VST01

Vital Sign Results and Change from Baseline by Visit

Output

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

Warning: `datanames<-()` was deprecated in teal.data 0.6.1.
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"]]
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)

Experimental use!

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#| '!! 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://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
  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] "2024-11-20 17:52:10 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