TLG Catalog - Stable
  • Stable
    • Dev
  1. Tables
  2. Pharmacokinetic
  3. PKPT03
  • Introduction

  • Tables
    • ADA
      • ADAT01
      • ADAT02
      • ADAT03
      • ADAT04A
      • ADAT04B
    • Adverse Events
      • AET01
      • AET01_AESI
      • AET02
      • AET02_SMQ
      • AET03
      • AET04
      • AET04_PI
      • AET05
      • AET05_ALL
      • AET06
      • AET06_SMQ
      • AET07
      • AET09
      • AET09_SMQ
      • AET10
    • Concomitant Medications
      • CMT01
      • CMT01A
      • CMT01B
      • CMT02_PT
    • Deaths
      • DTHT01
    • Demography
      • DMT01
    • Disclosures
      • DISCLOSUREST01
      • EUDRAT01
      • EUDRAT02
    • Disposition
      • DST01
      • PDT01
      • 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
  • Report an issue
  1. Tables
  2. Pharmacokinetic
  3. PKPT03

PKPT03

Pharmacokinetic Parameter Summary of Plasma by Treatment (Stats in Columns)


Output

  • Standard Table
  • Data Setup
Code
# lyt creation
lyt <- basic_table() %>%
  split_rows_by(
    var = "AVISIT",
    split_fun = drop_split_levels,
    split_label = "Visit",
    page_by = TRUE
  ) %>%
  split_rows_by(
    var = "ARMCD",
    split_fun = trim_levels_in_group("PARAM"),
    label_pos = "topleft",
    split_label = "Treatment Arm"
  ) %>%
  add_rowcounts(alt_counts = TRUE) %>%
  split_rows_by(
    var = "PARAM",
    label_pos = "topleft",
    split_label = "PK Parameter",
    child_labels = "hidden"
  ) %>%
  analyze_vars_in_cols(
    vars = "AVAL",
    .stats = c(
      "n", "mean", "sd", "cv",
      "geom_mean", "geom_cv", "median",
      "min", "max"
    ),
    .labels = c(
      n = "n",
      mean = "Mean",
      sd = "SD",
      cv = "CV (%)",
      geom_mean = "Geometric Mean",
      geom_cv = "CV % Geometric Mean",
      median = "Median",
      min = "Minimum",
      max = "Maximum"
    ),
    .formats = c(
      n = "xx.",
      mean = format_sigfig(3),
      sd = format_sigfig(3),
      cv = "xx.x",
      median = format_sigfig(3),
      geom_mean = format_sigfig(3),
      geom_cv = "xx.x",
      min = format_sigfig(3),
      max = format_sigfig(3)
    ),
    na_str = "NE"
  )

Plasma Drug X

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  • Try this using WebR
Code
result <- build_table(lyt, df = adpp_x, alt_counts_df = adsl_x_splitvars)
main_title(result) <- paste("Summary of", unique(adpp_x$PPSPEC), "PK Parameter by Treatment Arm, PK Population")
subtitles(result) <- paste("Analyte:", unique(adpp_x$PPCAT))
result <- paginate_table(result, landscape = TRUE)
result
$`CYCLE 1 DAY 1`
Summary of Plasma PK Parameter by Treatment Arm, PK Population
Analyte: Plasma Drug X
Visit: CYCLE 1 DAY 1

——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Treatment Arm                                                                                                             
  PK Parameter              n    Mean    SD     CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
ARM A (N=134)                                                                                                             
  Cmax (ug/mL)              0     NE     NE       NE           NE                 NE              NE       NE        NE   
  AUCinf obs (day*ug/mL)   134   203    37.7     18.6         199                18.7            197       125       311  
  CL obs (ml/day/kg)       134   5.04   1.04     20.6         4.93               22.4            5.08     2.25      7.39  
ARM C (N=132)                                                                                                             
  Cmax (ug/mL)             132   30.0   5.46     18.2         29.5               18.9            29.8     15.9      47.6  
  AUCinf obs (day*ug/mL)   132   195    37.8     19.4         192                20.1            196       103       315  
  CL obs (ml/day/kg)       132   5.01   0.985    19.7         4.91               21.1            4.97     2.10      7.49  

$`CYCLE 1 DAY 2`
Summary of Plasma PK Parameter by Treatment Arm, PK Population
Analyte: Plasma Drug X
Visit: CYCLE 1 DAY 2

