CMT01

Concomitant Medications (GNEDrug Legacy Coding)

Output

Preview
Try this using WebR

Code

# Define the split function
split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ARM") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one treatment",
      nonunique = "Total number of treatments"
    )
  ) %>%
  split_rows_by("CMCLAS",
    split_fun = split_fun,
    child_labels = "visible",
    nested = FALSE,
    indent_mod = 1L,
    label_pos = "topleft",
    split_label = obj_label(adcm_con$CMCLAS)
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .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_con, "CMDECOD", indent = 1L)

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

result

Medication Class                                            A: Drug X    B: Placebo    C: Combination
  Standardized Medication Name                               (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      
  medcl 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%)  
  medcl 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%)  
  medcl 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%)

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.

Preview
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Code

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

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

result

Medication Class                                         A: Drug X    B: Placebo   C: Combination
  Standardized Medication Name                            (N=134)      (N=134)        (N=132)    
—————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one treatment     89 (66.4%)   95 (70.9%)    106 (80.3%)  
Total number of treatments                                  194          208            243      
medcl A                                                                                          
  Total number of patients with at least one treatment   54 (40.3%)   49 (36.6%)     69 (52.3%)  
  Total number of treatments                                 71           70             99      
  medname A_1/3                                          54 (40.3%)   49 (36.6%)     69 (52.3%)  
medcl B                                                                                          
  Total number of patients with at least one treatment   76 (56.7%)   80 (59.7%)     81 (61.4%)  
  Total number of treatments                                123          138            144      
  medname B_2/4                                          52 (38.8%)   55 (41.0%)     56 (42.4%)  
  medname B_3/4                                          47 (35.1%)   47 (35.1%)     52 (39.4%)

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.

Preview
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Code

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

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

result

Medication Class                                          A: Drug X    B: Placebo    C: Combination
  Standardized Medication Name                             (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      
medcl 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%)  
medcl 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%)  
medcl 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%)

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.

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Code

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

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

result

Medication Class                                              A: Drug X    B: Placebo    C: Combination   All Patients
  Standardized Medication Name                                 (N=134)       (N=134)        (N=132)         (N=400)   
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one treatment (%)     117 (87.3%)   116 (86.6%)    116 (87.9%)     349 (87.2%) 
Total number of treatments                                       415           414            460             1289    
medcl C                                                                                                               
  Total number of patients with at least one treatment (%)   82 (61.2%)    84 (62.7%)      89 (67.4%)     255 (63.7%) 
  Total number of treatments                                     140           140            155             435     
  medname C_2/2                                              52 (38.8%)    58 (43.3%)      60 (45.5%)     170 (42.5%) 
  medname C_1/2                                              51 (38.1%)    50 (37.3%)      56 (42.4%)     157 (39.2%) 
medcl B                                                                                                               
  Total number of patients with at least one treatment (%)   83 (61.9%)    74 (55.2%)      88 (66.7%)     245 (61.3%) 
  Total number of treatments                                     141           137            162             440     
  medname B_1/4                                              52 (38.8%)    57 (42.5%)      59 (44.7%)     168 (42.0%) 
  medname B_4/4                                              50 (37.3%)    45 (33.6%)      55 (41.7%)     150 (37.5%) 
medcl A                                                                                                               
  Total number of patients with at least one treatment (%)   75 (56.0%)    79 (59.0%)      81 (61.4%)     235 (58.8%) 
  Total number of treatments                                     134           137            143             414     
  medname A_2/3                                              53 (39.6%)    50 (37.3%)      56 (42.4%)     159 (39.8%) 
  medname A_3/3                                              45 (33.6%)    54 (40.3%)      48 (36.4%)     147 (36.8%)

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.

Code

library(dplyr)
library(tern)

# The following tables require ADCM data structure that follows the legacy concomitant medication coding.
# WHO coding is not used.
adsl <- random.cdisc.data::cadsl
adcm <- random.cdisc.data::cadcm

# Below step is to set up example data based on legacy standard.
adcm <- adcm %>%
  select(-starts_with("ATC")) %>%
  unique()

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

adcm_con <- adcm %>%
  filter(ATIREL == "CONCOMITANT")

adcm_prior <- adcm %>%
  filter(ATIREL == "PRIOR")

Here, adcm is processed to keep only one path per treatment.

Code

library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  library(dplyr)

  ADSL <- random.cdisc.data::cadsl
  ADCM <- random.cdisc.data::cadcm

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

  ADCM <- ADCM %>%
    group_by(STUDYID, USUBJID, CMDECOD) %>%
    slice(1) %>%
    ungroup()
})
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

Code

join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_events(
      label = "Concomitant Medication Table",
      dataname = "ADCM",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      llt = choices_selected(
        choices = variable_choices(ADCM, "CMDECOD"),
        selected = "CMDECOD"
      ),
      hlt = choices_selected(
        choices = variable_choices(ADCM, "CMCLAS"),
        selected = c("CMCLAS")
      ),
      add_total = TRUE,
      event_type = "treatment"
    )
  )
)

shinyApp(app$ui, app$server)

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)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  library(dplyr)

  ADSL <- random.cdisc.data::cadsl
  ADCM <- random.cdisc.data::cadcm

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

  ADCM <- ADCM %>%
    group_by(STUDYID, USUBJID, CMDECOD) %>%
    slice(1) %>%
    ungroup()
})
datanames <- c("ADSL", "ADCM")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_events(
      label = "Concomitant Medication Table",
      dataname = "ADCM",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      llt = choices_selected(
        choices = variable_choices(ADCM, "CMDECOD"),
        selected = "CMDECOD"
      ),
      hlt = choices_selected(
        choices = variable_choices(ADCM, "CMCLAS"),
        selected = c("CMCLAS")
      ),
      add_total = TRUE,
      event_type = "treatment"
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-26 06:24:40 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     2024-11-26
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version     date (UTC) lib source
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 brio                    1.1.5       2024-04-24 [1] RSPM
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 random.cdisc.data       0.3.16.9001 2024-11-26 [1] https://p~
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 svglite                 2.1.3       2023-12-08 [1] RSPM
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 teal                  * 0.15.2.9090 2024-11-26 [1] https://p~
 teal.code             * 0.5.0.9018  2024-11-26 [1] https://p~
 teal.data             * 0.6.0.9021  2024-11-26 [1] https://p~
 teal.logger             0.3.0.9002  2024-11-26 [1] https://p~
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 teal.reporter           0.3.1.9016  2024-11-26 [1] https://p~
 teal.slice            * 0.5.1.9016  2024-11-26 [1] https://p~
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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

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Output

teal App

Reproducibility

Timestamp

Session Info

.lock file

`teal` App

`.lock` file