LBT03

Laboratory Test Results Change from Baseline by Visit

---

Output

Standard Table

The LBT03 template is the result of a junction between the analysis of AVAL at baseline and CHG at visit time. AVAL is summarized for baseline visits and and CHG is summarized for post-baseline visits.

Preview

# Define the split function
split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  split_rows_by("AVISIT", split_fun = split_fun, label_pos = "topleft", split_label = obj_label(adlb_f$AVISIT)) %>%
  summarize_change(
    "CHG",
    variables = list(value = "AVAL", baseline_flag = "ABLFLL"),
    na.rm = TRUE
  )

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

result

                  A: Drug X      B: Placebo    C: Combination
Analysis Visit     (N=134)        (N=134)         (N=132)    
—————————————————————————————————————————————————————————————
BASELINE                                                     
  n                  134            134             132      
  Mean (SD)      9.06 (0.93)    8.99 (0.98)     8.98 (0.89)  
  Median             9.07           8.92            8.96     
  Min - Max      6.21 - 11.87   6.23 - 11.63    6.24 - 11.18 
WEEK 1 DAY 8                                                 
  n                  134             0              132      
  Mean (SD)      -0.05 (1.38)        NA         -0.02 (1.30) 
  Median            -0.17            NA             0.02     
  Min - Max      -3.56 - 3.48        NA         -3.28 - 3.33 
WEEK 2 DAY 15                                                
  n                  134            134             132      
  Mean (SD)      -0.19 (1.47)   0.01 (1.45)     0.15 (1.25)  
  Median            -0.27          -0.00            0.15     
  Min - Max      -4.53 - 4.45   -3.79 - 3.43    -2.92 - 3.28 
WEEK 3 DAY 22                                                
  n                  134            134             132      
  Mean (SD)      0.03 (1.38)    -0.02 (1.49)    0.02 (1.34)  
  Median             0.15          -0.04            0.20     
  Min - Max      -3.95 - 2.99   -4.28 - 4.24    -2.76 - 3.26 
WEEK 4 DAY 29                                                
  n                  134            134             132      
  Mean (SD)      -0.26 (1.45)   0.05 (1.24)     -0.01 (1.17) 
  Median            -0.37           0.10           -0.06     
  Min - Max      -3.74 - 4.15   -3.34 - 3.71    -3.06 - 3.22 
WEEK 5 DAY 36                                                
  n                  134            134             132      
  Mean (SD)      -0.02 (1.50)   0.07 (1.34)     0.03 (1.27)  
  Median             0.01           0.15            0.05     
  Min - Max      -4.15 - 3.96   -3.50 - 3.53    -3.63 - 4.78 

Try this using WebR

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.

In the final step, a new variable is derived from AVISIT that can specify the method of estimation of the evaluated change.

Preview

adlb_f <- adlb_f %>% mutate(AVISIT_header = recode(AVISIT,
  "BASELINE" = "BASELINE",
  "WEEK 1 DAY 8" = "WEEK 1 DAY 8 value minus baseline",
  "WEEK 2 DAY 15" = "WEEK 2 DAY 15 value minus baseline",
  "WEEK 3 DAY 22" = "WEEK 3 DAY 22 value minus baseline",
  "WEEK 4 DAY 29" = "WEEK 4 DAY 29 value minus baseline",
  "WEEK 5 DAY 36" = "WEEK 5 DAY 36 value minus baseline"
))

# Define the split function
split_fun <- drop_split_levels

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARM") %>%
  split_rows_by("AVISIT_header",
    split_fun = split_fun,
    label_pos = "topleft",
    split_label = obj_label(adlb_f$AVISIT)
  ) %>%
  summarize_change(
    "CHG",
    variables = list(value = "AVAL", baseline_flag = "ABLFLL"),
    na.rm = TRUE
  )

