- adsl %>%
  mutate(
    SEX = factor(case_when(
      SEX == "M" ~ "Male",
      SEX == "F" ~ "Female",
      SEX == "U" ~ "Unknown",
      SEX == "UNDIFFERENTIATED" ~ "Undifferentiated"
    )),
    AGEGRP = factor(
      case_when(
        between(AGE, 18, 40) ~ "18-40",
        between(AGE, 41, 64) ~ "41-64",
        AGE > 64 ~ ">=65"
      ),
      levels = c("18-40", "41-64", ">=65")
    )
  )

Demographic Table

Preview

vars <- c("AGE", "AGEGRP", "SEX", "RACE", "ETHNIC")
var_labels <- c("Age (yr)", "Age group", "Sex", "Race", "Ethnicity")

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  add_overall_col("All Patients") %>%
  analyze_vars(
    vars = vars,
    var_labels = var_labels
  )

result <- build_table(lyt = lyt, df = adsl)
result

                                               A: Drug X    B: Placebo    C: Combination   All Patients
                                                (N=134)       (N=134)        (N=132)         (N=400)   
———————————————————————————————————————————————————————————————————————————————————————————————————————
Age (yr)                                                                                               
  n                                               134           134            132             400     
  Mean (SD)                                   33.8 (6.6)    35.4 (7.9)      35.4 (7.7)      34.9 (7.4) 
  Median                                         33.0          35.0            35.0            34.0    
  Min - Max                                   21.0 - 50.0   21.0 - 62.0    20.0 - 69.0     20.0 - 69.0 
Age group                                                                                              
  n                                               134           134            132             400     
  18-40                                       113 (84.3%)   103 (76.9%)    106 (80.3%)     322 (80.5%) 
  41-64                                       21 (15.7%)    31 (23.1%)      25 (18.9%)      77 (19.2%) 
  >=65                                             0             0           1 (0.8%)        1 (0.2%)  
Sex                                                                                                    
  n                                               134           134            132             400     
  Female                                       79 (59%)     82 (61.2%)       70 (53%)      231 (57.8%) 
  Male                                         55 (41%)     52 (38.8%)       62 (47%)      169 (42.2%) 
Race                                                                                                   
  n                                               134           134            132             400     
  ASIAN                                       68 (50.7%)     67 (50%)       73 (55.3%)      208 (52%)  
  BLACK OR AFRICAN AMERICAN                   31 (23.1%)    28 (20.9%)      32 (24.2%)      91 (22.8%) 
  WHITE                                       27 (20.1%)    26 (19.4%)      21 (15.9%)      74 (18.5%) 
  AMERICAN INDIAN OR ALASKA NATIVE              8 (6%)       11 (8.2%)       6 (4.5%)       25 (6.2%)  
  MULTIPLE                                         0         1 (0.7%)           0            1 (0.2%)  
  NATIVE HAWAIIAN OR OTHER PACIFIC ISLANDER        0         1 (0.7%)           0            1 (0.2%)  
  OTHER                                            0             0              0               0      
  UNKNOWN                                          0             0              0               0      
Ethnicity                                                                                              
  n                                               134           134            132             400     
  HISPANIC OR LATINO                          15 (11.2%)    18 (13.4%)      15 (11.4%)       48 (12%)  
  NOT HISPANIC OR LATINO                      104 (77.6%)   103 (76.9%)    101 (76.5%)      308 (77%)  
  NOT REPORTED                                 6 (4.5%)      10 (7.5%)      11 (8.3%)       27 (6.8%)  
  UNKNOWN                                      9 (6.7%)      3 (2.2%)        5 (3.8%)       17 (4.2%)  

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Enrollment by Country Table (EudraCT only)

Data Setup

library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl
adsl_labels <- var_labels(adsl)

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)

adsl <- adsl %>%
  mutate(COUNTRY = droplevels(COUNTRY)) %>%
  arrange(REGION1, COUNTRY)

var_labels(adsl) <- c(adsl_labels)

