VST01

Vital Sign Results and Change from Baseline by Visit

Standard Table
Data Setup

Preview
Try this using WebR

Code

split_fun <- drop_split_levels

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(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", split_label = "\n\nAnalysis Visit") %>%
  split_cols_by_multivar(
    vars = c("AVAL", "CHG"),
    varlabels = c("Value at Visit", "Change from\nBaseline")
  ) %>%
  analyze_colvars(afun = afun)

result <- build_table(lyt = lyt, df = advs_f, alt_counts_df = adsl)
result

                                   A: Drug X                        B: Placebo                      C: Combination         
                                          Change from                       Change from                       Change from  
                        Value at Visit      Baseline      Value at Visit      Baseline      Value at Visit      Baseline   
Analysis Visit             (N=134)          (N=134)          (N=134)          (N=134)          (N=132)          (N=132)    
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
SCREENING                                                                                                                  
  n                          134               0               134               0               132               0       
  Mean (SD)             99.92 (17.99)       NE (NE)       101.88 (21.07)      NE (NE)       100.48 (19.07)      NE (NE)    
  Median                    99.26              NE             100.22             NE             99.10              NE      
  Min - Max             54.22 - 152.98      NE - NE       48.15 - 148.03      NE - NE       42.22 - 149.95      NE - NE    
BASELINE                                                                                                                   
  n                          134                               134                               132                       
  Mean (SD)             96.50 (19.90)                     101.10 (19.87)                    102.77 (19.48)                 
  Median                    96.05                             100.45                            102.01                     
  Min - Max             44.28 - 136.59                    29.21 - 143.77                    49.37 - 153.50                 
WEEK 1 DAY 8                                                                                                               
  n                          134              134              134              134              132              132      
  Mean (SD)             100.65 (18.79)    4.14 (26.93)    99.19 (19.36)    -1.92 (27.37)    97.14 (19.72)    -5.63 (25.95) 
  Median                    100.22            1.52            99.37            -2.79            94.19            -5.57     
  Min - Max             57.60 - 147.46   -50.92 - 74.84   59.28 - 141.21   -64.50 - 70.93   50.98 - 142.47   -55.15 - 56.11
WEEK 2 DAY 15                                                                                                              
  n                          134              134              134              134              132              132      
  Mean (SD)             102.09 (19.62)    5.58 (29.38)    99.29 (21.11)    -1.81 (31.11)    99.95 (20.85)    -2.81 (29.10) 
  Median                    103.59            7.82            100.57           -1.77            102.52           -5.68     
  Min - Max             54.73 - 150.85   -59.71 - 98.08   51.60 - 145.28   -71.00 - 92.43   37.06 - 138.92   -73.10 - 76.80
WEEK 3 DAY 22                                                                                                              
  n                          134              134              134              134              132              132      
  Mean (SD)             101.78 (19.54)    5.28 (27.16)    97.73 (19.85)    -3.37 (27.76)    99.86 (19.15)    -2.91 (27.25) 
  Median                    100.39            6.53            98.83            -5.79            100.06           -1.54     
  Min - Max             47.68 - 162.22   -64.46 - 76.64   36.25 - 142.78   -70.23 - 84.74   53.80 - 146.37   -74.30 - 67.46
WEEK 4 DAY 29                                                                                                              
  n                          134              134              134              134              132              132      
  Mean (SD)             100.18 (20.18)    3.68 (27.21)    99.06 (17.75)    -2.04 (26.59)    99.27 (20.12)    -3.50 (27.62) 
  Median                    98.17             3.00            97.98            -1.89            99.61            -0.46     
  Min - Max             48.52 - 153.41   -77.30 - 61.90   56.78 - 142.45   -82.96 - 89.87   46.87 - 146.12   -88.68 - 70.90
WEEK 5 DAY 36                                                                                                              
  n                          134              134              134              134              132              132      
  Mean (SD)             101.42 (18.83)    4.92 (28.02)    95.92 (19.90)    -5.19 (29.34)    97.73 (18.92)    -5.04 (26.64) 
  Median                    98.56             3.93            94.30            -7.18            98.09            -5.62     
  Min - Max             62.66 - 155.52   -63.05 - 78.66   51.06 - 151.52   -78.07 - 72.91   51.48 - 157.27   -76.13 - 86.42
Post-Baseline Last                                                                                                         
  n                          134              134              134              134              132              132      
  Mean (SD)             101.42 (18.83)    4.92 (28.02)    95.92 (19.90)    -5.19 (29.34)    97.73 (18.92)    -5.04 (26.64) 
  Median                    98.56             3.93            94.30            -7.18            98.09            -5.62     
  Min - Max             62.66 - 155.52   -63.05 - 78.66   51.06 - 151.52   -78.07 - 72.91   51.48 - 157.27   -76.13 - 86.42
Post-Baseline Minimum                                                                                                      
  n                          134              134              134              134              132              132      
  Mean (SD)             78.98 (11.94)    -17.52 (23.92)   75.77 (13.25)    -25.33 (25.39)   76.35 (13.13)    -26.41 (22.78)
  Median                    80.14            -13.96           75.71            -26.70           76.26            -27.17    
  Min - Max             47.68 - 110.54   -77.30 - 33.00   36.25 - 113.53   -82.96 - 69.23   37.06 - 112.35   -88.68 - 34.24
Post-Baseline Maximum                                                                                                      
  n                          134              134              134              134              132              132      
  Mean (SD)             125.08 (12.28)   28.57 (23.20)    121.33 (12.01)   20.23 (23.64)    121.42 (12.09)   18.65 (22.74) 
  Median                    124.20           29.14            120.80           18.74            122.55           16.93     
  Min - Max             94.06 - 162.22   -21.49 - 98.08   94.43 - 151.52   -34.95 - 92.43   79.39 - 157.27   -43.32 - 86.42

