LBT05

Laboratory Abnormalities with Single and Replicated Marked

Output

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Code

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by(
    "PARAM",
    label_pos = "topleft",
    split_label = "Laboratory Test"
  ) %>%
  summarize_num_patients(var = "USUBJID", .stats = "unique_count") %>%
  split_rows_by("abn_dir", split_fun = trim_levels_to_map(map)) %>%
  count_abnormal_by_marked(
    var = "AVALCAT1",
    variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir")
  ) %>%
  append_topleft("  Direction of Abnormality")

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

has_lbl <- function(lbl) CombinationFunction(function(tr) obj_label(tr) == lbl || sum(unlist(row_values(tr))) != 0)
result <- prune_table(result, keep_rows(has_lbl("Any Abnormality")))

result

Laboratory Test                            A: Drug X    B: Placebo   C: Combination
  Direction of Abnormality                  (N=134)      (N=134)        (N=132)    
———————————————————————————————————————————————————————————————————————————————————
Alanine Aminotransferase Measurement (n)      125          120            125      
  Low                                                                              
    Single, not last                        3 (2.4%)     5 (4.2%)        5 (4%)    
    Last or replicated                     52 (41.6%)   59 (49.2%)     44 (35.2%)  
    Any Abnormality                         55 (44%)    64 (53.3%)     49 (39.2%)  
  High                                                                             
    Any Abnormality                            0            0              0       
C-Reactive Protein Measurement (n)            129          130            121      
  Low                                                                              
    Single, not last                        3 (2.3%)     7 (5.4%)        6 (5%)    
    Last or replicated                     59 (45.7%)   50 (38.5%)     49 (40.5%)  
    Any Abnormality                        62 (48.1%)   57 (43.8%)     55 (45.5%)  
  High                                                                             
    Single, not last                        5 (3.9%)     4 (3.1%)       2 (1.7%)   
    Last or replicated                      49 (38%)    54 (41.5%)     45 (37.2%)  
    Any Abnormality                        54 (41.9%)   58 (44.6%)     47 (38.8%)  
Immunoglobulin A Measurement (n)              129          122            121      
  Low                                                                              
    Any Abnormality                            0            0              0       
  High                                                                             
    Single, not last                        4 (3.1%)     6 (4.9%)       3 (2.5%)   
    Last or replicated                     48 (37.2%)   47 (38.5%)     55 (45.5%)  
    Any Abnormality                        52 (40.3%)   53 (43.4%)     58 (47.9%)

Experimental use!

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R Code

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lyt <- basic_table(show_colcounts = TRUE) %>%

split_cols_by("ACTARM") %>%

split_rows_by(

"PARAM",

label_pos = "topleft",

split_label = "Laboratory Test"

) %>%

summarize_num_patients(var = "USUBJID", .stats = "unique_count") %>%

split_rows_by("abn_dir", split_fun = trim_levels_to_map(map)) %>%

count_abnormal_by_marked(

var = "AVALCAT1",

variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir")

) %>%

append_topleft(" Direction of Abnormality")

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

has_lbl <- function(lbl) CombinationFunction(function(tr) obj_label(tr) == lbl || sum(unlist(row_values(tr))) != 0)

result <- prune_table(result, keep_rows(has_lbl("Any Abnormality")))

result

_webr_editor_2 = Object {code: "lyt <- basic_table(show_colcounts = TRUE) %>%\n sp…t, keep_rows(has_lbl(\"Any Abnormality\")))\n\nresult", options: Object, indicator: Ke}

Preview
Try this using WebR

Code

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by(
    "PARAM",
    label_pos = "topleft",
    split_label = "Laboratory Test"
  ) %>%
  summarize_num_patients(var = "USUBJID", .stats = "unique_count") %>%
  split_rows_by("abn_dir", split_fun = trim_levels_to_map(map)) %>%
  count_abnormal_by_marked(
    var = "AVALCAT1",
    variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir")
  ) %>%
  append_topleft("  Direction of Abnormality")