Enrollment by Country Table

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  add_overall_col(label = "All Patients") %>%
  analyze_vars("COUNTRY") %>%
  append_varlabels(adsl, "COUNTRY")

result <- build_table(lyt, adsl)
result

          A: Drug X    B: Placebo   C: Combination   All Patients
Country    (N=134)      (N=134)        (N=132)         (N=400)   
—————————————————————————————————————————————————————————————————
n            134          134            132             400     
CHN       74 (55.2%)   81 (60.4%)     64 (48.5%)     219 (54.8%) 
USA       10 (7.5%)    13 (9.7%)      17 (12.9%)       40 (10%)  
BRA       13 (9.7%)     7 (5.2%)      10 (7.6%)       30 (7.5%)  
PAK        12 (9%)      9 (6.7%)      10 (7.6%)       31 (7.8%)  
NGA         8 (6%)      7 (5.2%)      11 (8.3%)       26 (6.5%)  
RUS        5 (3.7%)      8 (6%)        6 (4.5%)       19 (4.8%)  
JPN        5 (3.7%)      4 (3%)        9 (6.8%)       18 (4.5%)  
GBR         4 (3%)      3 (2.2%)       2 (1.5%)        9 (2.2%)  
CAN        3 (2.2%)     2 (1.5%)       3 (2.3%)         8 (2%)   

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Non-Serious Adverse Events Reported in \(\geq\) 5% of Patients in Any Treatment Group (CTgov & EudraCT)

Data Setup

Trimming function get_adae_trimmed is defined to filter AEs with greater than 5% incidence rate.

library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl
adae <- random.cdisc.data::cadae

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adae <- df_explicit_na(adae)

adae_nonser <- adae %>% filter(AESER != "Y", SAFFL == "Y")

get_adae_trimmed <- function(adsl, adae, cutoff_rate) {
  n_per_arm <- adsl %>%
    dplyr::count(ARM)

  anl_terms <- adae %>%
    dplyr::group_by(ARM, AEBODSYS, AEDECOD) %>%
    dplyr::count(
      unique_terms = n_distinct(USUBJID)
    ) %>%
    dplyr::select(-n) %>%
    dplyr::ungroup()

  anl_terms <- dplyr::left_join(
    anl_terms,
    n_per_arm,
    by = "ARM"
  ) %>%
    dplyr::mutate(
      ae_rate = unique_terms / n
    ) %>%
    dplyr::filter(ae_rate >= cutoff_rate) %>%
    dplyr::select(AEDECOD) %>%
    unique()

  anl <- dplyr::left_join(
    anl_terms,
    adae,
    by = "AEDECOD"
  )
  anl
}

adae_trim <- get_adae_trimmed(adsl, adae_nonser, cutoff_rate = 0.05)

Non-Serious Adverse Events Report in \(\geq\) 5% of Patients in Any Treatment Group

Preview

# Define the split function
split_fun <- drop_split_levels

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  summarize_patients_events_in_cols(
    custom_label = "Total number of patients with at least one non-serious adverse event occuring at a relative frequency of >=5%" 
  ) %>%
  split_rows_by("AEBODSYS",
    nested = FALSE,
    split_fun = split_fun,
    indent_mod = -1L,
    label_pos = "topleft",
    split_label = obj_label(adae_trim$AEBODSYS)
  ) %>%
  split_rows_by("AEDECOD", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adae_trim$AEDECOD)) %>%
  summarize_patients_events_in_cols(
    col_split = FALSE
  )

result <- build_table(lyt, adae_trim)
result

Body System or Organ Class                                                                                                A: Drug X                      B: Placebo                    C: Combination        
  Dictionary-Derived Term                                                                                       Patients (All)   Events (All)   Patients (All)   Events (All)   Patients (All)   Events (All)
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one non-serious adverse event occuring at a relative frequency of >=5%        106             360             112             367             112             421     
cl A.1                                                                                                                                                                                                       
  dcd A.1.1.1.1                                                                                                       50              64              45              62              63              88     
cl B.2                                                                                                                                                                                                       
  dcd B.2.1.2.1                                                                                                       49              65              44              62              52              66     
cl C.1                                                                                                                                                                                                       
  dcd C.1.1.1.3                                                                                                       43              55              46              63              43              64     
cl C.2                                                                                                                                                                                                       
  dcd C.2.1.2.1                                                                                                       35              48              48              53              55              65     
cl D.1                                                                                                                                                                                                       
  dcd D.1.1.4.2                                                                                                       48              66              42              55              50              64     
cl D.2                                                                                                                                                                                                       
  dcd D.2.1.5.3                                                                                                       47              62              58              72              57              74     

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Serious Adverse Events, Fatal SAEs and SAEs Related to Study Medication, by Treatment Group (CTgov & EudraCT)

For illustrative purposes, the adae data is filtered by arm “A: Drug X” here.

