TLG Catalog - Stable
  • Stable
    • Dev
  1. Tables
  2. Efficacy
  3. CFBT01
  • Introduction

  • Tables
    • ADA
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      • LBT05
      • LBT06
      • LBT07
      • LBT08
      • LBT09
      • LBT10
      • LBT10_BL
      • LBT11
      • LBT11_BL
      • LBT12
      • LBT12_BL
      • LBT13
      • LBT14
      • LBT15
    • Medical History
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  • Listings
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      • ADAL02
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    • Development Safety Update Report
      • DSUR4
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      • DSL01
      • DSL02
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      • EGL01
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      • ONCL01
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      • EXL01
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      • LBL01
      • LBL01_RLS
      • LBL02A
      • LBL02A_RLS
      • LBL02B
    • Medical History
      • MHL01
    • Pharmacokinetic
      • ADAL01
      • PKCL01
      • PKCL02
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      • PKPL04
    • Vital Signs
      • VSL01
  • Graphs
    • Efficacy
      • FSTG01
      • FSTG02
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      • MMRMG02
    • Other
      • BRG01
      • BWG01
      • CIG01
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      • MNG01
    • Pharmacokinetic
      • PKCG01
      • PKCG02
      • PKCG03
      • PKPG01
      • PKPG02
      • PKPG03
      • PKPG04
      • PKPG06

  • Appendix
    • Reproducibility

  • Index

On this page

  • Output
  • teal App
  • Reproducibility
    • Timestamp
    • Session Info
    • .lock file
  • Edit this page
  • Report an issue
  1. Tables
  2. Efficacy
  3. CFBT01

CFBT01

Efficacy Data and Change from Baseline by Visit


Output

  • Standard Table
  • Data Setup
  • Preview
  • Try this using WebR
Code
afun <- function(x, .var, .spl_context, ...) {
  n_fun <- sum(!is.na(x), na.rm = TRUE)
  if (n_fun == 0) {
    mean_sd_fun <- c(NA, NA)
    median_fun <- NA
    min_max_fun <- c(NA, NA)
  } else {
    mean_sd_fun <- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))
    median_fun <- median(x, na.rm = TRUE)
    min_max_fun <- c(min(x), max(x))
  }
  is_chg <- .var == "CHG"
  is_baseline <- .spl_context$value[which(.spl_context$split == "AVISIT")] == "BASELINE"
  if (is_baseline && is_chg) n_fun <- mean_sd_fun <- median_fun <- min_max_fun <- NULL

  in_rows(
    "n" = n_fun,
    "Mean (SD)" = mean_sd_fun,
    "Median" = median_fun,
    "Min - Max" = min_max_fun,
    .formats = list("n" = "xx", "Mean (SD)" = "xx.xx (xx.xx)", "Median" = "xx.xx", "Min - Max" = "xx.xx - xx.xx"),
    .format_na_strs = list("n" = "NE", "Mean (SD)" = "NE (NE)", "Median" = "NE", "Min - Max" = "NE - NE")
  )
}

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by(
    "PARAM",
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adqs$PARAM)
  ) %>%
  split_rows_by(
    "AVISIT",
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adqs$AVISIT)
  ) %>%
  split_cols_by_multivar(
    vars = c("AVAL", "CHG"),
    varlabels = c("Value at Visit", "Change from\nBaseline")
  ) %>%
  analyze_colvars(afun = afun)

