RMPT03

Extent of Exposure by Age Group and Gender for Risk Management Plan

Output

Preview
Try this using WebR

Code

lyt <- basic_table(
  title = "Extent of Exposure by Age Group and Gender: Safety-Evaluable Patients",
  main_footer = "* Patient Time is the sum of exposure across all patients in days."
) %>%
  split_cols_by("SEX", split_fun = add_overall_level("Total", first = FALSE)) %>%
  summarize_patients_exposure_in_cols(
    var = "AVAL", col_split = TRUE,
    .labels = c(n_patients = "Number of Patients", sum_exposure = "Patient Time*"),
    custom_label = "Total Number of Patients and Patient Time"
  ) %>%
  analyze_patients_exposure_in_cols(
    var = "AGEGR1",
    col_split = FALSE
  ) %>%
  append_topleft(c("", "Age group (years)"))

result <- build_table(lyt, df = anl, alt_counts_df = adsl_f)
result

Extent of Exposure by Age Group and Gender: Safety-Evaluable Patients

————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
                                                          Female                                Male                                Total               
Age group (years)                           Number of Patients   Patient Time*   Number of Patients   Patient Time*   Number of Patients   Patient Time*
————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Total Number of Patients and Patient Time      134 (58.0%)           12591           83 (49.1%)           8050           217 (54.2%)           20641    
  <65                                          134 (58.0%)           12591           82 (48.5%)           7970           216 (54.0%)           20561    
  >=65                                           0 (0.0%)              0              1 (0.6%)             80              1 (0.2%)             80      
————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————

* Patient Time is the sum of exposure across all patients in days.

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
Try this using WebR

Code

lyt <- basic_table(
  title = "Duration of Exposure: Safety-Evaluable Patients",
  main_footer = "* Patient Time is the sum of exposure across all patients in days."
) %>%
  split_cols_by("SEX", split_fun = add_overall_level("Total", first = FALSE)) %>%
  summarize_patients_exposure_in_cols(
    var = "AVAL", col_split = TRUE,
    .labels = c(n_patients = "Number of Patients", sum_exposure = "Patient Time*"),
    custom_label = "Total Number of Patients and Patient Time"
  ) %>%
  analyze_patients_exposure_in_cols(
    var = "AGEGR2",
    col_split = FALSE
  ) %>%
  append_topleft(c("", "Age group (years)"))

result <- build_table(lyt, df = anl, alt_counts_df = adsl_f) %>%
  prune_table()
result

Duration of Exposure: Safety-Evaluable Patients

————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
                                                          Female                                Male                                Total               
Age group (years)                           Number of Patients   Patient Time*   Number of Patients   Patient Time*   Number of Patients   Patient Time*
————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Total Number of Patients and Patient Time      134 (58.0%)           12591           83 (49.1%)           8050           217 (54.2%)           20641    
  18 - 40                                      118 (51.1%)           11203           60 (35.5%)           5787           178 (44.5%)           16990    
  41 - 64                                       16 (6.9%)            1388            22 (13.0%)           2183            38 (9.5%)            3571     
  >=65                                           0 (0.0%)              0              1 (0.6%)             80              1 (0.2%)             80      
————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————

* Patient Time is the sum of exposure across all patients in days.

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(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl
adex <- random.cdisc.data::cadex

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

# Simulate ADEX records with PARAMCD == "TDURD" as they are not in sample random.cdisc.data dataset.
set.seed(1, kind = "Mersenne-Twister")
adex2 <- adex %>%
  distinct(USUBJID, .keep_all = TRUE) %>%
  mutate(
    PARAMCD = "TDURD",
    PARAM = "Overall duration (days)",
    AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE)
  ) %>%
  bind_rows(adex)

# Now pre-processing steps are carried out.
anl <- adex2 %>%
  filter(
    PARAMCD == "TDURD",
    PARCAT2 == "Drug A",
    SAFFL == "Y"
  ) %>%
  mutate(
    AGEGR1 = factor(ifelse(AGE < 65, "<65", ">=65")),
    AGEGR2 = factor(case_when(
      AGE < 18 ~ "< 18",
      AGE >= 18 & AGE <= 40 ~ "18 - 40",
      AGE > 40 & AGE <= 64 ~ "41 - 64",
      TRUE ~ ">=65"
    ), levels = c("< 18", "18 - 40", "41 - 64", ">=65")),
    SEX = factor(case_when(
      SEX == "F" ~ "Female",
      SEX == "M" ~ "Male"
    ))
  )

adsl_f <- adsl %>%
  filter(adsl$SAFFL == "Y") %>%
  mutate(SEX = factor(case_when(
    SEX == "F" ~ "Female",
    SEX == "M" ~ "Male"
  )))

Code

library(teal.modules.clinical)