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

result

Laboratory Test                            A: Drug X    B: Placebo   C: Combination
  Direction of Abnormality                  (N=134)      (N=134)        (N=132)    
———————————————————————————————————————————————————————————————————————————————————
Alanine Aminotransferase Measurement (n)      125          120            125      
  Low                                                                              
    Single, not last                        3 (2.4%)     5 (4.2%)        5 (4%)    
    Last or replicated                     52 (41.6%)   59 (49.2%)     44 (35.2%)  
    Any Abnormality                         55 (44%)    64 (53.3%)     49 (39.2%)  
  High                                                                             
    Single, not last                           0            0              0       
    Last or replicated                         0            0              0       
    Any Abnormality                            0            0              0       
C-Reactive Protein Measurement (n)            129          130            121      
  Low                                                                              
    Single, not last                        3 (2.3%)     7 (5.4%)        6 (5%)    
    Last or replicated                     59 (45.7%)   50 (38.5%)     49 (40.5%)  
    Any Abnormality                        62 (48.1%)   57 (43.8%)     55 (45.5%)  
  High                                                                             
    Single, not last                        5 (3.9%)     4 (3.1%)       2 (1.7%)   
    Last or replicated                      49 (38%)    54 (41.5%)     45 (37.2%)  
    Any Abnormality                        54 (41.9%)   58 (44.6%)     47 (38.8%)  
Immunoglobulin A Measurement (n)              129          122            121      
  Low                                                                              
    Single, not last                           0            0              0       
    Last or replicated                         0            0              0       
    Any Abnormality                            0            0              0       
  High                                                                             
    Single, not last                        4 (3.1%)     6 (4.9%)       3 (2.5%)   
    Last or replicated                     48 (37.2%)   47 (38.5%)     55 (45.5%)  
    Any Abnormality                        52 (40.3%)   53 (43.4%)     58 (47.9%)

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.

R Code

Start Over

Run Code

lyt <- basic_table(show_colcounts = TRUE) %>%

split_cols_by("ACTARM") %>%

split_rows_by(

"PARAM",

label_pos = "topleft",

split_label = "Laboratory Test"

) %>%

summarize_num_patients(var = "USUBJID", .stats = "unique_count") %>%

split_rows_by("abn_dir", split_fun = trim_levels_to_map(map)) %>%

count_abnormal_by_marked(

var = "AVALCAT1",

variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir")

) %>%

append_topleft(" Direction of Abnormality")

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

result

_webr_editor_3 = Object {code: "lyt <- basic_table(show_colcounts = TRUE) %>%\n sp…e(lyt, df = adlb_f, alt_counts_df = adsl)\n\nresult", options: Object, indicator: Ke}

Code

# This variant reflects user modifications made to the laboratory analysis data set related to
# Safety Lab Standardization metadata.
# There is no unique layout level variation.

Preview
Try this using WebR

Code

lyt <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ACTARM") %>%
  split_rows_by(
    "PARAM",
    label_pos = "topleft",
    split_label = "Laboratory Test",
    split_fun = trim_levels_in_group("abn_dir", drop_outlevs = TRUE)
  ) %>%
  summarize_num_patients(var = "USUBJID", .stats = "unique_count") %>%
  split_rows_by("abn_dir") %>%
  count_abnormal_by_marked(
    var = "AVALCAT1",
    variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir")
  ) %>%
  append_topleft("  Direction of Abnormality")

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

result <- result %>% prune_table()
result

Laboratory Test                            A: Drug X    B: Placebo   C: Combination
  Direction of Abnormality                  (N=134)      (N=134)        (N=132)    
———————————————————————————————————————————————————————————————————————————————————
Alanine Aminotransferase Measurement (n)      125          120            125      
  Low                                                                              
    Single, not last                        3 (2.4%)     5 (4.2%)        5 (4%)    
    Last or replicated                     52 (41.6%)   59 (49.2%)     44 (35.2%)  
    Any Abnormality                         55 (44%)    64 (53.3%)     49 (39.2%)  
C-Reactive Protein Measurement (n)            129          130            121      
  Low                                                                              
    Single, not last                        3 (2.3%)     7 (5.4%)        6 (5%)    
    Last or replicated                     59 (45.7%)   50 (38.5%)     49 (40.5%)  
    Any Abnormality                        62 (48.1%)   57 (43.8%)     55 (45.5%)  
  High                                                                             
    Single, not last                        5 (3.9%)     4 (3.1%)       2 (1.7%)   
    Last or replicated                      49 (38%)    54 (41.5%)     45 (37.2%)  
    Any Abnormality                        54 (41.9%)   58 (44.6%)     47 (38.8%)  
Immunoglobulin A Measurement (n)              129          122            121      
  High                                                                             
    Single, not last                        4 (3.1%)     6 (4.9%)       3 (2.5%)   
    Last or replicated                     48 (37.2%)   47 (38.5%)     55 (45.5%)  
    Any Abnormality                        52 (40.3%)   53 (43.4%)     58 (47.9%)