—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Treatment Arm                                                                                                            
  PK Parameter              n    Mean    SD    CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
ARM A (N=134)                                                                                                            
  Cmax (ug/mL)             134   29.4   6.22    21.2         28.6               24.4            29.8     10.1      43.5  
  AUCinf obs (day*ug/mL)   134   202    41.2    20.4         197                21.4            200       105       294  
  CL obs (ml/day/kg)       134   5.04   1.04    20.7         4.92               22.6            5.01     2.39      7.18  
ARM C (N=132)                                                                                                            
  Cmax (ug/mL)             132   30.4   6.03    19.9         29.7               21.7            30.5     12.4      45.5  
  AUCinf obs (day*ug/mL)   132   191    43.0    22.5         186                24.7            189      74.8       296  
  CL obs (ml/day/kg)       132   5.07   1.07    21.1         4.96               22.4            5.01     2.48      7.50  
Experimental use!

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Plasma Drug X: Remove Rows with 0s

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Code
result <- build_table(lyt, df = adpp_x, alt_counts_df = adsl_x_splitvars) %>%
  prune_table()

main_title(result) <- paste("Summary of", unique(adpp_x$PPSPEC), "PK Parameter by Treatment Arm, PK Population")
subtitles(result) <- paste("Analyte:", unique(adpp_x$PPCAT))
result <- paginate_table(result, landscape = TRUE)
result
$`CYCLE 1 DAY 1`
Summary of Plasma PK Parameter by Treatment Arm, PK Population
Analyte: Plasma Drug X
Visit: CYCLE 1 DAY 1

——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Treatment Arm                                                                                                             
  PK Parameter              n    Mean    SD     CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
ARM A (N=134)                                                                                                             
  AUCinf obs (day*ug/mL)   134   203    37.7     18.6         199                18.7            197       125       311  
  CL obs (ml/day/kg)       134   5.04   1.04     20.6         4.93               22.4            5.08     2.25      7.39  
ARM C (N=132)                                                                                                             
  Cmax (ug/mL)             132   30.0   5.46     18.2         29.5               18.9            29.8     15.9      47.6  
  AUCinf obs (day*ug/mL)   132   195    37.8     19.4         192                20.1            196       103       315  
  CL obs (ml/day/kg)       132   5.01   0.985    19.7         4.91               21.1            4.97     2.10      7.49  

$`CYCLE 1 DAY 2`
Summary of Plasma PK Parameter by Treatment Arm, PK Population
Analyte: Plasma Drug X
Visit: CYCLE 1 DAY 2

—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Treatment Arm                                                                                                            
  PK Parameter              n    Mean    SD    CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
ARM A (N=134)                                                                                                            
  Cmax (ug/mL)             134   29.4   6.22    21.2         28.6               24.4            29.8     10.1      43.5  
  AUCinf obs (day*ug/mL)   134   202    41.2    20.4         197                21.4            200       105       294  
  CL obs (ml/day/kg)       134   5.04   1.04    20.7         4.92               22.6            5.01     2.39      7.18  
ARM C (N=132)                                                                                                            
  Cmax (ug/mL)             132   30.4   6.03    19.9         29.7               21.7            30.5     12.4      45.5  
  AUCinf obs (day*ug/mL)   132   191    43.0    22.5         186                24.7            189      74.8       296  
  CL obs (ml/day/kg)       132   5.07   1.07    21.1         4.96               22.4            5.01     2.48      7.50  
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.

Plasma Drug Y

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  • Try this using WebR
Code
result <- build_table(lyt, df = adpp_y, alt_counts_df = adsl_y_splitvars)
main_title(result) <- paste("Summary of", unique(adpp_y$PPSPEC), "PK Parameter by Treatment Arm, PK Population")
subtitles(result) <- paste("Analyte:", unique(adpp_y$PPCAT))
result <- paginate_table(result, landscape = TRUE)
result
$`CYCLE 1 DAY 1`
Summary of Plasma PK Parameter by Treatment Arm, PK Population
Analyte: Plasma Drug Y
Visit: CYCLE 1 DAY 1

——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Treatment Arm                                                                                                             
  PK Parameter              n    Mean    SD     CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
ARM C (N=132)                                                                                                             
  Cmax (ug/mL)             132   29.9   5.55     18.6         29.4               20.1            29.7     14.1      43.4  
  AUCinf obs (day*ug/mL)   132   199    37.9     19.1         195                18.9            195       126       318  
  CL obs (ml/day/kg)       132   4.96   0.895    18.1         4.87               18.7            4.94     2.99      7.21  

