TLG Catalog - Stable
  • Stable
    • Dev
  1. Tables
  2. Lab Results
  3. LBT08
  • Introduction

  • Tables
    • ADA
      • ADAT01
      • ADAT02
      • ADAT03
      • ADAT04A
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    • Adverse Events
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      • DISCLOSUREST01
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      • EUDRAT02
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      • PDT02
    • ECG
      • EGT01
      • EGT02
      • EGT03
      • EGT04
      • EGT05_QTCAT
    • Efficacy
      • AOVT01
      • AOVT02
      • AOVT03
      • CFBT01
      • CMHT01
      • COXT01
      • COXT02
      • DORT01
      • LGRT02
      • MMRMT01
      • ONCT05
      • RATET01
      • RBMIT01
      • RSPT01
      • TTET01
    • Exposure
      • EXT01
    • Lab Results
      • LBT01
      • LBT02
      • LBT03
      • LBT04
      • LBT05
      • LBT06
      • LBT07
      • LBT08
      • LBT09
      • LBT10
      • LBT10_BL
      • LBT11
      • LBT11_BL
      • LBT12
      • LBT12_BL
      • LBT13
      • LBT14
      • LBT15
    • Medical History
      • MHT01
    • Pharmacokinetic
      • PKCT01
      • PKPT02
      • PKPT03
      • PKPT04
      • PKPT05
      • PKPT06
      • PKPT07
      • PKPT08
      • PKPT11
    • Risk Management Plan
      • RMPT01
      • RMPT03
      • RMPT04
      • RMPT05
      • RMPT06
    • Safety
      • ENTXX
    • Vital Signs
      • VST01
      • VST02
  • Listings
    • ADA
      • ADAL02
    • Adverse Events
      • AEL01
      • AEL01_NOLLT
      • AEL02
      • AEL02_ED
      • AEL03
      • AEL04
    • Concomitant Medications
      • CML01
      • CML02A_GL
      • CML02B_GL
    • Development Safety Update Report
      • DSUR4
    • Disposition
      • DSL01
      • DSL02
    • ECG
      • EGL01
    • Efficacy
      • ONCL01
    • Exposure
      • EXL01
    • Lab Results
      • LBL01
      • LBL01_RLS
      • LBL02A
      • LBL02A_RLS
      • LBL02B
    • Medical History
      • MHL01
    • Pharmacokinetic
      • ADAL01
      • PKCL01
      • PKCL02
      • PKPL01
      • PKPL02
      • PKPL04
    • Vital Signs
      • VSL01
  • Graphs
    • Efficacy
      • FSTG01
      • FSTG02
      • KMG01
      • MMRMG01
      • MMRMG02
    • Other
      • BRG01
      • BWG01
      • CIG01
      • IPPG01
      • LTG01
      • MNG01
    • Pharmacokinetic
      • PKCG01
      • PKCG02
      • PKCG03
      • PKPG01
      • PKPG02
      • PKPG03
      • PKPG04
      • PKPG06

  • Appendix
    • Reproducibility

  • Index

On this page

  • Output
  • Reproducibility
    • Timestamp
    • Session Info
    • .lock file
  • Edit this page
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  1. Tables
  2. Lab Results
  3. LBT08

LBT08

Laboratory Test Results with Highest NCI CTCAE Grade at Any Time


Output

  • Standard Table
  • Data Setup

Note: The direction of the shift table for each lab test is based on metadata and NCI CTCAE specifications. In addition, the worst laboratory flags must be selected appropriately to match the direction of abnormality. For example, if any lab requires a shift table for both directions, then both worst_flag_low and worst_flag_high must be specified in h_adlb_worsen. If all labs requires a shift table for only one direction, then the matching worst lab flag variable must be selected in h_adlb_worsen.

