CMT01A

Concomitant Medications by Medication Class and Preferred Name

---

Output

Standard Table, Safety-Evaluable Patients

Preview

split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    count_by = "CMSEQ",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one treatment",
      nonunique = "Total number of treatments"
    )
  ) %>%
  split_rows_by(
    "ATC2",
    child_labels = "visible",
    nested = FALSE,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adcm$ATC2)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    count_by = "CMSEQ",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one treatment",
      nonunique = "Total number of treatments"
    )
  ) %>%
  count_occurrences(vars = "CMDECOD", .indent_mods = -1L) %>%
  append_varlabels(adcm, "CMDECOD", indent = 1L)

result <- build_table(lyt = lyt, df = adcm, alt_counts_df = adsl) %>%
  prune_table() %>%
  # Sort lowest level terms by descending frequency.
  sort_at_path(
    path = c("ATC2", "*", "CMDECOD"),
    scorefun = score_occurrences
  )

result

ATC Level 2 Text                                          A: Drug X    B: Placebo    C: Combination
  Other Treatment                                          (N=134)       (N=134)        (N=132)    
———————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  
Total number of treatments                                   415           414            460      
ATCCLAS2 A                                                                                         
  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  
  Total number of treatments                                 134           137            143      
  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  
  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  
ATCCLAS2 A p2                                                                                      
  Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  
  Total number of treatments                                 58            66              64      
  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  
ATCCLAS2 B                                                                                         
  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  
  Total number of treatments                                 141           137            162      
  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  
  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  
ATCCLAS2 B p2                                                                                      
  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  
  Total number of treatments                                 75            82              83      
  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  
ATCCLAS2 B p3                                                                                      
  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  
  Total number of treatments                                 75            82              83      
  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  
ATCCLAS2 C                                                                                         
  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  
  Total number of treatments                                 140           140            155      
  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  
  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  
ATCCLAS2 C p2                                                                                      
  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  
  Total number of treatments                                 140           140            155      
  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  
  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  
ATCCLAS2 C p3                                                                                      
  Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  
  Total number of treatments                                 69            73              80      
  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  

Try this using WebR

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.

Table Changing ATC Class Level

Preview

split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    count_by = "CMSEQ",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one treatment",
      nonunique = "Total number of treatments"
    )
  ) %>%
  split_rows_by(
    "ATC1",
    child_labels = "visible",
    nested = FALSE,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adcm$ATC1)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    count_by = "CMSEQ",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one treatment",
      nonunique = "Total number of treatments"
    )
  ) %>%
  count_occurrences(vars = "CMDECOD", .indent_mods = -1L) %>%
  append_varlabels(adcm, "CMDECOD", indent = 1L)

result <- build_table(lyt = lyt, df = adcm, alt_counts_df = adsl) %>%
  prune_table() %>%
  # Sort lowest level terms by descending frequency.
  sort_at_path(
    path = c("ATC1", "*", "CMDECOD"),
    scorefun = score_occurrences
  )

result

ATC Level 1 Text                                          A: Drug X    B: Placebo    C: Combination
  Other Treatment                                          (N=134)       (N=134)        (N=132)    
———————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  
Total number of treatments                                   415           414            460      
ATCCLAS1 A                                                                                         
  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  
  Total number of treatments                                 134           137            143      
  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  
  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  
ATCCLAS1 A p2                                                                                      
  Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  
  Total number of treatments                                 58            66              64      
  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  
ATCCLAS1 B                                                                                         
  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  
  Total number of treatments                                 141           137            162      
  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  
  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  
ATCCLAS1 B p2                                                                                      
  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  
  Total number of treatments                                 75            82              83      
  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  
ATCCLAS1 B p3                                                                                      
  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  
  Total number of treatments                                 75            82              83      
  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  
ATCCLAS1 C                                                                                         
  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  
  Total number of treatments                                 140           140            155      
  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  
  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  
ATCCLAS1 C p2                                                                                      
  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  
  Total number of treatments                                 140           140            155      
  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  
  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  
ATCCLAS1 C p3                                                                                      
  Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  
  Total number of treatments                                 69            73              80      
  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  

Try this using WebR

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.