$`CYCLE 1 DAY 2`
Summary of Plasma PK Parameter by Treatment Arm, PK Population
Analyte: Plasma Drug Y
Visit: CYCLE 1 DAY 2

——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Treatment Arm                                                                                                             
  PK Parameter              n    Mean    SD     CV (%)   Geometric Mean   CV % Geometric Mean   Median   Minimum   Maximum
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
ARM C (N=132)                                                                                                             
  Cmax (ug/mL)             132   30.7   6.12     19.9         30.1               21.9            31.4     12.9      48.3  
  AUCinf obs (day*ug/mL)   132   199    40.0     20.1         194                22.2            197      79.2       295  
  CL obs (ml/day/kg)       132   4.99   0.984    19.7         4.89               20.9            4.96     2.58      8.39  
Experimental use!

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Code
library(dplyr)
library(tern)

# Preprocess analysis data ----
adpp <- random.cdisc.data::cadpp
adpp <- adpp %>%
  filter(PPSPEC == "Plasma") %>%
  filter(AVISIT %in% c("CYCLE 1 DAY 1", "CYCLE 1 DAY 2")) %>%
  h_pkparam_sort() %>%
  mutate(PARAM = factor(paste0(TLG_DISPLAY, " (", AVALU, ")"))) %>%
  mutate(PARAM = reorder(PARAM, TLG_ORDER))

# Preprocess subject-level data ----
adsl <- random.cdisc.data::cadsl

# Workaround needed to include (N=xx) population counts
# Repeat ADSL by the number of levels in AVISIT
# Include AVISIT and dummy PARAM as it's needed for trim_levels_in_group
adsl_tmp <- adsl %>%
  select(STUDYID, USUBJID, ARMCD) %>%
  unique() %>%
  mutate(PARAM = factor(NA_character_, levels = levels(adpp$PARAM)))

# Data for Plasma Drug X example ----
adpp_x <- adpp %>%
  filter(PPCAT == "Plasma Drug X") %>%
  # Please do not replicate mutate statement below!
  # It is used to make the random data in this example more realistic
  # as not all parameters are always available across all visits.
  mutate(
    AVAL = if_else(
      ARMCD == "ARM A" & AVISIT == "CYCLE 1 DAY 1" & PARAM == "Cmax (ug/mL)",
      NA_real_, AVAL
    )
  )

adpp_x_tmp <- adpp_x %>%
  select(STUDYID, USUBJID, ARMCD, AVISIT) %>%
  unique()

adsl_x_splitvars <- adsl_tmp %>%
  left_join(adpp_x_tmp, by = c("STUDYID", "USUBJID", "ARMCD")) %>%
  filter(!is.na(AVISIT))

# Data for Plasma Drug Y example ----
adpp_y <- adpp %>%
  filter(PPCAT == "Plasma Drug Y")

adpp_y_tmp <- adpp_y %>%
  select(STUDYID, USUBJID, ARMCD, AVISIT) %>%
  unique()

adsl_y_splitvars <- adsl_tmp %>%
  left_join(adpp_y_tmp, by = c("STUDYID", "USUBJID", "ARMCD")) %>%
  filter(!is.na(AVISIT))

Reproducibility

Timestamp

[1] "2025-07-05 17:49:51 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
 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
 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/4.5
 * ── Packages attached to the search path.

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

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

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PKPT02
PKPT04
Source Code
---
title: PKPT03
subtitle: Pharmacokinetic Parameter Summary of Plasma by Treatment (Stats in Columns)
---

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

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

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

# Preprocess analysis data ----
adpp <- random.cdisc.data::cadpp
adpp <- adpp %>%
  filter(PPSPEC == "Plasma") %>%
  filter(AVISIT %in% c("CYCLE 1 DAY 1", "CYCLE 1 DAY 2")) %>%
  h_pkparam_sort() %>%
  mutate(PARAM = factor(paste0(TLG_DISPLAY, " (", AVALU, ")"))) %>%
  mutate(PARAM = reorder(PARAM, TLG_ORDER))

# Preprocess subject-level data ----
adsl <- random.cdisc.data::cadsl

# Workaround needed to include (N=xx) population counts
# Repeat ADSL by the number of levels in AVISIT
# Include AVISIT and dummy PARAM as it's needed for trim_levels_in_group
adsl_tmp <- adsl %>%
  select(STUDYID, USUBJID, ARMCD) %>%
  unique() %>%
  mutate(PARAM = factor(NA_character_, levels = levels(adpp$PARAM)))