  • Preview
  • Try this using WebR
Code
result <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARMCD") %>%
  split_rows_by("PARAMCD", label_pos = "topleft", split_label = obj_label(df$PARAMCD)) %>%
  split_rows_by("GRADDR", label_pos = "topleft", split_label = obj_label(df$GRADDR)) %>%
  count_abnormal_lab_worsen_by_baseline(
    var = "ATOXGR",
    variables = list(
      id = "USUBJID",
      baseline_var = "BTOXGR",
      direction_var = "GRADDR"
    )
  ) %>%
  append_topleft("    Highest NCI CTCAE Grade") %>%
  build_table(df = df, alt_counts_df = adsl)

result
Parameter Code                                                                
  Direction of Abnormality        ARM A            ARM B            ARM C     
    Highest NCI CTCAE Grade      (N=134)          (N=134)          (N=132)    
——————————————————————————————————————————————————————————————————————————————
ALT                                                                           
  Low                                                                         
    1                         11/113 (9.7%)     9/117 (7.7%)    15/123 (12.2%)
    2                         15/119 (12.6%)   23/123 (18.7%)   16/127 (12.6%)
    3                         15/127 (11.8%)   22/128 (17.2%)   14/128 (10.9%)
    4                         17/130 (13.1%)   10/131 (7.6%)    18/130 (13.8%)
    Any                       58/130 (44.6%)   64/131 (48.9%)   63/130 (48.5%)
CRP                                                                           
  High                                                                        
    1                         18/114 (15.8%)   18/112 (16.1%)   19/115 (16.5%)
    2                         20/122 (16.4%)   13/122 (10.7%)   14/122 (11.5%)
    3                         23/124 (18.5%)   14/128 (10.9%)   20/129 (15.5%)
    4                         12/131 (9.2%)    20/132 (15.2%)   11/132 (8.3%) 
    Any                       73/131 (55.7%)   65/132 (49.2%)   64/132 (48.5%)
  Low                                                                         
    1                         20/119 (16.8%)   18/113 (15.9%)   11/112 (9.8%) 
    2                         24/122 (19.7%)   21/118 (17.8%)    17/121 (14%) 
    3                         26/127 (20.5%)   20/127 (15.7%)   22/123 (17.9%)
    4                         10/131 (7.6%)    16/132 (12.1%)   21/130 (16.2%)
    Any                       80/131 (61.1%)   75/132 (56.8%)   71/130 (54.6%)
IGA                                                                           
  High                                                                        
    1                         18/119 (15.1%)   11/116 (9.5%)    15/113 (13.3%)
    2                         15/124 (12.1%)   23/120 (19.2%)   19/115 (16.5%)
    3                         21/128 (16.4%)   21/124 (16.9%)   20/120 (16.7%)
    4                         12/132 (9.1%)    12/129 (9.3%)     9/131 (6.9%) 
    Any                        66/132 (50%)    67/129 (51.9%)   63/131 (48.1%)
Experimental use!

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Code
library(dplyr)
library(tern)

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

adlb <- adlb %>%
  mutate(
    GRADDR = case_when(
      PARAMCD == "ALT" ~ "L",
      PARAMCD == "CRP" ~ "B",
      PARAMCD == "IGA" ~ "H"
    )
  ) %>%
  filter(SAFFL == "Y" & ONTRTFL == "Y" & GRADDR != "")

adsl <- df_explicit_na(adsl)
adlb <- df_explicit_na(adlb)

df <- h_adlb_worsen(
  adlb,
  worst_flag_low = c("WGRLOFL" = "Y"),
  worst_flag_high = c("WGRHIFL" = "Y"),
  direction_var = "GRADDR"
)

attributes(df$GRADDR) <- list("label" = "Direction of Abnormality")

