VST02

Vital Sign Abnormalities

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Code

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  add_overall_col("All Patients") %>%
  split_rows_by("PARAM", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(advs_f$PARAM)) %>%
  count_abnormal(
    "ANRIND",
    abnormal = list(Low = c("LOW", "LOW LOW"), High = c("HIGH", "HIGH HIGH")),
    exclude_base_abn = FALSE
  ) %>%
  append_varlabels(advs_f, "ANRIND", indent = 1L)

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

result

Assessment                    A: Drug X        B: Placebo      C: Combination     All Patients  
  Abnormality                  (N=134)           (N=134)           (N=132)           (N=400)    
————————————————————————————————————————————————————————————————————————————————————————————————
Diastolic Blood Pressure                                                                        
  Low                      66/134 (49.3%)    86/134 (64.2%)    78/132 (59.1%)    230/400 (57.5%)
  High                     88/134 (65.7%)    70/134 (52.2%)    76/132 (57.6%)    234/400 (58.5%)
Pulse Rate                                                                                      
  Low                      58/134 (43.3%)    32/134 (23.9%)    48/132 (36.4%)    138/400 (34.5%)
  High                     53/134 (39.6%)    49/134 (36.6%)    44/132 (33.3%)    146/400 (36.5%)
Respiratory Rate                                                                                
  Low                      97/134 (72.4%)    84/134 (62.7%)    98/132 (74.2%)    279/400 (69.8%)
  High                     96/134 (71.6%)    98/134 (73.1%)    98/132 (74.2%)     292/400 (73%) 
Systolic Blood Pressure                                                                         
  Low                       71/134 (53%)     77/134 (57.5%)    73/132 (55.3%)    221/400 (55.2%)
  High                     78/134 (58.2%)    74/134 (55.2%)    73/132 (55.3%)    225/400 (56.2%)
Temperature                                                                                     
  Low                      108/134 (80.6%)   110/134 (82.1%)   108/132 (81.8%)   326/400 (81.5%)
  High                     112/134 (83.6%)   106/134 (79.1%)   107/132 (81.1%)   325/400 (81.2%)
Weight                                                                                          
  Low                      43/134 (32.1%)    39/134 (29.1%)    39/132 (29.5%)    121/400 (30.2%)
  High                     46/134 (34.3%)    43/134 (32.1%)    44/132 (33.3%)    133/400 (33.2%)

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(show_colcounts = TRUE) %>%
  split_cols_by(var = "ACTARM") %>%
  add_overall_col("All Patients") %>%
  split_rows_by("PARAM", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(advs_f$PARAM)) %>%
  count_abnormal(
    "ANRIND",
    abnormal = list(Low = c("LOW", "LOW LOW"), High = c("HIGH", "HIGH HIGH")),
    exclude_base_abn = TRUE
  ) %>%
  append_varlabels(advs_f, "ANRIND", indent = 1L)

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

result

Assessment                   A: Drug X        B: Placebo     C: Combination    All Patients  
  Abnormality                 (N=134)          (N=134)          (N=132)           (N=400)    
—————————————————————————————————————————————————————————————————————————————————————————————
Diastolic Blood Pressure                                                                     
  Low                       54/108 (50%)    76/116 (65.5%)   68/114 (59.6%)   198/338 (58.6%)
  High                     74/114 (64.9%)   56/109 (51.4%)   62/106 (58.5%)   192/329 (58.4%)
Pulse Rate                                                                                   
  Low                      53/121 (43.8%)    30/125 (24%)    42/119 (35.3%)   125/365 (34.2%)
  High                      48/123 (39%)    45/116 (38.8%)   39/120 (32.5%)   132/359 (36.8%)
Respiratory Rate                                                                             
  Low                       72/100 (72%)    72/110 (65.5%)   79/102 (77.5%)   223/312 (71.5%)
  High                      77/107 (72%)    78/108 (72.2%)   79/109 (72.5%)   234/324 (72.2%)
Systolic Blood Pressure                                                                      
  Low                       62/117 (53%)    63/114 (55.3%)   57/103 (55.3%)   182/334 (54.5%)
  High                     64/110 (58.2%)   67/120 (55.8%)   68/115 (59.1%)   199/345 (57.7%)
Temperature                                                                                  
  Low                      77/96 (80.2%)     78/94 (83%)     74/90 (82.2%)    229/280 (81.8%)
  High                     78/93 (83.9%)    73/92 (79.3%)     81/100 (81%)    232/285 (81.4%)
Weight                                                                                       
  Low                      42/129 (32.6%)   37/125 (29.6%)   37/123 (30.1%)   116/377 (30.8%)
  High                     41/126 (32.5%)   41/124 (33.1%)   42/121 (34.7%)   124/371 (33.4%)

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.