result <- build_table(lyt, adqs)
result
                               A: Drug X                        B: Placebo                      C: Combination         
Parameter                             Change from                       Change from                       Change from  
  Analysis Visit    Value at Visit      Baseline      Value at Visit      Baseline      Value at Visit      Baseline   
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
BFI All Questions                                                                                                      
  BASELINE                                                                                                             
    n                    134                               134                               132                       
    Mean (SD)        49.93 (7.44)                      49.74 (8.29)                      50.30 (9.06)                  
    Median              48.69                             49.28                             49.74                      
    Min - Max       33.72 - 65.91                     25.82 - 71.47                     26.04 - 69.99                  
  WEEK 1 DAY 8                                                                                                         
    n                    134              134              134              134              132              132      
    Mean (SD)        54.51 (8.61)     4.58 (11.23)     56.33 (7.86)     6.59 (11.81)     54.03 (8.31)     3.72 (12.66) 
    Median              55.15             5.47            56.39             6.96            53.76             3.71     
    Min - Max       34.26 - 75.42    -25.52 - 29.68   36.82 - 74.45    -25.10 - 31.05   26.89 - 75.95    -26.81 - 33.75
  WEEK 2 DAY 15                                                                                                        
    n                    134              134              134              134              132              132      
    Mean (SD)       60.98 (10.31)    11.05 (12.46)     59.68 (9.59)     9.94 (12.84)     60.11 (8.76)     9.80 (12.59) 
    Median              60.60            10.45            58.18             8.95            61.08            10.04     
    Min - Max       35.94 - 96.53    -14.02 - 45.01   40.44 - 84.70    -17.59 - 40.52   32.76 - 78.25    -26.57 - 39.56
  WEEK 3 DAY 22                                                                                                        
    n                    134              134              134              134              132              132      
    Mean (SD)        64.64 (9.88)    14.72 (11.99)    65.78 (10.17)    16.04 (13.80)    63.65 (10.50)    13.35 (13.39) 
    Median              65.21            15.10            66.28            15.49            63.79            12.24     
    Min - Max       40.49 - 95.35    -16.76 - 43.40   42.16 - 92.32    -17.70 - 46.85   29.29 - 88.41    -22.40 - 39.94
  WEEK 4 DAY 29                                                                                                        
    n                    134              134              134              134              132              132      
    Mean (SD)       69.43 (11.12)    19.51 (13.83)    69.79 (11.46)    20.05 (14.38)    70.68 (10.23)    20.37 (12.99) 
    Median              69.22            19.90            70.37            20.99            70.85            20.94     
    Min - Max       38.38 - 95.48    -17.34 - 49.18   45.79 - 93.33    -19.66 - 58.05   30.53 - 90.61    -14.01 - 60.23
  WEEK 5 DAY 36                                                                                                        
    n                    134              134              134              134              132              132      
    Mean (SD)       74.31 (12.44)    24.38 (14.75)    74.73 (12.96)    24.99 (14.67)    75.89 (13.54)    25.59 (17.18) 
    Median              76.12            25.20            74.13            22.81            76.70            25.32     
    Min - Max       39.45 - 103.92   -7.53 - 56.23    38.19 - 109.61   -18.42 - 64.15   43.79 - 102.40   -16.11 - 67.78
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
adqs <- random.cdisc.data::cadqs

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

# This example focuses on "BFI All Questions" starting from baseline.
adqs <- adqs %>%
  dplyr::filter(
    PARAM == "BFI All Questions",
    AVISIT != "SCREENING"
  )

# Define the split function for AVISIT
split_fun <- drop_split_levels

teal App

  • Preview
  • Try this using shinylive
Code
library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- random.cdisc.data::cadsl
  ADQS <- random.cdisc.data::cadqs

  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
  ADSL <- df_explicit_na(ADSL)
  ADQS <- df_explicit_na(ADQS)
})
datanames <- c("ADSL", "ADQS")
datanames(data) <- datanames
Warning: `datanames<-()` was deprecated in teal.data 0.7.0.
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
ADSL <- data[["ADSL"]]
ADQS <- data[["ADQS"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary_by(
      label = "Efficacy Data and Change from Baseline by Visit",
      dataname = "ADQS",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADQS, c("PARAM", "AVISIT")),
        selected = c("AVISIT")
      ),
      summarize_vars = choices_selected(
        choices = variable_choices(ADQS, c("AVAL", "CHG")),
        selected = c("AVAL", "CHG")
      ),
      useNA = "ifany",
      paramcd = choices_selected(
        choices = value_choices(ADQS, "PARAMCD", "PARAM"),
        selected = "BFIALL"
      ),
      add_total = FALSE,
      parallel_vars = TRUE
    )
  ),
  filter = teal_slices(
    teal_slice("ADQS", "AVISIT", selected = NULL),
    teal_slice("ADQS", "AVAL", selected = NULL)
  )
)

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, {
  ADSL <- random.cdisc.data::cadsl
  ADQS <- random.cdisc.data::cadqs

