EGT05_QTCAT

ECG Actual Values and Changes from Baseline by Visit

Code
split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  split_rows_by(
    "PARAM",
    split_label = obj_label(adeg_f$PARAM),
    split_fun = split_fun,
    label_pos = "topleft"
  ) %>%
  split_rows_by(
    "AVISIT",
    split_label = obj_label(adeg_f$AVISIT),
    split_fun = split_fun,
    label_pos = "topleft"
  ) %>%
  analyze_vars(
    vars = c("AVALCAT1", "CHGCAT1"),
    var_labels = c("Value at Visit", "Change from Baseline")
  ) %>%
  append_topleft("    Category")

result <- build_table(lyt = lyt, df = adeg_f, alt_counts_df = adsl) %>%
  prune_table()

result
Parameter                                                             
  Analysis Visit             A: Drug X    B: Placebo    C: Combination
    Category                  (N=134)       (N=134)        (N=132)    
——————————————————————————————————————————————————————————————————————
QT Duration                                                           
  BASELINE                                                            
    Value at Visit                                                    
      n                         134           134            132      
      <=450 msec            115 (85.8%)   117 (87.3%)    104 (78.8%)  
      >450 to <=480 msec     6 (4.5%)      10 (7.5%)       9 (6.8%)   
      >480 to <= 500 msec     4 (3%)       3 (2.2%)        6 (4.5%)   
      >500 msec              9 (6.7%)       4 (3%)        13 (9.8%)   
  WEEK 1 DAY 8                                                        
    Value at Visit                                                    
      n                         134           134            132      
      <=450 msec            113 (84.3%)   106 (79.1%)    106 (80.3%)  
      >450 to <=480 msec     10 (7.5%)     10 (7.5%)      11 (8.3%)   
      >480 to <= 500 msec     4 (3%)        4 (3%)         3 (2.3%)   
      >500 msec              7 (5.2%)     14 (10.4%)      12 (9.1%)   
    Change from Baseline                                              
      n                         134           134            132      
      <=30 msec             76 (56.7%)     75 (56%)       75 (56.8%)  
      >30 to <=60 msec       7 (5.2%)      13 (9.7%)      11 (8.3%)   
      >60 msec              51 (38.1%)    46 (34.3%)      46 (34.8%)  
  WEEK 2 DAY 15                                                       
    Value at Visit                                                    
      n                         134           134            132      
      <=450 msec            111 (82.8%)   114 (85.1%)    112 (84.8%)  
      >450 to <=480 msec     10 (7.5%)     9 (6.7%)        9 (6.8%)   
      >480 to <= 500 msec    7 (5.2%)      2 (1.5%)        5 (3.8%)   
      >500 msec              6 (4.5%)      9 (6.7%)        6 (4.5%)   
    Change from Baseline                                              
      n                         134           134            132      
      <=30 msec              71 (53%)     87 (64.9%)      89 (67.4%)  
      >30 to <=60 msec       11 (8.2%)     9 (6.7%)        9 (6.8%)   
      >60 msec              52 (38.8%)    38 (28.4%)      34 (25.8%)  
  WEEK 3 DAY 22                                                       
    Value at Visit                                                    
      n                         134           134            132      
      <=450 msec            106 (79.1%)   112 (83.6%)    118 (89.4%)  
      >450 to <=480 msec     13 (9.7%)     7 (5.2%)        3 (2.3%)   
      >480 to <= 500 msec     4 (3%)       5 (3.7%)        2 (1.5%)   
      >500 msec              11 (8.2%)     10 (7.5%)       9 (6.8%)   
    Change from Baseline                                              
      n                         134           134            132      
      <=30 msec              63 (47%)     80 (59.7%)      81 (61.4%)  
      >30 to <=60 msec      14 (10.4%)      8 (6%)        11 (8.3%)   
      >60 msec              57 (42.5%)    46 (34.3%)      40 (30.3%)  
  WEEK 4 DAY 29                                                       
    Value at Visit                                                    
      n                         134           134            132      
      <=450 msec            117 (87.3%)   103 (76.9%)    114 (86.4%)  
      >450 to <=480 msec     7 (5.2%)     14 (10.4%)       6 (4.5%)   
      >480 to <= 500 msec     4 (3%)       7 (5.2%)        3 (2.3%)   
      >500 msec              6 (4.5%)      10 (7.5%)       9 (6.8%)   
    Change from Baseline                                              
      n                         134           134            132      
      <=30 msec              79 (59%)     80 (59.7%)      79 (59.8%)  
      >30 to <=60 msec       11 (8.2%)     7 (5.2%)       10 (7.6%)   
      >60 msec              44 (32.8%)    47 (35.1%)      43 (32.6%)  
  WEEK 5 DAY 36                                                       
    Value at Visit                                                    
      n                         134           134            132      
      <=450 msec            107 (79.9%)   117 (87.3%)    112 (84.8%)  
      >450 to <=480 msec    16 (11.9%)     5 (3.7%)       13 (9.8%)   
      >480 to <= 500 msec    5 (3.7%)      9 (6.7%)        3 (2.3%)   
      >500 msec              6 (4.5%)      3 (2.2%)         4 (3%)    
    Change from Baseline                                              
      n                         134           134            132      
      <=30 msec             72 (53.7%)    82 (61.2%)      73 (55.3%)  
      >30 to <=60 msec       10 (7.5%)     11 (8.2%)      11 (8.3%)   
      >60 msec              52 (38.8%)    41 (30.6%)      48 (36.4%)