Table with Classes Sorted by Frequency

Preview

split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    count_by = "CMSEQ",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one treatment",
      nonunique = "Total number of treatments"
    )
  ) %>%
  split_rows_by(
    "ATC2",
    child_labels = "visible",
    nested = FALSE,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adcm$ATC2)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    count_by = "CMSEQ",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one treatment",
      nonunique = "Total number of treatments"
    )
  ) %>%
  count_occurrences(vars = "CMDECOD", .indent_mods = -1L) %>%
  append_varlabels(adcm, "CMDECOD", indent = 1L)

result <- build_table(lyt = lyt, df = adcm, alt_counts_df = adsl) %>%
  prune_table() %>%
  sort_at_path(path = c("ATC2"), scorefun = cont_n_allcols) %>%
  sort_at_path(path = c("ATC2", "*", "CMDECOD"), scorefun = score_occurrences)

result

ATC Level 2 Text                                          A: Drug X    B: Placebo    C: Combination
  Other Treatment                                          (N=134)       (N=134)        (N=132)    
———————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  
Total number of treatments                                   415           414            460      
ATCCLAS2 C                                                                                         
  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  
  Total number of treatments                                 140           140            155      
  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  
  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  
ATCCLAS2 C p2                                                                                      
  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  
  Total number of treatments                                 140           140            155      
  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  
  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  
ATCCLAS2 B                                                                                         
  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  
  Total number of treatments                                 141           137            162      
  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  
  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  
ATCCLAS2 A                                                                                         
  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  
  Total number of treatments                                 134           137            143      
  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  
  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  
ATCCLAS2 C p3                                                                                      
  Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  
  Total number of treatments                                 69            73              80      
  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  
ATCCLAS2 B p2                                                                                      
  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  
  Total number of treatments                                 75            82              83      
  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  
ATCCLAS2 B p3                                                                                      
  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  
  Total number of treatments                                 75            82              83      
  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  
ATCCLAS2 A p2                                                                                      
  Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  
  Total number of treatments                                 58            66              64      
  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  

Try this using WebR

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.

Table with Total Number of Treatments per Medication Class Suppressed

Preview

split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    count_by = "CMSEQ",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one treatment",
      nonunique = "Total number of treatments"
    )
  ) %>%
  split_rows_by(
    "ATC2",
    child_labels = "visible",
    nested = FALSE,
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adcm$ATC2)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    count_by = "CMSEQ",
    .stats = c("unique"),
    .labels = c(
      unique = "Total number of patients with at least one treatment"
    )
  ) %>%
  count_occurrences(vars = "CMDECOD", .indent_mods = -1L) %>%
  append_varlabels(adcm, "CMDECOD", indent = 1L)

result <- build_table(lyt = lyt, df = adcm, alt_counts_df = adsl) %>%
  prune_table() %>%
  # Sort lowest level terms by descending frequency.
  sort_at_path(
    path = c("ATC2", "*", "CMDECOD"),
    scorefun = score_occurrences
  )