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

  ADSL <- random.cdisc.data::cadsl
  ADEX <- random.cdisc.data::cadex

  labels <- col_labels(ADEX)
  set.seed(1, kind = "Mersenne-Twister")

  labels <- col_labels(ADEX)
  ADEX <- ADEX %>%
    distinct(USUBJID, .keep_all = TRUE) %>%
    mutate(
      PARAMCD = "TDURD",
      PARAM = "Overall duration (days)",
      AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE),
      AVALU = "Days",
      SEX = factor(case_when(
        SEX == "F" ~ "Female",
        SEX == "M" ~ "Male"
      ))
    ) %>%
    bind_rows(ADEX)

  col_labels(ADEX) <- labels

  ADEX <- ADEX %>%
    mutate(
      AGEGR1 = factor(ifelse(AGE < 65, "<65", ">=65")) %>% with_label("Age group (years)"),
      AGEGR2 = factor(case_when(
        AGE < 18 ~ "< 18",
        AGE >= 18 & AGE <= 40 ~ "18 - 40",
        AGE > 40 & AGE <= 64 ~ "41 - 64",
        TRUE ~ ">=65"
      ), levels = c("< 18", "18 - 40", "41 - 64", ">=65")) %>% with_label("Age group (years)")
    )

  ADSL <- ADSL %>%
    mutate(SEX = factor(case_when(
      SEX == "F" ~ "Female",
      SEX == "M" ~ "Male"
    )) %>% with_label("Sex"))
})
datanames <- c("ADSL", "ADEX")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_exposure(
      label = "Duration of Exposure Table",
      dataname = "ADEX",
      paramcd = choices_selected(
        choices = value_choices(ADEX, "PARAMCD", "PARAM"),
        selected = "TDURD"
      ),
      col_by_var = choices_selected(
        choices = variable_choices(ADEX, subset = c("ARM")),
        selected = "ARM"
      ),
      row_by_var = choices_selected(
        choices = variable_choices(ADEX, subset = c("AGEGR1", "AGEGR2")),
        selected = "AGEGR1"
      ),
      parcat = choices_selected(
        choices = value_choices(ADEX, "PARCAT2"),
        selected = "Drug A"
      ),
      add_total = FALSE
    )
  ),
  filter = teal_slices(teal_slice("ADSL", "SAFFL", selected = "Y"))
)

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://insightsengineering.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
  ADEX <- random.cdisc.data::cadex

  labels <- col_labels(ADEX)
  set.seed(1, kind = "Mersenne-Twister")

  labels <- col_labels(ADEX)
  ADEX <- ADEX %>%
    distinct(USUBJID, .keep_all = TRUE) %>%
    mutate(
      PARAMCD = "TDURD",
      PARAM = "Overall duration (days)",
      AVAL = sample(x = seq(1, 200), size = n(), replace = TRUE),
      AVALU = "Days",
      SEX = factor(case_when(
        SEX == "F" ~ "Female",
        SEX == "M" ~ "Male"
      ))
    ) %>%
    bind_rows(ADEX)

  col_labels(ADEX) <- labels

  ADEX <- ADEX %>%
    mutate(
      AGEGR1 = factor(ifelse(AGE < 65, "<65", ">=65")) %>% with_label("Age group (years)"),
      AGEGR2 = factor(case_when(
        AGE < 18 ~ "< 18",
        AGE >= 18 & AGE <= 40 ~ "18 - 40",
        AGE > 40 & AGE <= 64 ~ "41 - 64",
        TRUE ~ ">=65"
      ), levels = c("< 18", "18 - 40", "41 - 64", ">=65")) %>% with_label("Age group (years)")
    )

  ADSL <- ADSL %>%
    mutate(SEX = factor(case_when(
      SEX == "F" ~ "Female",
      SEX == "M" ~ "Male"
    )) %>% with_label("Sex"))
})
datanames <- c("ADSL", "ADEX")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_exposure(
      label = "Duration of Exposure Table",
      dataname = "ADEX",
      paramcd = choices_selected(
        choices = value_choices(ADEX, "PARAMCD", "PARAM"),
        selected = "TDURD"
      ),
      col_by_var = choices_selected(
        choices = variable_choices(ADEX, subset = c("ARM")),
        selected = "ARM"
      ),
      row_by_var = choices_selected(
        choices = variable_choices(ADEX, subset = c("AGEGR1", "AGEGR2")),
        selected = "AGEGR1"
      ),
      parcat = choices_selected(
        choices = value_choices(ADEX, "PARCAT2"),
        selected = "Drug A"
      ),
      add_total = FALSE
    )
  ),
  filter = teal_slices(teal_slice("ADSL", "SAFFL", selected = "Y"))
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-20 09:30:42 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 ───────────────────────────────────────────────────────────────────
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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