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Code
library(tern)
library(dplyr)

adsl <- random.cdisc.data::cadsl
adeg <- random.cdisc.data::cadeg

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

adeg_labels <- var_labels(adeg)
adeg_f <- adeg %>%
  filter(
    PARAMCD == "QT",
    ANL01FL == "Y"
  ) %>%
  mutate(
    AVALCAT1 = case_when(
      AVAL <= 450 ~ "<=450 msec",
      AVAL <= 480 ~ ">450 to <=480 msec",
      AVAL <= 500 ~ ">480 to <= 500 msec",
      AVAL > 500 ~ ">500 msec",
      is.na(AVAL) ~ "<Missing>"
    ),
    CHGCAT1 = case_when(
      CHG <= 30 ~ "<=30 msec",
      CHG <= 60 ~ ">30 to <=60 msec",
      CHG > 60 ~ ">60 msec",
      is.na(CHG) ~ "<Missing>"
    )
  ) %>%
  mutate(
    AVALCAT1 = factor(
      AVALCAT1,
      levels = c(
        "<=450 msec",
        ">450 to <=480 msec",
        ">480 to <= 500 msec",
        ">500 msec",
        "<Missing>"
      )
    ),
    CHGCAT1 = factor(
      CHGCAT1,
      levels = c(
        "<=30 msec",
        ">30 to <=60 msec",
        ">60 msec",
        "<Missing>"
      )
    )
  ) %>%
  var_relabel(
    AVALCAT1 = "Value at Visit",
    CHGCAT1 = "Change from Baseline"
  )

teal App

Code
library(teal.modules.clinical)

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

  ADSL <- random.cdisc.data::cadsl
  ADEG <- random.cdisc.data::cadeg

  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
  ADSL <- df_explicit_na(ADSL)
  ADEG <- df_explicit_na(ADEG)
  adeg_labels <- col_labels(ADEG)