result

ATC Level 2 Text                                          A: Drug X    B: Placebo    C: Combination
  Other Treatment                                          (N=134)       (N=134)        (N=132)    
———————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one treatment     117 (87.3%)   116 (86.6%)    116 (87.9%)  
Total number of treatments                                   415           414            460      
ATCCLAS2 A                                                                                         
  Total number of patients with at least one treatment   75 (56.0%)    79 (59.0%)      81 (61.4%)  
  medname A_2/3                                          53 (39.6%)    50 (37.3%)      56 (42.4%)  
  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  
ATCCLAS2 A p2                                                                                      
  Total number of patients with at least one treatment   45 (33.6%)    54 (40.3%)      48 (36.4%)  
  medname A_3/3                                          45 (33.6%)    54 (40.3%)      48 (36.4%)  
ATCCLAS2 B                                                                                         
  Total number of patients with at least one treatment   83 (61.9%)    74 (55.2%)      88 (66.7%)  
  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  
  medname B_4/4                                          50 (37.3%)    45 (33.6%)      55 (41.7%)  
ATCCLAS2 B p2                                                                                      
  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  
  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  
ATCCLAS2 B p3                                                                                      
  Total number of patients with at least one treatment   52 (38.8%)    57 (42.5%)      59 (44.7%)  
  medname B_1/4                                          52 (38.8%)    57 (42.5%)      59 (44.7%)  
ATCCLAS2 C                                                                                         
  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  
  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  
  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  
ATCCLAS2 C p2                                                                                      
  Total number of patients with at least one treatment   82 (61.2%)    84 (62.7%)      89 (67.4%)  
  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  
  medname C_1/2                                          51 (38.1%)    50 (37.3%)      56 (42.4%)  
ATCCLAS2 C p3                                                                                      
  Total number of patients with at least one treatment   52 (38.8%)    58 (43.3%)      60 (45.5%)  
  medname C_2/2                                          52 (38.8%)    58 (43.3%)      60 (45.5%)  

Try this using WebR

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.

Data Setup

library(dplyr)
library(tern)

adsl <- random.cdisc.data::cadsl
adcm <- random.cdisc.data::cadcm

adcm$CMSEQ <- as.factor(adcm$CMSEQ)

adsl <- df_explicit_na(adsl)
adcm <- df_explicit_na(adcm)

# Keep only safety-evaluable patients and concomitant medications
adsl <- adsl %>%
  filter(SAFFL == "Y")

adcm <- adcm %>%
  filter(SAFFL == "Y" & ATIREL == "CONCOMITANT") %>%
  var_relabel(CMDECOD = "Other Treatment")

teal App

Preview

library(teal.modules.clinical)

adcm_keys <- c("STUDYID", "USUBJID", "ASTDTM", "CMSEQ", "ATC1", "ATC2", "ATC3", "ATC4")

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- random.cdisc.data::cadsl
  ADCM <- random.cdisc.data::cadcm
  ADCM$CMSEQ <- as.integer(ADCM$CMSEQ)

  ADSL <- df_explicit_na(ADSL)
  ADCM <- df_explicit_na(ADCM)
})
datanames <- c("ADSL", "ADCM")
datanames(data) <- datanames

Warning: `datanames<-()` was deprecated in teal.data 0.6.1.
ℹ invalid to use `datanames()<-` or `names()<-` on an object of class
  `teal_data`. See ?names.teal_data

join_keys(data) <- default_cdisc_join_keys[datanames]
join_keys(data)["ADCM", "ADCM"] <- adcm_keys

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_mult_events(
      label = "Concomitant Medications by Medication Class and Preferred Name",
      dataname = "ADCM",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      seq_var = choices_selected("CMSEQ", selected = "CMSEQ", fixed = TRUE),
      hlt = choices_selected(
        choices = variable_choices(ADCM, c("ATC1", "ATC2", "ATC3", "ATC4")),
        selected = c("ATC2")
      ),
      llt = choices_selected(
        choices = variable_choices(ADCM, c("CMDECOD")),
        selected = c("CMDECOD")
      ),
      add_total = FALSE,
      event_type = "treatment"
    )
  ),
  filter = teal_slices(
    teal_slice("ADSL", "SAFFL", selected = "Y"),
    teal_slice("ADCM", "ATIREL", selected = "CONCOMITANT")
  )
)

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.

#| '!! shinylive warning !!': |
#|   shinylive does not work in self-contained HTML documents.
#|   Please set `embed-resources: false` in your metadata.
#| 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)

adcm_keys <- c("STUDYID", "USUBJID", "ASTDTM", "CMSEQ", "ATC1", "ATC2", "ATC3", "ATC4")

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- random.cdisc.data::cadsl
  ADCM <- random.cdisc.data::cadcm
  ADCM$CMSEQ <- as.integer(ADCM$CMSEQ)

  ADSL <- df_explicit_na(ADSL)
  ADCM <- df_explicit_na(ADCM)
})
datanames <- c("ADSL", "ADCM")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]
join_keys(data)["ADCM", "ADCM"] <- adcm_keys