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

result

                                      A: Drug X      B: Placebo    C: Combination
Analysis Visit                         (N=134)        (N=134)         (N=132)    
—————————————————————————————————————————————————————————————————————————————————
BASELINE                                                                         
  n                                      134            134             132      
  Mean (SD)                          9.06 (0.93)    8.99 (0.98)     8.98 (0.89)  
  Median                                 9.07           8.92            8.96     
  Min - Max                          6.21 - 11.87   6.23 - 11.63    6.24 - 11.18 
WEEK 1 DAY 8 value minus baseline                                                
  n                                      134             0              132      
  Mean (SD)                          -0.05 (1.38)        NA         -0.02 (1.30) 
  Median                                -0.17            NA             0.02     
  Min - Max                          -3.56 - 3.48        NA         -3.28 - 3.33 
WEEK 2 DAY 15 value minus baseline                                               
  n                                      134            134             132      
  Mean (SD)                          -0.19 (1.47)   0.01 (1.45)     0.15 (1.25)  
  Median                                -0.27          -0.00            0.15     
  Min - Max                          -4.53 - 4.45   -3.79 - 3.43    -2.92 - 3.28 
WEEK 3 DAY 22 value minus baseline                                               
  n                                      134            134             132      
  Mean (SD)                          0.03 (1.38)    -0.02 (1.49)    0.02 (1.34)  
  Median                                 0.15          -0.04            0.20     
  Min - Max                          -3.95 - 2.99   -4.28 - 4.24    -2.76 - 3.26 
WEEK 4 DAY 29 value minus baseline                                               
  n                                      134            134             132      
  Mean (SD)                          -0.26 (1.45)   0.05 (1.24)     -0.01 (1.17) 
  Median                                -0.37           0.10           -0.06     
  Min - Max                          -3.74 - 4.15   -3.34 - 3.71    -3.06 - 3.22 
WEEK 5 DAY 36 value minus baseline                                               
  n                                      134            134             132      
  Mean (SD)                          -0.02 (1.50)   0.07 (1.34)     0.03 (1.27)  
  Median                                 0.01           0.15            0.05     
  Min - Max                          -4.15 - 3.96   -3.50 - 3.53    -3.63 - 4.78 

Try this using WebR

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.

Data Setup

For illustration purposes, this example focuses on “C-Reactive Protein Measurement” starting from baseline, while excluding visit at week 1 for subjects who were randomized to the placebo group.

library(dplyr)
library(tern)

adsl <- random.cdisc.data::cadsl
adlb <- random.cdisc.data::cadlb

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

saved_labels <- var_labels(adlb)

adlb_f <- adlb %>%
  filter(
    PARAM == "C-Reactive Protein Measurement",
    !(ARM == "B: Placebo" & AVISIT == "WEEK 1 DAY 8"),
    AVISIT != "SCREENING"
  ) %>%
  dplyr::mutate(
    AVISIT = droplevels(AVISIT),
    ABLFLL = ABLFL == "Y"
  )

var_labels(adlb_f) <- c(saved_labels, "")

teal App

Preview

Here, we pre-process and manually define the variable “Baseline or Absolute Change from Baseline”.

library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- df_explicit_na(random.cdisc.data::cadsl)
  ADLB <- df_explicit_na(random.cdisc.data::cadlb) %>%
    filter(
      !(ARM == "B: Placebo" & AVISIT == "WEEK 1 DAY 8"),
      AVISIT != "SCREENING"
    ) %>%
    mutate(
      AVISIT = droplevels(AVISIT),
      ABLFLL = ABLFL == "Y",
      AVISIT_header = recode(AVISIT,
        "BASELINE" = "BASELINE",
        "WEEK 1 DAY 8" = "WEEK 1 DAY 8 value minus baseline",
        "WEEK 2 DAY 15" = "WEEK 2 DAY 15 value minus baseline",
        "WEEK 3 DAY 22" = "WEEK 3 DAY 22 value minus baseline",
        "WEEK 4 DAY 29" = "WEEK 4 DAY 29 value minus baseline",
        "WEEK 5 DAY 36" = "WEEK 5 DAY 36 value minus baseline"
      )
    ) %>%
    group_by(USUBJID, PARAM) %>%
    mutate(
      AVAL_CHG = AVAL - (!ABLFLL) * sum(AVAL * ABLFLL)
    ) %>%
    ungroup() %>%
    col_relabel(
      AVAL_CHG = "Baseline or Absolute Change from Baseline",
      ABLFLL = "Baseline Flag (TRUE/FALSE)",
      AVISIT_header = "Analysis Visit"
    )
})
datanames <- c("ADSL", "ADLB")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary_by(
      label = "Laboratory Test Results Change from Baseline by Visit",
      dataname = "ADLB",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        # note: order matters here. If `PARAM` is first, the split will be first by `PARAM`and then by `AVISIT`
        choices = variable_choices(ADLB, c("PARAM", "AVISIT_header")),
        selected = c("PARAM", "AVISIT_header")
      ),
      summarize_vars = choices_selected(
        choices = variable_choices(ADLB, c("AVAL", "CHG", "AVAL_CHG")),
        selected = c("AVAL_CHG")
      ),
      useNA = "ifany",
      paramcd = choices_selected(
        choices = value_choices(ADLB, "PARAMCD", "PARAM"),
        selected = "CRP"
      )
    )
  ),
  filter = teal_slices(teal_slice("ADLB", "AVAL", selected = NULL))
)
shinyApp(app$ui, app$server)