  ADEG <- ADEG %>%
    filter(
      ANL01FL == "Y" # no need to filter for PARAMCD here
    ) %>%
    mutate(
      AVALCAT1 = case_when(
        AVAL <= 450 ~ "<=450 msec",
        AVAL <= 480 ~ ">450 to <=480 msec",
        AVAL <= 500 ~ ">480 to <= 500 msec",
        AVAL > 500 ~ ">500 msec",
        is.na(AVAL) ~ "<Missing>"
      ),
      CHGCAT1 = case_when(
        CHG <= 30 ~ "<=30 msec",
        CHG <= 60 ~ ">30 to <=60 msec",
        CHG > 60 ~ ">60 msec",
        is.na(CHG) ~ "<Missing>"
      )
    ) %>%
    mutate(
      AVALCAT1 = factor(
        AVALCAT1,
        levels = c(
          "<=450 msec",
          ">450 to <=480 msec",
          ">480 to <= 500 msec",
          ">500 msec",
          "<Missing>"
        )
      ),
      CHGCAT1 = factor(
        CHGCAT1,
        levels = c(
          "<=30 msec",
          ">30 to <=60 msec",
          ">60 msec",
          "<Missing>"
        )
      )
    )
  col_labels(ADEG) <- c(
    adeg_labels,
    "AVALCAT1" = "Value at Visit",
    "CHGCAT1" = "Change from Baseline"
  )
})
datanames <- c("ADSL", "ADEG")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary_by(
      label = "ECG Actual Values and Changes from Baseline by Visit",
      dataname = "ADEG",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADEG, c("PARAM", "AVISIT")),
        selected = c("AVISIT")
      ),
      summarize_vars = choices_selected(
        choices = variable_choices(ADEG, c("AVALCAT1", "CHGCAT1")),
        selected = c("AVALCAT1", "CHGCAT1")
      ),
      useNA = "ifany",
      paramcd = choices_selected(
        choices = value_choices(ADEG, "PARAMCD", "PARAM"),
        selected = "QT"
      )
    )
  )
)

shinyApp(app$ui, app$server)

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
  ADEG <- random.cdisc.data::cadeg

  # Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
  ADSL <- df_explicit_na(ADSL)
  ADEG <- df_explicit_na(ADEG)
  adeg_labels <- col_labels(ADEG)

  ADEG <- ADEG %>%
    filter(
      ANL01FL == "Y" # no need to filter for PARAMCD here
    ) %>%
    mutate(
      AVALCAT1 = case_when(
        AVAL <= 450 ~ "<=450 msec",
        AVAL <= 480 ~ ">450 to <=480 msec",
        AVAL <= 500 ~ ">480 to <= 500 msec",
        AVAL > 500 ~ ">500 msec",
        is.na(AVAL) ~ "<Missing>"
      ),
      CHGCAT1 = case_when(
        CHG <= 30 ~ "<=30 msec",
        CHG <= 60 ~ ">30 to <=60 msec",
        CHG > 60 ~ ">60 msec",
        is.na(CHG) ~ "<Missing>"
      )
    ) %>%
    mutate(
      AVALCAT1 = factor(
        AVALCAT1,
        levels = c(
          "<=450 msec",
          ">450 to <=480 msec",
          ">480 to <= 500 msec",
          ">500 msec",
          "<Missing>"
        )
      ),
      CHGCAT1 = factor(
        CHGCAT1,
        levels = c(
          "<=30 msec",
          ">30 to <=60 msec",
          ">60 msec",
          "<Missing>"
        )
      )
    )
  col_labels(ADEG) <- c(
    adeg_labels,
    "AVALCAT1" = "Value at Visit",
    "CHGCAT1" = "Change from Baseline"
  )
})
datanames <- c("ADSL", "ADEG")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary_by(
      label = "ECG Actual Values and Changes from Baseline by Visit",
      dataname = "ADEG",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        choices = variable_choices(ADEG, c("PARAM", "AVISIT")),
        selected = c("AVISIT")
      ),
      summarize_vars = choices_selected(
        choices = variable_choices(ADEG, c("AVALCAT1", "CHGCAT1")),
        selected = c("AVALCAT1", "CHGCAT1")
      ),
      useNA = "ifany",
      paramcd = choices_selected(
        choices = value_choices(ADEG, "PARAMCD", "PARAM"),
        selected = "QT"
      )
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 18:39:46 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)

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