AET06_SMQ

Adverse Events by Baseline Characteristic, by SMQ and Preferred Term

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Code

criteria_fun <- function(tr) !is(tr, "ContentRow") && all_zero_or_na(tr)

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARMCD") %>%
  split_cols_by("SEX") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique"),
    .labels = c(unique = "Total number of patients with at least one adverse event")
  ) %>%
  split_rows_by(
    "SMQ",
    child_labels = "visible",
    nested = FALSE,
    split_fun = trim_levels_in_group("AEDECOD", drop_outlevs = FALSE),
    label_pos = "topleft",
    split_label = "Standardized MedDRA Query"
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event",
      nonunique = "Total number of events"
    )
  ) %>%
  count_occurrences(vars = "AEDECOD", drop = FALSE) %>%
  append_varlabels(adae_smq_1, "AEDECOD", indent = 1L)

result <- build_table(
  lyt = lyt,
  df = adae_smq_1,
  alt_counts_df = adsl
) %>%
  sort_at_path(path = c("SMQ"), scorefun = cont_n_allcols) %>%
  sort_at_path(path = c("SMQ", "*", "AEDECOD"), scorefun = score_occurrences, na.pos = "last") %>%
  trim_rows(criteria = criteria_fun)

result

                                                                      ARM A                     ARM B                     ARM C         
Standardized MedDRA Query                                        F            M            F            M            F            M     
  Dictionary-Derived Term                                      (N=79)       (N=55)       (N=82)       (N=52)       (N=70)       (N=62)  
————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event     47 (59.5%)   25 (45.5%)   51 (62.2%)   28 (53.8%)   42 (60.0%)   33 (53.2%)
C.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                                                         
  Total number of patients with at least one adverse event   47 (59.5%)   25 (45.5%)   51 (62.2%)   28 (53.8%)   42 (60.0%)   33 (53.2%)
  Total number of events                                         79           40           95           44           81           60    
    dcd B.2.2.3.1                                            30 (38.0%)   18 (32.7%)   32 (39.0%)   22 (42.3%)   26 (37.1%)   25 (40.3%)
    dcd C.1.1.1.3                                            30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)

Experimental use!

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Code

criteria_fun <- function(tr) {
  !is(tr, "ContentRow") && all_zero_or_na(tr) && !grepl("Total number of", obj_label(tr))
}

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARMCD") %>%
  split_cols_by("SEX") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique"),
    .labels = c(unique = "Total number of patients with at least one adverse event")
  ) %>%
  split_rows_by(
    "SMQ",
    child_labels = "visible",
    nested = FALSE,
    split_fun = trim_levels_in_group("AEDECOD", drop_outlevs = FALSE),
    label_pos = "topleft",
    split_label = "Standardized MedDRA Query"
  ) %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event",
      nonunique = "Total number of events"
    ),
    show_labels = "hidden"
  ) %>%
  count_occurrences(vars = "AEDECOD", drop = FALSE) %>%
  append_varlabels(adae_smq_all, "AEDECOD", indent = 1L)

score_ae_pts <- function(tt) {
  count_vals <- cell_values(tree_children(tt)[["USUBJID"]])[[1]]
  sum(matrix(unlist(count_vals), nrow = length(count_vals), byrow = TRUE)[, 1])
}

result <- build_table(
  lyt = lyt,
  df = adae_smq_all,
  alt_counts_df = adsl
) %>%
  sort_at_path(path = c("SMQ"), scorefun = score_ae_pts) %>%
  sort_at_path(path = c("SMQ", "*", "AEDECOD"), scorefun = score_occurrences, na.pos = "last") %>%
  trim_rows(criteria = criteria_fun)

result

                                                                      ARM A                     ARM B                     ARM C         
Standardized MedDRA Query                                        F            M            F            M            F            M     
  Dictionary-Derived Term                                      (N=79)       (N=55)       (N=82)       (N=52)       (N=70)       (N=62)  
————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event     59 (74.7%)   36 (65.5%)   68 (82.9%)   35 (67.3%)   61 (87.1%)   47 (75.8%)
D.2.1.5.3/A.1.1.1.1 AESI                                                                                                                
  Total number of patients with at least one adverse event   45 (57.0%)   29 (52.7%)   54 (65.9%)   26 (50.0%)   50 (71.4%)   37 (59.7%)
  Total number of events                                         78           48           91           43           88           74    
  dcd D.2.1.5.3                                              26 (32.9%)   21 (38.2%)   40 (48.8%)   18 (34.6%)   34 (48.6%)   23 (37.1%)
  dcd A.1.1.1.1                                              34 (43.0%)   16 (29.1%)   31 (37.8%)   14 (26.9%)   33 (47.1%)   30 (48.4%)
C.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                                                         
  Total number of patients with at least one adverse event   47 (59.5%)   25 (45.5%)   51 (62.2%)   28 (53.8%)   42 (60.0%)   33 (53.2%)
  Total number of events                                         79           40           95           44           81           60    
  dcd B.2.2.3.1                                              30 (38.0%)   18 (32.7%)   32 (39.0%)   22 (42.3%)   26 (37.1%)   25 (40.3%)
  dcd C.1.1.1.3                                              30 (38.0%)   13 (23.6%)   36 (43.9%)   10 (19.2%)   27 (38.6%)   16 (25.8%)
non observed SMQ02NAM level(BROAD)                                                                                                      
  Total number of patients with at least one adverse event       0            0            0            0            0            0     
  Total number of events                                         0            0            0            0            0            0