  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
  ADSL <- df_explicit_na(ADSL)
  ADQS <- df_explicit_na(ADQS)
})
datanames <- c("ADSL", "ADQS")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary_by(
      label = "Efficacy Data and Change from Baseline by Visit",
      dataname = "ADQS",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADQS, c("PARAM", "AVISIT")),
        selected = c("AVISIT")
      ),
      summarize_vars = choices_selected(
        choices = variable_choices(ADQS, c("AVAL", "CHG")),
        selected = c("AVAL", "CHG")
      ),
      useNA = "ifany",
      paramcd = choices_selected(
        choices = value_choices(ADQS, "PARAMCD", "PARAM"),
        selected = "BFIALL"
      ),
      add_total = FALSE,
      parallel_vars = TRUE
    )
  ),
  filter = teal_slices(
    teal_slice("ADQS", "AVISIT", selected = NULL),
    teal_slice("ADQS", "AVAL", selected = NULL)
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2025-07-05 17:39:02 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     2025-07-05
 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
 broom                   1.0.8    2025-03-28 [1] RSPM
 bslib                   0.9.0    2025-01-30 [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                0.5.1    2025-04-24 [1] RSPM
 cli                     3.6.5    2025-04-23 [1] RSPM
 coda                    0.19-4.1 2024-01-31 [1] CRAN (R 4.5.0)
 codetools               0.2-20   2024-03-31 [2] CRAN (R 4.5.0)
 curl                    6.4.0    2025-06-22 [1] RSPM
 data.table              1.17.6   2025-06-17 [1] RSPM
 dichromat               2.0-0.1  2022-05-02 [1] CRAN (R 4.5.0)
 digest                  0.6.37   2024-08-19 [1] RSPM
 dplyr                 * 1.1.4    2023-11-17 [1] RSPM
 emmeans                 1.11.1   2025-05-04 [1] RSPM
 estimability            1.5.1    2024-05-12 [1] RSPM
 evaluate                1.0.4    2025-06-18 [1] RSPM
 farver                  2.1.2    2024-05-13 [1] RSPM
 fastmap                 1.2.0    2024-05-15 [1] RSPM
 fontawesome             0.5.3    2024-11-16 [1] RSPM
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 formatR                 1.14     2023-01-17 [1] CRAN (R 4.5.0)
 formatters            * 0.5.11   2025-04-09 [1] RSPM
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 generics                0.1.4    2025-05-09 [1] RSPM
 ggplot2                 3.5.2    2025-04-09 [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
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 httr                    1.4.7    2023-08-15 [1] RSPM
 jquerylib               0.1.4    2021-04-26 [1] RSPM
 jsonlite                2.0.0    2025-03-27 [1] RSPM
 knitr                   1.50     2025-03-16 [1] RSPM
 later                   1.4.2    2025-04-08 [1] RSPM
 lattice                 0.22-7   2025-04-02 [2] CRAN (R 4.5.0)
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 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                    7.3-65   2025-02-28 [2] CRAN (R 4.5.0)
 Matrix                  1.7-3    2025-03-11 [1] CRAN (R 4.5.0)
 memoise                 2.0.1    2021-11-26 [1] RSPM
 mime                    0.13     2025-03-17 [1] RSPM
 multcomp                1.4-28   2025-01-29 [1] RSPM
 mvtnorm                 1.3-3    2025-01-10 [1] RSPM
 nestcolor               0.1.3    2025-01-21 [1] RSPM
 nlme                    3.1-168  2025-03-31 [2] CRAN (R 4.5.0)
 pillar                  1.11.0   2025-07-04 [1] RSPM
 pkgcache                2.2.4    2025-05-26 [1] RSPM
 pkgconfig               2.0.3    2019-09-22 [1] RSPM
 plotly                  4.11.0   2025-06-19 [1] RSPM
 processx                3.8.6    2025-02-21 [1] RSPM
 promises                1.3.3    2025-05-29 [1] RSPM
 ps                      1.9.1    2025-04-12 [1] RSPM
 purrr                   1.0.4    2025-02-05 [1] RSPM
 R6                      2.6.1    2025-02-15 [1] RSPM
 random.cdisc.data       0.3.16   2024-10-10 [1] RSPM
 rbibutils               2.3      2024-10-04 [1] RSPM
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 sessioninfo             1.2.3    2025-02-05 [1] any (@1.2.3)
 shiny                 * 1.11.1   2025-07-03 [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            0.9.0    2025-02-21 [1] RSPM
 stringi                 1.8.7    2025-03-27 [1] RSPM
 stringr                 1.5.1    2023-11-14 [1] RSPM
 survival                3.8-3    2024-12-17 [2] CRAN (R 4.5.0)
 teal                  * 0.16.0   2025-02-23 [1] RSPM
 teal.code             * 0.6.1    2025-02-14 [1] RSPM
 teal.data             * 0.7.0    2025-01-28 [1] RSPM
 teal.logger             0.3.2    2025-02-14 [1] RSPM
 teal.modules.clinical * 0.10.0   2025-02-28 [1] RSPM
 teal.reporter           0.4.0    2025-01-24 [1] RSPM
 teal.slice            * 0.6.0    2025-02-03 [1] RSPM
 teal.transform        * 0.6.0    2025-02-12 [1] RSPM
 teal.widgets            0.4.3    2025-01-31 [1] RSPM
 tern                  * 0.9.9    2025-06-20 [1] RSPM
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 xtable                  1.8-4    2019-04-21 [1] RSPM
 yaml                    2.3.10   2024-07-26 [1] RSPM
 zoo                     1.8-14   2025-04-10 [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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AOVT03
CMHT01
Source Code
---
title: CFBT01
subtitle: Efficacy Data and Change from Baseline by Visit
---

