LBT03

Laboratory Test Results Change from Baseline by Visit

Output

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.

Code
# 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)    
β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”
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

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.

Code
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)    
β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”β€”
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

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.

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

Code
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

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

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

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://pharmaverse.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] "2024-11-20 17:56:35 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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