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_mult_events(
      label = "Concomitant Medications by Medication Class and Preferred Name",
      dataname = "ADCM",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      seq_var = choices_selected("CMSEQ", selected = "CMSEQ", fixed = TRUE),
      hlt = choices_selected(
        choices = variable_choices(ADCM, c("ATC1", "ATC2", "ATC3", "ATC4")),
        selected = c("ATC2")
      ),
      llt = choices_selected(
        choices = variable_choices(ADCM, c("CMDECOD")),
        selected = c("CMDECOD")
      ),
      add_total = FALSE,
      event_type = "treatment"
    )
  ),
  filter = teal_slices(
    teal_slice("ADSL", "SAFFL", selected = "Y"),
    teal_slice("ADCM", "ATIREL", selected = "CONCOMITANT")
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-20 18:02:27 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     2024-11-20
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ 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
 broom                   1.0.7       2024-09-26 [1] RSPM
 bslib                   0.8.0       2024-07-29 [1] RSPM
 cachem                  1.1.0       2024-05-16 [1] RSPM
 callr                   3.7.6       2024-03-25 [1] RSPM
 checkmate               2.3.2       2024-07-29 [1] RSPM
 chromote                0.3.1       2024-08-30 [1] RSPM
 cli                     3.6.3       2024-06-21 [1] RSPM
 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] RSPM
 curl                    6.0.1       2024-11-14 [1] RSPM
 digest                  0.6.37      2024-08-19 [1] RSPM
 dplyr                 * 1.1.4       2023-11-17 [1] RSPM
 emmeans                 1.10.5      2024-10-14 [1] RSPM
 estimability            1.5.1       2024-05-12 [1] RSPM
 evaluate                1.0.1       2024-10-10 [1] RSPM
 fansi                   1.0.6       2023-12-08 [1] RSPM
 fastmap                 1.2.0       2024-05-15 [1] RSPM
 fontawesome             0.5.3       2024-11-16 [1] RSPM
 forcats                 1.0.0       2023-01-29 [1] RSPM
 formatR                 1.14        2023-01-17 [1] CRAN (R 4.4.1)
 formatters            * 0.5.9.9003  2024-11-20 [1] https://p~
 geepack                 1.3.12      2024-09-23 [1] RSPM
 generics                0.1.3       2022-07-05 [1] RSPM
 ggplot2                 3.5.1       2024-04-23 [1] RSPM
 glue                    1.8.0       2024-09-30 [1] RSPM
 gtable                  0.3.6       2024-10-25 [1] RSPM
 htmltools               0.5.8.1     2024-04-04 [1] RSPM
 htmlwidgets             1.6.4       2023-12-06 [1] RSPM
 httpuv                  1.6.15      2024-03-26 [1] RSPM
 jquerylib               0.1.4       2021-04-26 [1] RSPM
 jsonlite                1.8.9       2024-09-20 [1] RSPM
 knitr                   1.49        2024-11-08 [1] RSPM
 later                   1.3.2       2023-12-06 [1] RSPM
 lattice                 0.22-6      2024-03-20 [2] CRAN (R 4.4.1)
 lifecycle               1.0.4       2023-11-07 [1] RSPM
 logger                  0.4.0       2024-10-22 [1] RSPM
 magrittr              * 2.0.3       2022-03-30 [1] RSPM
 MASS                    7.3-61      2024-06-13 [2] CRAN (R 4.4.1)
 Matrix                  1.7-1       2024-10-18 [1] RSPM
 memoise                 2.0.1       2021-11-26 [1] RSPM
 mime                    0.12        2021-09-28 [1] RSPM
 multcomp                1.4-26      2024-07-18 [1] CRAN (R 4.4.1)
 munsell                 0.5.1       2024-04-01 [1] RSPM
 mvtnorm                 1.3-2       2024-11-04 [1] RSPM