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
advs <- random.cdisc.data::cadvs

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

advs_label <- var_labels(advs)

advs <- advs %>%
  filter(
    PARAMCD == "DIABP",
    PARAM == "Diastolic Blood Pressure"
  ) %>%
  mutate(
    PARAMCD = droplevels(PARAMCD),
    PARAM = droplevels(PARAM)
  )

# post-baseline
advs_pb <- advs %>%
  filter(ABLFL != "Y", ABLFL2 != "Y")

advs_pb_max <- advs_pb %>%
  group_by(PARAM, USUBJID) %>%
  arrange(desc(AVAL)) %>%
  slice(1) %>%
  ungroup() %>%
  mutate(AVISIT = "Post-Baseline Maximum")

advs_pb_min <- advs_pb %>%
  group_by(PARAM, USUBJID) %>%
  arrange(AVAL) %>%
  slice(1) %>%
  ungroup() %>%
  mutate(AVISIT = "Post-Baseline Minimum")

advs_pb_last <- advs_pb %>%
  group_by(PARAM, USUBJID) %>%
  arrange(desc(AVISITN)) %>%
  slice(1) %>%
  ungroup() %>%
  mutate(AVISIT = "Post-Baseline Last")

# Please note that for real data, per ADaM Spec 1.1, the advs_f can be obtained by filtering on PARAMCD, then
# ANL01FL == 'Y' or AVISIT in c('POST-BASELINE MAXIMUM', 'POST-BASELINE MINIMUM', 'POST-BASELINE LAST')

advs_f <- rbind(
  advs,
  advs_pb_last,
  advs_pb_min,
  advs_pb_max
)

advs_f <- advs_f %>%
  mutate(AVISIT = droplevels(AVISIT))

var_labels(advs_f) <- advs_label

Code

library(teal.modules.clinical)

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

  ADSL <- random.cdisc.data::cadsl
  ADVS <- random.cdisc.data::cadvs
  advs_label <- col_labels(ADVS)

  # post-baseline
  advs_pb <- ADVS %>%
    filter(
      ABLFL != "Y",
      ABLFL2 != "Y"
    )

  advs_pb_max <- advs_pb %>%
    group_by(PARAM, USUBJID) %>%
    arrange(desc(AVAL)) %>%
    slice(1) %>%
    ungroup() %>%
    mutate(AVISIT = "Post-Baseline Maximum")

  advs_pb_min <- advs_pb %>%
    group_by(PARAM, USUBJID) %>%
    arrange(AVAL) %>%
    slice(1) %>%
    ungroup() %>%
    mutate(AVISIT = "Post-Baseline Minimum")

  advs_pb_last <- advs_pb %>%
    group_by(PARAM, USUBJID) %>%
    arrange(desc(AVISITN)) %>%
    slice(1) %>%
    ungroup() %>%
    mutate(AVISIT = "Post-Baseline Last")

  ADVS <- rbind(
    ADVS,
    advs_pb_last,
    advs_pb_min,
    advs_pb_max
  ) %>%
    mutate(AVISIT = droplevels(AVISIT))

  col_labels(ADVS) <- advs_label
})
datanames <- c("ADSL", "ADVS")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

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

shinyApp(app$ui, app$server)

Warning: The 'plotly_relayout' event tied a source ID of
'teal-main_ui-filter_panel-active-ADVS-filter-ADVS_AVAL-inputs-histogram_plot'
is not registered. In order to obtain this event data, please add
`event_register(p, 'plotly_relayout')` to the plot (`p`) that you wish to
obtain event data from.

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://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
  ADVS <- random.cdisc.data::cadvs
  advs_label <- col_labels(ADVS)

  # post-baseline
  advs_pb <- ADVS %>%
    filter(
      ABLFL != "Y",
      ABLFL2 != "Y"
    )

  advs_pb_max <- advs_pb %>%
    group_by(PARAM, USUBJID) %>%
    arrange(desc(AVAL)) %>%
    slice(1) %>%
    ungroup() %>%
    mutate(AVISIT = "Post-Baseline Maximum")

  advs_pb_min <- advs_pb %>%
    group_by(PARAM, USUBJID) %>%
    arrange(AVAL) %>%
    slice(1) %>%
    ungroup() %>%
    mutate(AVISIT = "Post-Baseline Minimum")

  advs_pb_last <- advs_pb %>%
    group_by(PARAM, USUBJID) %>%
    arrange(desc(AVISITN)) %>%
    slice(1) %>%
    ungroup() %>%
    mutate(AVISIT = "Post-Baseline Last")

  ADVS <- rbind(
    ADVS,
    advs_pb_last,
    advs_pb_min,
    advs_pb_max
  ) %>%
    mutate(AVISIT = droplevels(AVISIT))

  col_labels(ADVS) <- advs_label
})
datanames <- c("ADSL", "ADVS")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

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

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 18:07:33 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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 random.cdisc.data       0.3.16   2024-11-02 [1] https://insightsengineering.r-universe.dev (R 4.4.1)
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 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)
 viridisLite             0.4.2    2023-05-02 [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                   3.0.2    2024-10-28 [1] CRAN (R 4.4.1)
 xfun                    0.49     2024-10-31 [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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`.lock` file

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

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teal App

Reproducibility

Timestamp

Session Info

.lock file

`teal` App

`.lock` file