Code

# Another approach would be to create an empirical map by removing the ALT records
# and use it in `trim_levels_to_map`.
# this is an a posteriori approach, though.

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.

R Code

Start Over

Run Code

lyt <- basic_table(show_colcounts = TRUE) %>%

split_cols_by("ACTARM") %>%

split_rows_by(

"PARAM",

label_pos = "topleft",

split_label = "Laboratory Test",

split_fun = trim_levels_in_group("abn_dir", drop_outlevs = TRUE)

) %>%

summarize_num_patients(var = "USUBJID", .stats = "unique_count") %>%

split_rows_by("abn_dir") %>%

count_abnormal_by_marked(

var = "AVALCAT1",

variables = list(id = "USUBJID", param = "PARAM", direction = "abn_dir")

) %>%

append_topleft(" Direction of Abnormality")

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

result <- result %>% prune_table()

result

# Another approach would be to create an empirical map by removing the ALT records

# and use it in `trim_levels_to_map`.

# this is an a posteriori approach, though.

_webr_editor_4 = Object {code: "lyt <- basic_table(show_colcounts = TRUE) %>%\n sp…map`.\n# this is an a posteriori approach, though.", options: Object, indicator: Ke}

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)

qntls <- adlb %>%
  group_by(PARAMCD) %>%
  summarise(as_tibble(t(quantile(AVAL, probs = c(0.1, 0.9)))), .groups = "drop_last") %>%
  rename(q1 = 2, q2 = 3)

adlb <- adlb %>%
  left_join(qntls, by = "PARAMCD")

set.seed(1)

# Modify ANRIND and create AVALCAT1/PARCAT2
# PARCAT2 is just used for filtering, but in order to be the
# filtering as realistic as possible, will create the variable.
adlb <- adlb %>%
  mutate(
    ANRIND = factor(
      case_when(
        ANRIND == "LOW" & AVAL <= q1 ~ "LOW LOW",
        ANRIND == "HIGH" & AVAL >= q2 ~ "HIGH HIGH",
        TRUE ~ as.character(ANRIND)
      ),
      levels = c("", "HIGH", "HIGH HIGH", "LOW", "LOW LOW", "NORMAL")
    ),
    AVALCAT1 = factor(
      case_when(
        ANRIND %in% c("HIGH HIGH", "LOW LOW") ~
          sample(x = c("LAST", "REPLICATED", "SINGLE"), size = n(), replace = TRUE, prob = c(0.3, 0.6, 0.1)),
        TRUE ~ ""
      ),
      levels = c("", c("LAST", "REPLICATED", "SINGLE"))
    ),
    PARCAT2 = factor(ifelse(ANRIND %in% c("HIGH HIGH", "LOW LOW"), "LS",
      sample(c("SI", "CV", "LS"), size = n(), replace = TRUE)
    ))
  ) %>%
  select(-q1, -q2)

# Pre-processing steps
adlb_f <- adlb %>%
  filter(ONTRTFL == "Y" & PARCAT2 == "LS" & SAFFL == "Y" & !is.na(AVAL)) %>%
  mutate(abn_dir = factor(case_when(
    ANRIND == "LOW LOW" ~ "Low",
    ANRIND == "HIGH HIGH" ~ "High",
    TRUE ~ ""
  ), levels = c("Low", "High", ""))) %>%
  df_explicit_na()

# Construct analysis map
map <- expand.grid(
  PARAM = levels(adlb$PARAM),
  abn_dir = c("Low", "High"),
  stringsAsFactors = FALSE
) %>%
  arrange(PARAM, desc(abn_dir))

Reproducibility

Timestamp

[1] "2025-04-05 17:39:11 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.2 (2024-10-31)
 os       Ubuntu 24.04.1 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2025-04-05
 pandoc   3.6.3 @ /usr/bin/ (via rmarkdown)
 quarto   1.6.42 @ /usr/local/bin/quarto