 nestcolor               0.1.2.9017  2024-11-20 [1] https://p~
 nlme                    3.1-166     2024-08-14 [2] CRAN (R 4.4.1)
 pillar                  1.9.0       2023-03-22 [1] RSPM
 pkgcache                2.2.3       2024-09-12 [1] RSPM
 pkgconfig               2.0.3       2019-09-22 [1] RSPM
 processx                3.8.4       2024-03-16 [1] RSPM
 promises                1.3.0       2024-04-05 [1] RSPM
 ps                      1.8.1       2024-10-28 [1] RSPM
 purrr                   1.0.2       2023-08-10 [1] RSPM
 R6                      2.5.1       2021-08-19 [1] RSPM
 random.cdisc.data       0.3.16.9001 2024-11-20 [1] https://p~
 rbibutils               2.3         2024-10-04 [1] RSPM
 Rcpp                    1.0.13-1    2024-11-02 [1] RSPM
 Rdpack                  2.6.2       2024-11-15 [1] RSPM
 rlang                   1.1.4       2024-06-04 [1] RSPM
 rmarkdown               2.29        2024-11-04 [1] RSPM
 rtables               * 0.6.10.9005 2024-11-20 [1] https://p~
 sandwich                3.1-1       2024-09-15 [1] CRAN (R 4.4.1)
 sass                    0.4.9       2024-03-15 [1] RSPM
 scales                  1.3.0       2023-11-28 [1] RSPM
 sessioninfo             1.2.2       2021-12-06 [1] any (@1.2.2)
 shiny                 * 1.9.1       2024-08-01 [1] RSPM
 shinycssloaders         1.1.0       2024-07-30 [1] RSPM
 shinyjs                 2.1.0       2021-12-23 [1] RSPM
 shinyvalidate           0.1.3       2023-10-04 [1] RSPM
 shinyWidgets            0.8.7       2024-09-23 [1] RSPM
 stringi                 1.8.4       2024-05-06 [1] RSPM
 stringr                 1.5.1       2023-11-14 [1] RSPM
 survival                3.7-0       2024-06-05 [2] CRAN (R 4.4.1)
 teal                  * 0.15.2.9089 2024-11-20 [1] https://p~
 teal.code             * 0.5.0.9018  2024-11-20 [1] https://p~
 teal.data             * 0.6.0.9021  2024-11-20 [1] https://p~
 teal.logger             0.3.0.9002  2024-11-20 [1] https://p~
 teal.modules.clinical * 0.9.1.9031  2024-11-20 [1] https://p~
 teal.reporter           0.3.1.9016  2024-11-20 [1] https://p~
 teal.slice            * 0.5.1.9016  2024-11-20 [1] https://p~
 teal.transform        * 0.5.0.9016  2024-11-20 [1] https://p~
 teal.widgets            0.4.2.9022  2024-11-20 [1] https://p~
 tern                  * 0.9.6.9016  2024-11-20 [1] https://p~
 tern.gee                0.1.5.9004  2024-11-20 [1] https://p~
 testthat                3.2.1.1     2024-04-14 [1] RSPM
 TH.data                 1.1-2       2023-04-17 [1] CRAN (R 4.4.1)
 tibble                  3.2.1       2023-03-20 [1] RSPM
 tidyr                   1.3.1       2024-01-24 [1] RSPM
 tidyselect              1.2.1       2024-03-11 [1] RSPM
 utf8                    1.2.4       2023-10-22 [1] RSPM
 vctrs                   0.6.5       2023-12-01 [1] RSPM
 webshot                 0.5.5       2023-06-26 [1] CRAN (R 4.4.1)
 webshot2                0.1.1       2023-08-11 [1] RSPM
 websocket               1.4.2       2024-07-22 [1] RSPM
 withr                   3.0.2       2024-10-28 [1] RSPM
 xfun                    0.49        2024-10-31 [1] RSPM
 xtable                  1.8-4       2019-04-21 [1] RSPM
 yaml                    2.3.10      2024-07-26 [1] RSPM
 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

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

.lock file

Download the .lock file and use renv::restore() on it to recreate environment used to generate this website.

Download