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

criteria_fun <- function(tr) !is(tr, "ContentRow") && all_zero_or_na(tr)

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARMCD") %>%
  split_cols_by("AGE65") %>%
  analyze_num_patients(
    vars = "USUBJID",
    .stats = c("unique"),
    .labels = c(unique = "Total number of patients with at least one adverse event")
  ) %>%
  split_rows_by(
    "SMQ",
    child_labels = "visible",
    nested = FALSE,
    split_fun = trim_levels_in_group("AEDECOD", drop_outlevs = FALSE),
    label_pos = "topleft",
    split_label = "Standardized MedDRA Query"
  ) %>%
  summarize_num_patients(
    var = "USUBJID",
    .stats = c("unique", "nonunique"),
    .labels = c(
      unique = "Total number of patients with at least one adverse event",
      nonunique = "Total number of events"
    )
  ) %>%
  count_occurrences(vars = "AEDECOD", drop = FALSE) %>%
  append_varlabels(adae_smq_1, "AEDECOD", indent = 1L)

result <- build_table(
  lyt = lyt,
  df = adae_smq_1,
  alt_counts_df = adsl
) %>%
  sort_at_path(path = c("SMQ"), scorefun = cont_n_allcols) %>%
  sort_at_path(path = c("SMQ", "*", "AEDECOD"), scorefun = score_occurrences, na.pos = "last") %>%
  trim_rows(criteria = criteria_fun)

result

                                                                   ARM A                ARM B                ARM C       
Standardized MedDRA Query                                       < 65      >= 65      < 65      >= 65      < 65      >= 65
  Dictionary-Derived Term                                     (N=134)     (N=0)    (N=134)     (N=0)    (N=131)     (N=1)
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Total number of patients with at least one adverse event     72 (53.7%)     0     79 (59.0%)     0     75 (57.3%)     0  
C.1.1.1.3/B.2.2.3.1 AESI(BROAD)                                                                                          
  Total number of patients with at least one adverse event   72 (53.7%)     0     79 (59.0%)     0     75 (57.3%)     0  
  Total number of events                                        119         0        139         0        141         0  
    dcd B.2.2.3.1                                            48 (35.8%)     0     54 (40.3%)     0     51 (38.9%)     0  
    dcd C.1.1.1.3                                            43 (32.1%)     0     46 (34.3%)     0     43 (32.8%)     0

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)
library(stringr)

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

adsl_labels <- var_labels(adsl)
adae_labels <- var_labels(adae)

adsl <- adsl %>%
  mutate(
    AGE65 = case_when(
      AGE >= 65 ~ ">= 65",
      TRUE ~ "< 65"
    ),
    AGE65 = factor(
      AGE65,
      levels = c("< 65", ">= 65")
    )
  )

adae <- adae %>%
  mutate(
    AGE65 = case_when(
      AGE >= 65 ~ ">= 65",
      TRUE ~ "< 65"
    ),
    AGE65 = factor(
      AGE65,
      levels = c("< 65", ">= 65")
    )
  )

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing
# levels. For details, refer to Teal and Study Data article.
adsl <- df_explicit_na(adsl)
adae <- df_explicit_na(adae)

# Simulate a random AAG dataset.
aag <- data.frame(
  NAMVAR = c("SMQ01NAM", "SMQ01NAM", "SMQ02NAM", "CQ01NAM", "CQ01NAM"),
  SRCVAR = rep("AEDECOD", 5),
  GRPTYPE = c("SMQ", "SMQ", "SMQ", "CUSTOM", "CUSTOM"),
  REFID = c(1, 1, 2, 3, 3),
  REFNAME = c(rep("C.1.1.1.3/B.2.2.3.1 AESI", 2), "non observed SMQ02NAM level", rep("D.2.1.5.3/A.1.1.1.1 AESI", 2)),
  SCOPE = c("BROAD", "BROAD", "BROAD", "", ""),
  REFTERM = c("C.1.1.1.3", "B.2.2.3.1", "Z.9.9.9.9", "D.2.1.5.3", "A.1.1.1.1"),
  stringsAsFactors = FALSE
)