─ Packages ───────────────────────────────────────────────────────────────────
 package           * version date (UTC) lib source
 backports           1.5.0   2024-05-23 [1] RSPM
 brio                1.1.5   2024-04-24 [1] RSPM
 broom               1.0.8   2025-03-28 [1] RSPM
 checkmate           2.3.2   2024-07-29 [1] RSPM
 cli                 3.6.4   2025-02-13 [1] RSPM
 codetools           0.2-20  2024-03-31 [2] CRAN (R 4.4.2)
 colorspace          2.1-1   2024-07-26 [1] RSPM
 curl                6.2.2   2025-03-24 [1] RSPM
 digest              0.6.37  2024-08-19 [1] RSPM
 dplyr             * 1.1.4   2023-11-17 [1] RSPM
 evaluate            1.0.3   2025-01-10 [1] RSPM
 fastmap             1.2.0   2024-05-15 [1] RSPM
 forcats             1.0.0   2023-01-29 [1] RSPM
 formatters        * 0.5.10  2025-01-09 [1] RSPM
 generics            0.1.3   2022-07-05 [1] RSPM
 ggplot2             3.5.1   2024-04-23 [1] RSPM
 glue                1.8.0   2024-09-30 [1] RSPM
 gtable              0.3.6   2024-10-25 [1] RSPM
 htmltools           0.5.8.1 2024-04-04 [1] RSPM
 htmlwidgets         1.6.4   2023-12-06 [1] RSPM
 jsonlite            2.0.0   2025-03-27 [1] RSPM
 knitr               1.50    2025-03-16 [1] RSPM
 lattice             0.22-7  2025-04-02 [1] RSPM
 lifecycle           1.0.4   2023-11-07 [1] RSPM
 magrittr          * 2.0.3   2022-03-30 [1] RSPM
 Matrix              1.7-3   2025-03-11 [1] CRAN (R 4.4.2)
 munsell             0.5.1   2024-04-01 [1] RSPM
 nestcolor           0.1.3   2025-01-21 [1] RSPM
 pillar              1.10.2  2025-04-05 [1] CRAN (R 4.4.2)
 pkgcache            2.2.3   2024-09-12 [1] RSPM
 pkgconfig           2.0.3   2019-09-22 [1] RSPM
 processx            3.8.6   2025-02-21 [1] RSPM
 ps                  1.9.0   2025-02-18 [1] RSPM
 purrr               1.0.4   2025-02-05 [1] RSPM
 R6                  2.6.1   2025-02-15 [1] RSPM
 random.cdisc.data   0.3.16  2024-10-10 [1] RSPM
 rbibutils           2.3     2024-10-04 [1] RSPM
 Rdpack              2.6.3   2025-03-16 [1] RSPM
 rlang               1.1.5   2025-01-17 [1] RSPM
 rmarkdown           2.29    2024-11-04 [1] RSPM
 rtables           * 0.6.11  2025-01-10 [1] RSPM
 scales              1.3.0   2023-11-28 [1] RSPM
 sessioninfo         1.2.3   2025-02-05 [1] any (@1.2.3)
 stringi             1.8.7   2025-03-27 [1] RSPM
 stringr             1.5.1   2023-11-14 [1] RSPM
 survival            3.8-3   2024-12-17 [2] CRAN (R 4.4.2)
 tern              * 0.9.7   2025-01-17 [1] RSPM
 testthat            3.2.3   2025-01-13 [1] RSPM
 tibble              3.2.1   2023-03-20 [1] RSPM
 tidyr               1.3.1   2024-01-24 [1] RSPM
 tidyselect          1.2.1   2024-03-11 [1] RSPM
 vctrs               0.6.5   2023-12-01 [1] RSPM
 withr               3.0.2   2024-10-28 [1] RSPM
 xfun                0.52    2025-04-02 [1] RSPM
 yaml                2.3.10  2024-07-26 [1] RSPM

 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library
 [3] /github/home/R/x86_64-pc-linux-gnu-library/4.4
 * ── Packages attached to the search path.

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

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Reproducibility

Timestamp

Session Info

.lock file

`.lock` file