Reproducibility

Timestamp

[1] "2025-07-05 17:45:59 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.5.0 (2025-04-11)
 os       Ubuntu 24.04.2 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2025-07-05
 pandoc   3.7.0.2 @ /usr/bin/ (via rmarkdown)
 quarto   1.7.32 @ /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                 3.6.5   2025-04-23 [1] RSPM
 codetools           0.2-20  2024-03-31 [2] CRAN (R 4.5.0)
 curl                6.4.0   2025-06-22 [1] RSPM
 dichromat           2.0-0.1 2022-05-02 [1] CRAN (R 4.5.0)
 digest              0.6.37  2024-08-19 [1] RSPM
 dplyr             * 1.1.4   2023-11-17 [1] RSPM
 evaluate            1.0.4   2025-06-18 [1] RSPM
 farver              2.1.2   2024-05-13 [1] RSPM
 fastmap             1.2.0   2024-05-15 [1] RSPM
 forcats             1.0.0   2023-01-29 [1] RSPM
 formatters        * 0.5.11  2025-04-09 [1] RSPM
 generics            0.1.4   2025-05-09 [1] RSPM
 ggplot2             3.5.2   2025-04-09 [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 [2] CRAN (R 4.5.0)
 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 4.5.0)
 nestcolor           0.1.3   2025-01-21 [1] RSPM
 pillar              1.11.0  2025-07-04 [1] RSPM
 pkgcache            2.2.4   2025-05-26 [1] RSPM
 pkgconfig           2.0.3   2019-09-22 [1] RSPM
 processx            3.8.6   2025-02-21 [1] RSPM
 ps                  1.9.1   2025-04-12 [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
 RColorBrewer        1.1-3   2022-04-03 [1] RSPM
 Rdpack              2.6.4   2025-04-09 [1] RSPM
 rlang               1.1.6   2025-04-11 [1] RSPM
 rmarkdown           2.29    2024-11-04 [1] RSPM
 rtables           * 0.6.13  2025-06-19 [1] RSPM
 scales              1.4.0   2025-04-24 [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 4.5.0)
 tern              * 0.9.9   2025-06-20 [1] RSPM
 testthat            3.2.3   2025-01-13 [1] RSPM
 tibble              3.3.0   2025-06-08 [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
 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/4.5
 * ── Packages attached to the search path.

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

.lock file

Download the .lock file and use renv::restore() on it to recreate environment used to generate this website.

Download

LBT07
LBT09
Source Code
---
title: LBT08
subtitle: Laboratory Test Results with Highest NCI CTCAE Grade at Any Time
---

------------------------------------------------------------------------

{{< include ../../_utils/envir_hook.qmd >}}

```{r setup, echo = FALSE, warning = FALSE, message = FALSE}
library(dplyr)
library(tern)

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

adlb <- adlb %>%
  mutate(
    GRADDR = case_when(
      PARAMCD == "ALT" ~ "L",
      PARAMCD == "CRP" ~ "B",
      PARAMCD == "IGA" ~ "H"
    )
  ) %>%
  filter(SAFFL == "Y" & ONTRTFL == "Y" & GRADDR != "")

adsl <- df_explicit_na(adsl)
adlb <- df_explicit_na(adlb)

df <- h_adlb_worsen(
  adlb,
  worst_flag_low = c("WGRLOFL" = "Y"),
  worst_flag_high = c("WGRHIFL" = "Y"),
  direction_var = "GRADDR"
)

attributes(df$GRADDR) <- list("label" = "Direction of Abnormality")
```

```{r include = FALSE}
webr_code_labels <- c("setup")
```

{{< include ../../_utils/webr_no_include.qmd >}}

## Output

:::: panel-tabset
## Standard Table

Note: The direction of the shift table for each lab test is based on metadata and NCI CTCAE specifications. In addition, the worst laboratory flags must be selected appropriately to match the direction of abnormality. For example, if any lab requires a shift table for both directions, then both `worst_flag_low` and `worst_flag_high` must be specified in `h_adlb_worsen`. If all labs requires a shift table for only one direction, then the matching worst lab flag variable must be selected in `h_adlb_worsen`.

::: {.panel-tabset .nav-justified group="webr"}
## {{< fa regular file-lines sm fw >}} Preview

```{r variant1, test = list(result_v1 = "result")}
result <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by("ARMCD") %>%
  split_rows_by("PARAMCD", label_pos = "topleft", split_label = obj_label(df$PARAMCD)) %>%
  split_rows_by("GRADDR", label_pos = "topleft", split_label = obj_label(df$GRADDR)) %>%
  count_abnormal_lab_worsen_by_baseline(
    var = "ATOXGR",
    variables = list(
      id = "USUBJID",
      baseline_var = "BTOXGR",
      direction_var = "GRADDR"
    )
  ) %>%
  append_topleft("    Highest NCI CTCAE Grade") %>%
  build_table(df = df, alt_counts_df = adsl)

result
```

```{r include = FALSE}
webr_code_labels <- c("variant1")
```

{{< include ../../_utils/webr.qmd >}}
:::

## Data Setup

```{r setup}
#| code-fold: show
```
::::

{{< include ../../_utils/save_results.qmd >}}

{{< include ../../repro.qmd >}}

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