# Data for Plasma Drug X example ----
adpp_x <- adpp %>%
  filter(PPCAT == "Plasma Drug X") %>%
  # Please do not replicate mutate statement below!
  # It is used to make the random data in this example more realistic
  # as not all parameters are always available across all visits.
  mutate(
    AVAL = if_else(
      ARMCD == "ARM A" & AVISIT == "CYCLE 1 DAY 1" & PARAM == "Cmax (ug/mL)",
      NA_real_, AVAL
    )
  )

adpp_x_tmp <- adpp_x %>%
  select(STUDYID, USUBJID, ARMCD, AVISIT) %>%
  unique()

adsl_x_splitvars <- adsl_tmp %>%
  left_join(adpp_x_tmp, by = c("STUDYID", "USUBJID", "ARMCD")) %>%
  filter(!is.na(AVISIT))

# Data for Plasma Drug Y example ----
adpp_y <- adpp %>%
  filter(PPCAT == "Plasma Drug Y")

adpp_y_tmp <- adpp_y %>%
  select(STUDYID, USUBJID, ARMCD, AVISIT) %>%
  unique()

adsl_y_splitvars <- adsl_tmp %>%
  left_join(adpp_y_tmp, by = c("STUDYID", "USUBJID", "ARMCD")) %>%
  filter(!is.na(AVISIT))
```

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

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

## Output

:::::: panel-tabset
## Standard Table

```{r lyt}
# lyt creation
lyt <- basic_table() %>%
  split_rows_by(
    var = "AVISIT",
    split_fun = drop_split_levels,
    split_label = "Visit",
    page_by = TRUE
  ) %>%
  split_rows_by(
    var = "ARMCD",
    split_fun = trim_levels_in_group("PARAM"),
    label_pos = "topleft",
    split_label = "Treatment Arm"
  ) %>%
  add_rowcounts(alt_counts = TRUE) %>%
  split_rows_by(
    var = "PARAM",
    label_pos = "topleft",
    split_label = "PK Parameter",
    child_labels = "hidden"
  ) %>%
  analyze_vars_in_cols(
    vars = "AVAL",
    .stats = c(
      "n", "mean", "sd", "cv",
      "geom_mean", "geom_cv", "median",
      "min", "max"
    ),
    .labels = c(
      n = "n",
      mean = "Mean",
      sd = "SD",
      cv = "CV (%)",
      geom_mean = "Geometric Mean",
      geom_cv = "CV % Geometric Mean",
      median = "Median",
      min = "Minimum",
      max = "Maximum"
    ),
    .formats = c(
      n = "xx.",
      mean = format_sigfig(3),
      sd = format_sigfig(3),
      cv = "xx.x",
      median = format_sigfig(3),
      geom_mean = format_sigfig(3),
      geom_cv = "xx.x",
      min = format_sigfig(3),
      max = format_sigfig(3)
    ),
    na_str = "NE"
  )
```

#### Plasma Drug X

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

```{r variant1, test = list(result_v1 = "result")}
result <- build_table(lyt, df = adpp_x, alt_counts_df = adsl_x_splitvars)
main_title(result) <- paste("Summary of", unique(adpp_x$PPSPEC), "PK Parameter by Treatment Arm, PK Population")
subtitles(result) <- paste("Analyte:", unique(adpp_x$PPCAT))
result <- paginate_table(result, landscape = TRUE)
result
```

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

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

#### Plasma Drug X: Remove Rows with 0s

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

```{r variant2, test = list(result_v2 = "result")}
result <- build_table(lyt, df = adpp_x, alt_counts_df = adsl_x_splitvars) %>%
  prune_table()

main_title(result) <- paste("Summary of", unique(adpp_x$PPSPEC), "PK Parameter by Treatment Arm, PK Population")
subtitles(result) <- paste("Analyte:", unique(adpp_x$PPCAT))
result <- paginate_table(result, landscape = TRUE)
result
```

```{r include = FALSE}
webr_code_labels <- c("lyt", "variant2")
```

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

#### Plasma Drug Y

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

```{r variant3, test = list(result_v3 = "result")}
result <- build_table(lyt, df = adpp_y, alt_counts_df = adsl_y_splitvars)
main_title(result) <- paste("Summary of", unique(adpp_y$PPSPEC), "PK Parameter by Treatment Arm, PK Population")
subtitles(result) <- paste("Analyte:", unique(adpp_y$PPCAT))
result <- paginate_table(result, landscape = TRUE)
result
```

```{r include = FALSE}
webr_code_labels <- c("lyt", "variant3")
```

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

## Data Setup

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

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

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

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