# Create summary AAG dataset (used for labelling).
# Note it's important to incorporate SCOPE into the basket names for SMQs so as to
# match the flags present in ADAE.
aag_summary <- aag %>%
  select(NAMVAR, REFNAME, SCOPE) %>%
  unique() %>%
  mutate(
    REFNAME_SCOPE = ifelse(SCOPE == "", REFNAME, paste0(REFNAME, "(", SCOPE, ")"))
  ) %>%
  rename(basket = NAMVAR, basket_name = REFNAME_SCOPE) %>%
  select(basket, basket_name)

# Make a summary of the full ADAE based on AAG by using h_stack_by_baskets helper function
adae_smq_all <- h_stack_by_baskets(
  df = adae,
  aag_summary = aag_summary,
  keys = c("STUDYID", "USUBJID", "ACTARMCD", "AEDECOD", "SEX", "AGE65")
)
# Post-process adae_smq_all to keep only certain baskets of interest for variant 1
# Not need to drop baskets for variant 2 as all baskets will be considered.
baskets_to_drop_variant <- aag_summary$basket_name[!aag_summary$basket %in% c("SMQ01NAM")]

# Adjust the analysis dataset based on basket subset for variant 1.
adae_smq_1 <- adae_smq_all %>%
  filter(!adae_smq_all$SMQ %in% baskets_to_drop_variant) %>%
  mutate(
    SMQ = tern::fct_discard(SMQ, discard = baskets_to_drop_variant)
  )

Code

library(teal.modules.clinical)

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

  ADSL <- random.cdisc.data::cadsl %>%
    mutate(
      AGE65 = case_when(
        AGE >= 65 ~ ">= 65",
        TRUE ~ "< 65"
      ),
      AGE65 = factor(
        AGE65,
        levels = c("< 65", ">= 65")
      )
    )
  ADAE <- random.cdisc.data::cadae %>%
    mutate(
      AGE65 = case_when(
        AGE >= 65 ~ ">= 65",
        TRUE ~ "< 65"
      ),
      AGE65 = factor(
        AGE65,
        levels = c("< 65", ">= 65")
      )
    )
})
datanames <- c("ADSL", "ADAE")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

names_baskets <- grep("^(SMQ|CQ).*NAM$", names(ADAE), value = TRUE)
names_scopes <- grep("^SMQ.*SC$", names(ADAE), value = TRUE)

cs_baskets <- choices_selected(
  choices = variable_choices(ADAE, subset = names_baskets),
  selected = names_baskets
)
cs_scopes <- choices_selected(
  choices = variable_choices(ADAE, subset = names_scopes),
  selected = names_scopes,
  fixed = TRUE
)

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_smq(
      label = "Adverse events by `SMQ` Table",
      dataname = "ADAE",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, subset = c("ARM", "SEX", "AGE65")),
        selected = c("ARM", "SEX")
      ),
      add_total = FALSE,
      baskets = cs_baskets,
      scopes = cs_scopes,
      llt = choices_selected(
        choices = variable_choices(ADAE, subset = c("AEDECOD")),
        selected = "AEDECOD"
      )
    )
  )
)

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.

#| 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 %>%
    mutate(
      AGE65 = case_when(
        AGE >= 65 ~ ">= 65",
        TRUE ~ "< 65"
      ),
      AGE65 = factor(
        AGE65,
        levels = c("< 65", ">= 65")
      )
    )
  ADAE <- random.cdisc.data::cadae %>%
    mutate(
      AGE65 = case_when(
        AGE >= 65 ~ ">= 65",
        TRUE ~ "< 65"
      ),
      AGE65 = factor(
        AGE65,
        levels = c("< 65", ">= 65")
      )
    )
})
datanames <- c("ADSL", "ADAE")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

names_baskets <- grep("^(SMQ|CQ).*NAM$", names(ADAE), value = TRUE)
names_scopes <- grep("^SMQ.*SC$", names(ADAE), value = TRUE)

cs_baskets <- choices_selected(
  choices = variable_choices(ADAE, subset = names_baskets),
  selected = names_baskets
)
cs_scopes <- choices_selected(
  choices = variable_choices(ADAE, subset = names_scopes),
  selected = names_scopes,
  fixed = TRUE
)

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_smq(
      label = "Adverse events by `SMQ` Table",
      dataname = "ADAE",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, subset = c("ARM", "SEX", "AGE65")),
        selected = c("ARM", "SEX")
      ),
      add_total = FALSE,
      baskets = cs_baskets,
      scopes = cs_scopes,
      llt = choices_selected(
        choices = variable_choices(ADAE, subset = c("AEDECOD")),
        selected = "AEDECOD"
      )
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 18:12:38 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 ───────────────────────────────────────────────────────────────────
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 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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`teal` App

`.lock` file