Code

library(dplyr)
library(tern)

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)

# Note:  We keep only post-baseline for analysis.
advs_f <- advs %>%
  filter(ONTRTFL == "Y", ANRIND != "<Missing>") %>%
  var_relabel(
    PARAM = "Assessment",
    ANRIND = "Abnormality"
  )

split_fun <- drop_split_levels

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 %>%
    mutate(ONTRTFL = ifelse(AVISIT %in% c("SCREENING", "BASELINE"), "", "Y")) %>%
    col_relabel(ONTRTFL = "On Treatment Record Flag")
})
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_abnormality(
      label = "Abnormality Table",
      dataname = "ADVS",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, subset = c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADVS, subset = c("VSCAT", "PARAM", "AVISIT")),
        selected = c("PARAM"),
        keep_order = TRUE
      ),
      grade = choices_selected(
        choices = variable_choices(ADVS, subset = "ANRIND"),
        selected = "ANRIND",
        fixed = TRUE
      ),
      abnormal = list(Low = c("LOW", "LOW LOW"), High = c("HIGH", "HIGH HIGH")),
      exclude_base_abn = FALSE
    )
  )
)

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://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 %>%
    mutate(ONTRTFL = ifelse(AVISIT %in% c("SCREENING", "BASELINE"), "", "Y")) %>%
    col_relabel(ONTRTFL = "On Treatment Record Flag")
})
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_abnormality(
      label = "Abnormality Table",
      dataname = "ADVS",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, subset = c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADVS, subset = c("VSCAT", "PARAM", "AVISIT")),
        selected = c("PARAM"),
        keep_order = TRUE
      ),
      grade = choices_selected(
        choices = variable_choices(ADVS, subset = "ANRIND"),
        selected = "ANRIND",
        fixed = TRUE
      ),
      abnormal = list(Low = c("LOW", "LOW LOW"), High = c("HIGH", "HIGH HIGH")),
      exclude_base_abn = FALSE
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 18:05:54 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 ───────────────────────────────────────────────────────────────────
 package               * version  date (UTC) lib source
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 brio                    1.1.5    2024-04-24 [1] CRAN (R 4.4.1)
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 bslib                   0.8.0    2024-07-29 [1] CRAN (R 4.4.1)
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 callr                   3.7.6    2024-03-25 [1] CRAN (R 4.4.1)
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 magrittr              * 2.0.3    2022-03-30 [1] CRAN (R 4.4.1)
 MASS                    7.3-61   2024-06-13 [2] CRAN (R 4.4.1)
 Matrix                  1.7-0    2024-04-26 [1] CRAN (R 4.4.1)
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 mime                    0.12     2021-09-28 [1] CRAN (R 4.4.1)
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 munsell                 0.5.1    2024-04-01 [1] CRAN (R 4.4.1)
 mvtnorm                 1.3-1    2024-09-03 [1] CRAN (R 4.4.1)
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 pkgconfig               2.0.3    2019-09-22 [1] CRAN (R 4.4.1)
 processx                3.8.4    2024-03-16 [1] CRAN (R 4.4.1)
 promises                1.3.0    2024-04-05 [1] CRAN (R 4.4.1)
 ps                      1.8.1    2024-10-28 [1] CRAN (R 4.4.1)
 purrr                   1.0.2    2023-08-10 [1] CRAN (R 4.4.1)
 R6                      2.5.1    2021-08-19 [1] CRAN (R 4.4.1)
 random.cdisc.data       0.3.16   2024-11-02 [1] https://insightsengineering.r-universe.dev (R 4.4.1)
 rbibutils               2.3      2024-10-04 [1] CRAN (R 4.4.1)
 Rcpp                    1.0.13   2024-07-17 [1] CRAN (R 4.4.1)
 Rdpack                  2.6.1    2024-08-06 [1] CRAN (R 4.4.1)
 rlang                   1.1.4    2024-06-04 [1] CRAN (R 4.4.1)
 rmarkdown               2.28     2024-08-17 [1] CRAN (R 4.4.1)
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 sessioninfo             1.2.2    2021-12-06 [1] CRAN (R 4.4.1)
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 teal                  * 0.15.2   2024-03-07 [1] CRAN (R 4.4.1)
 teal.code             * 0.5.0    2024-01-11 [1] CRAN (R 4.4.1)
 teal.data             * 0.6.0    2024-04-30 [1] CRAN (R 4.4.1)
 teal.logger             0.3.0    2024-11-02 [1] https://insightsengineering.r-universe.dev (R 4.4.1)
 teal.modules.clinical * 0.9.1    2024-04-27 [1] CRAN (R 4.4.1)
 teal.reporter           0.3.1    2024-03-15 [1] CRAN (R 4.4.1)
 teal.slice            * 0.5.1    2024-04-30 [1] CRAN (R 4.4.1)
 teal.transform        * 0.5.0    2024-02-17 [1] CRAN (R 4.4.1)
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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

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Reproducibility

Timestamp

Session Info

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`teal` App

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