------------------------------------------------------------------------

{{< include ../../_utils/envir_hook.qmd >}}

```{r setup, echo = FALSE, warning = FALSE, message = FALSE}
library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl
adqs <- random.cdisc.data::cadqs

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

# This example focuses on "BFI All Questions" starting from baseline.
adqs <- adqs %>%
  dplyr::filter(
    PARAM == "BFI All Questions",
    AVISIT != "SCREENING"
  )

# Define the split function for AVISIT
split_fun <- drop_split_levels
```

```{r include = FALSE}
webr_code_labels <- c("setup")
```

{{< include ../../_utils/webr_no_include.qmd >}}

## Output

:::: panel-tabset
## Standard Table

::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview

```{r variant1, test = list(result_v1 = "result")}
afun <- function(x, .var, .spl_context, ...) {
  n_fun <- sum(!is.na(x), na.rm = TRUE)
  if (n_fun == 0) {
    mean_sd_fun <- c(NA, NA)
    median_fun <- NA
    min_max_fun <- c(NA, NA)
  } else {
    mean_sd_fun <- c(mean(x, na.rm = TRUE), sd(x, na.rm = TRUE))
    median_fun <- median(x, na.rm = TRUE)
    min_max_fun <- c(min(x), max(x))
  }
  is_chg <- .var == "CHG"
  is_baseline <- .spl_context$value[which(.spl_context$split == "AVISIT")] == "BASELINE"
  if (is_baseline && is_chg) n_fun <- mean_sd_fun <- median_fun <- min_max_fun <- NULL

  in_rows(
    "n" = n_fun,
    "Mean (SD)" = mean_sd_fun,
    "Median" = median_fun,
    "Min - Max" = min_max_fun,
    .formats = list("n" = "xx", "Mean (SD)" = "xx.xx (xx.xx)", "Median" = "xx.xx", "Min - Max" = "xx.xx - xx.xx"),
    .format_na_strs = list("n" = "NE", "Mean (SD)" = "NE (NE)", "Median" = "NE", "Min - Max" = "NE - NE")
  )
}

lyt <- basic_table() %>%
  split_cols_by("ARM") %>%
  split_rows_by(
    "PARAM",
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adqs$PARAM)
  ) %>%
  split_rows_by(
    "AVISIT",
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adqs$AVISIT)
  ) %>%
  split_cols_by_multivar(
    vars = c("AVAL", "CHG"),
    varlabels = c("Value at Visit", "Change from\nBaseline")
  ) %>%
  analyze_colvars(afun = afun)

result <- build_table(lyt, adqs)
result
```

```{r include = FALSE}
webr_code_labels <- c("variant1")
```

{{< include ../../_utils/webr.qmd >}}
:::

## Data Setup

```{r setup}
#| code-fold: show
```
::::

{{< include ../../_utils/save_results.qmd >}}

## `teal` App

::: {.panel-tabset .nav-justified}
## {{< fa regular file-lines fa-sm fa-fw >}} Preview

```{r teal, opts.label = c("skip_if_testing", "app")}
library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- random.cdisc.data::cadsl
  ADQS <- random.cdisc.data::cadqs

  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
  ADSL <- df_explicit_na(ADSL)
  ADQS <- df_explicit_na(ADQS)
})
datanames <- c("ADSL", "ADQS")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary_by(
      label = "Efficacy Data and Change from Baseline by Visit",
      dataname = "ADQS",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADQS, c("PARAM", "AVISIT")),
        selected = c("AVISIT")
      ),
      summarize_vars = choices_selected(
        choices = variable_choices(ADQS, c("AVAL", "CHG")),
        selected = c("AVAL", "CHG")
      ),
      useNA = "ifany",
      paramcd = choices_selected(
        choices = value_choices(ADQS, "PARAMCD", "PARAM"),
        selected = "BFIALL"
      ),
      add_total = FALSE,
      parallel_vars = TRUE
    )
  ),
  filter = teal_slices(
    teal_slice("ADQS", "AVISIT", selected = NULL),
    teal_slice("ADQS", "AVAL", selected = NULL)
  )
)

shinyApp(app$ui, app$server)
```

{{< include ../../_utils/shinylive.qmd >}}
:::

{{< include ../../repro.qmd >}}

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