Data Setup

library(tern)
library(dplyr)

adae <- random.cdisc.data::cadae

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adae <- df_explicit_na(adae)

adae_serious <- adae %>% filter(AESER == "Y", SAFFL == "Y")
adae_serious_arm <- adae_serious %>% filter(ARM == "A: Drug X")

filters_list <- list(
  related = with_label(c(AEREL = "Y"), "Events (Related)"),
  fatal = with_label(c(AESDTH = "Y"), "Events (Fatal)"),
  fatal_related = with_label(c(AEREL = "Y", AESDTH = "Y"), "Events (Fatal & Related)")
)

Serious Adverse Events, Fatal SAEs and SAEs Related to Study Medication, by Treatment Group

Preview

# Define the split function
split_fun <- drop_split_levels

lyt <- basic_table() %>%
  summarize_patients_events_in_cols(
    filters_list = filters_list,
    empty_stats = c("all", "related", "fatal", "fatal_related"),
    custom_label = "Total number of patients with at least one serious adverse event"
  ) %>%
  split_rows_by("AEBODSYS",
    nested = FALSE,
    split_fun = split_fun,
    indent_mod = -1L,
    label_pos = "topleft",
    split_label = obj_label(adae_trim$AEBODSYS)
  ) %>%
  split_rows_by("AEDECOD",
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adae_serious_arm$AEDECOD)
  ) %>%
  summarize_patients_events_in_cols(
    filters_list = filters_list,
    col_split = FALSE
  )

result <- build_table(lyt, adae_serious_arm)
result

Body System or Organ Class                                                                                                                                     
  Dictionary-Derived Term                                          Patients (All)   Events (All)   Events (Related)   Events (Fatal)   Events (Fatal & Related)
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one serious adverse event        104                                                                                    
cl A.1                                                                                                                                                         
  dcd A.1.1.1.2                                                          48              68               0                 0                     0            
cl B.1                                                                                                                                                         
  dcd B.1.1.1.1                                                          47              56               56                56                    56           
cl B.2                                                                                                                                                         
  dcd B.2.2.3.1                                                          48              64               0                 0                     0            
cl D.1                                                                                                                                                         
  dcd D.1.1.1.1                                                          50              61               61                61                    61           

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Death Table (EudraCT only)

Data Setup

library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl
adae <- random.cdisc.data::cadae

Death Table

Preview

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  add_overall_col("All Patients") %>%
  count_patients_with_event(
    "USUBJID",
    filters = c("AESDTH" = "Y"),
    .labels = c(count_fraction = "Total Number of Deaths"),
    .formats = c(count_fraction = "xx (xx.xx%)")
  )
result <- build_table(lyt, adae, alt_counts_df = adsl)
result

                          A: Drug X    B: Placebo    C: Combination   All Patients
                           (N=134)       (N=134)        (N=132)         (N=400)   
——————————————————————————————————————————————————————————————————————————————————
Total Number of Deaths   76 (62.30%)   70 (56.91%)    75 (62.50%)     221 (60.55%)

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Reproducibility

Timestamp

[1] "2025-07-09 17:39:23 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.0 (2025-04-11)
 os       Ubuntu 24.04.2 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2025-07-09
 pandoc   3.7.0.2 @ /usr/bin/ (via rmarkdown)
 quarto   1.7.32 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package           * version date (UTC) lib source
 backports           1.5.0   2024-05-23 [1] RSPM
 brio                1.1.5   2024-04-24 [1] RSPM
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 checkmate           2.3.2   2024-07-29 [1] RSPM
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 codetools           0.2-20  2024-03-31 [2] CRAN (R 4.5.0)
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 evaluate            1.0.4   2025-06-18 [1] RSPM
 farver              2.1.2   2024-05-13 [1] RSPM
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 R6                  2.6.1   2025-02-15 [1] RSPM
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 testthat            3.2.3   2025-01-13 [1] RSPM
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 withr               3.0.2   2024-10-28 [1] RSPM
 xfun                0.52    2025-04-02 [1] RSPM
 yaml                2.3.10  2024-07-26 [1] RSPM

 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library
 [3] /github/home/R/x86_64-pc-linux-gnu-library/4.5
 * ── Packages attached to the search path.

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

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