Warning: The 'plotly_relayout' event tied a source ID of
'teal-main_ui-filter_panel-active-ADLB-filter-ADLB_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.

Try this using shinylive

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 <- df_explicit_na(random.cdisc.data::cadsl)
  ADLB <- df_explicit_na(random.cdisc.data::cadlb) %>%
    filter(
      !(ARM == "B: Placebo" & AVISIT == "WEEK 1 DAY 8"),
      AVISIT != "SCREENING"
    ) %>%
    mutate(
      AVISIT = droplevels(AVISIT),
      ABLFLL = ABLFL == "Y",
      AVISIT_header = recode(AVISIT,
        "BASELINE" = "BASELINE",
        "WEEK 1 DAY 8" = "WEEK 1 DAY 8 value minus baseline",
        "WEEK 2 DAY 15" = "WEEK 2 DAY 15 value minus baseline",
        "WEEK 3 DAY 22" = "WEEK 3 DAY 22 value minus baseline",
        "WEEK 4 DAY 29" = "WEEK 4 DAY 29 value minus baseline",
        "WEEK 5 DAY 36" = "WEEK 5 DAY 36 value minus baseline"
      )
    ) %>%
    group_by(USUBJID, PARAM) %>%
    mutate(
      AVAL_CHG = AVAL - (!ABLFLL) * sum(AVAL * ABLFLL)
    ) %>%
    ungroup() %>%
    col_relabel(
      AVAL_CHG = "Baseline or Absolute Change from Baseline",
      ABLFLL = "Baseline Flag (TRUE/FALSE)",
      AVISIT_header = "Analysis Visit"
    )
})
datanames <- c("ADSL", "ADLB")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_t_summary_by(
      label = "Laboratory Test Results Change from Baseline by Visit",
      dataname = "ADLB",
      arm_var = choices_selected(
        choices = variable_choices(ADSL, c("ARM", "ARMCD")),
        selected = "ARM"
      ),
      by_vars = choices_selected(
        # note: order matters here. If `PARAM` is first, the split will be first by `PARAM`and then by `AVISIT`
        choices = variable_choices(ADLB, c("PARAM", "AVISIT_header")),
        selected = c("PARAM", "AVISIT_header")
      ),
      summarize_vars = choices_selected(
        choices = variable_choices(ADLB, c("AVAL", "CHG", "AVAL_CHG")),
        selected = c("AVAL_CHG")
      ),
      useNA = "ifany",
      paramcd = choices_selected(
        choices = value_choices(ADLB, "PARAMCD", "PARAM"),
        selected = "CRP"
      )
    )
  ),
  filter = teal_slices(teal_slice("ADLB", "AVAL", selected = NULL))
)
shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-12-21 17:45:07 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     2024-12-21
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package               * version  date (UTC) lib source
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 plotly                  4.10.4   2024-01-13 [1] RSPM
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 R6                      2.5.1    2021-08-19 [1] RSPM
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 shinycssloaders         1.1.0    2024-07-30 [1] RSPM
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 shinyWidgets            0.8.7    2024-09-23 [1] RSPM
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 stringr                 1.5.1    2023-11-14 [1] RSPM
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 teal                  * 0.15.2   2024-03-07 [1] RSPM
 teal.code             * 0.5.0    2024-01-11 [1] RSPM
 teal.data             * 0.6.0    2024-04-30 [1] RSPM
 teal.logger             0.3.0    2024-10-24 [1] RSPM
 teal.modules.clinical * 0.9.1    2024-04-27 [1] RSPM
 teal.reporter           0.3.1    2024-03-15 [1] RSPM
 teal.slice            * 0.5.1    2024-04-30 [1] RSPM
 teal.transform        * 0.5.0    2024-02-17 [1] RSPM
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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