PKCT01

Summary Concentration Table

---

Output

Standard Table (Stats in Columns)

Preview

lyt <- lyt_rows %>%
  analyze_vars_in_cols(
    vars = c("AVAL", "AVALCAT1", rep("AVAL", 8)),
    .stats = c("n", "n_blq", "mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max"),
    .formats = c(
      n = "xx.", n_blq = "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)
    ),
    .labels = c(
      n = "n", n_blq = "Number\nof\nLTRs/BLQs", mean = "Mean", sd = "SD", cv = "CV (%) Mean",
      geom_mean = "Geometric Mean", geom_cv = "CV % Geometric Mean", median = "Median", min = "Minimum", max = "Maximum"
    ),
    na_str = "NE",
    .aligns = "decimal"
  )

result <- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %>% prune_table()

# Decorating
main_title(result) <- "Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable"
subtitles(result) <- c(
  "Protocol: xxxxx",
  paste("Analyte: ", unique(adpc_1$PARAM)),
  paste("Treatment:", unique(adpc_1$ACTARM))
)
main_footer(result) <- "NE: Not Estimable"

result

Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable
Protocol: xxxxx
Analyte:  Plasma Drug X
Treatment: A: Drug X

——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Treatment Group                            Number                                                                                                         
  Visit                                      of                                                                                                           
    Nominal Time (hr) / Timepoint    n    LTRs/BLQs    Mean       SD     CV (%) Mean   Geometric Mean   CV % Geometric Mean   Median    Minimum    Maximum
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
A: Drug X (N=134)                                                                                                                                         
  Day 1                                                                                                                                                   
    0 / Predose                     134      134       0        0            NE              NE                 NE             0         0          0     
    0.5 / 0.5H                      134        0      12.6      1.51        12.0          12.5                  12.2          12.6       9.72      15.6   
    1 / 1H                          134        0      16.2      1.63        10.0          16.1                  10.1          16.2      12.6       19.9   
    1.5 / 1.5H                      134        0      15.6      1.46         9.3          15.6                   9.3          15.5      12.3       19.0   
    2 / 2H                          134        0      13.4      1.35        10.1          13.4                  10.0          13.3      10.8       16.5   
    3 / 3H                          134        0       8.47     1.25        14.7           8.38                 15.0           8.40      5.88      10.9   
    4 / 4H                          134        0       4.79     1.02        21.2           4.69                 22.0           4.79      2.70       7.09  
    8 / 8H                          134        0       0.348    0.180       51.7           0.303                58.4           0.318     0.0760     0.866 
    12 / 12H                        134        0       0.0224   0.0189      84.6           0.0156              111.6           0.0170    0.00200    0.0830
  Day 2                                                                                                                                                   
    24 / 24H                        134      134       0        0            NE              NE                 NE             0         0          0     
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————

NE: Not Estimable

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Experimental use!

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Table Implementing 1/3 Imputation Rule

Preview

lyt <- lyt_rows %>%
  analyze_vars_in_cols(
    vars = c("AVAL", "AVALCAT1", rep("AVAL", 8)),
    .stats = c("n", "n_blq", "mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max"),
    .formats = c(
      n = "xx.", n_blq = "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)
    ),
    .labels = c(
      n = "n", n_blq = "Number\nof\nLTRs/BLQs", mean = "Mean", sd = "SD", cv = "CV (%) Mean",
      geom_mean = "Geometric Mean", geom_cv = "CV % Geometric Mean", median = "Median", min = "Minimum", max = "Maximum"
    ),
    imp_rule = "1/3",
    .aligns = "decimal"
  )

result <- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %>% prune_table()

# Decorating
main_title(result) <- "Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable"
subtitles(result) <- c(
  "Protocol: xxxxx",
  paste("Analyte: ", unique(adpc_1$PARAM)),
  paste("Treatment:", unique(adpc_1$ACTARM))
)
main_footer(result) <- c("NE: Not Estimable", "ND: Not Derived")

result

Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable
Protocol: xxxxx
Analyte:  Plasma Drug X
Treatment: A: Drug X

——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Treatment Group                            Number                                                                                                         
  Visit                                      of                                                                                                           
    Nominal Time (hr) / Timepoint    n    LTRs/BLQs    Mean       SD     CV (%) Mean   Geometric Mean   CV % Geometric Mean   Median    Minimum    Maximum
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
A: Drug X (N=134)                                                                                                                                         
  Day 1                                                                                                                                                   
    0 / Predose                     134      134        ND        ND         ND              NE                 ND             0           ND       0     
    0.5 / 0.5H                      134        0      12.6      1.51        12.0          12.5                  12.2          12.6       9.72      15.6   
    1 / 1H                          134        0      16.2      1.63        10.0          16.1                  10.1          16.2      12.6       19.9   
    1.5 / 1.5H                      134        0      15.6      1.46         9.3          15.6                   9.3          15.5      12.3       19.0   
    2 / 2H                          134        0      13.4      1.35        10.1          13.4                  10.0          13.3      10.8       16.5   
    3 / 3H                          134        0       8.47     1.25        14.7           8.38                 15.0           8.40      5.88      10.9   
    4 / 4H                          134        0       4.79     1.02        21.2           4.69                 22.0           4.79      2.70       7.09  
    8 / 8H                          134        0       0.348    0.180       51.7           0.303                58.4           0.318     0.0760     0.866 
    12 / 12H                        134        0       0.0224   0.0189      84.6           0.0156              111.6           0.0170    0.00200    0.0830
  Day 2                                                                                                                                                   
    24 / 24H                        134      134        ND        ND         ND              NE                 ND             0           ND       0     
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————

NE: Not Estimable
ND: Not Derived

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.

Table Implementing 1/2 Imputation Rule

Preview

lyt <- lyt_rows %>%
  analyze_vars_in_cols(
    vars = c("AVAL", "AVALCAT1", rep("AVAL", 8)),
    .stats = c("n", "n_blq", "mean", "sd", "cv", "geom_mean", "geom_cv", "median", "min", "max"),
    .formats = c(
      n = "xx.", n_blq = "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)
    ),
    .labels = c(
      n = "n", n_blq = "Number\nof\nLTRs/BLQs", mean = "Mean", sd = "SD", cv = "CV (%) Mean",
      geom_mean = "Geometric Mean", geom_cv = "CV % Geometric Mean", median = "Median", min = "Minimum", max = "Maximum"
    ),
    imp_rule = "1/2",
    .aligns = "decimal"
  )

result <- build_table(lyt, df = adpc_1, alt_counts_df = adsl) %>% prune_table()

# Decorate table
main_title(result) <- "Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable"
subtitles(result) <- c(
  "Protocol: xxxxx",
  paste("Analyte: ", unique(adpc_1$PARAM)),
  paste("Treatment:", unique(adpc_1$ACTARM))
)
main_footer(result) <- "ND: Not Derived"

result

Summary of PK Concentrations by Nominal Time and Treatment: PK Evaluable
Protocol: xxxxx
Analyte:  Plasma Drug X
Treatment: A: Drug X

——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Treatment Group                            Number                                                                                                         
  Visit                                      of                                                                                                           
    Nominal Time (hr) / Timepoint    n    LTRs/BLQs    Mean       SD     CV (%) Mean   Geometric Mean   CV % Geometric Mean   Median    Minimum    Maximum
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
A: Drug X (N=134)                                                                                                                                         
  Day 1                                                                                                                                                   
    0 / Predose                     134      134        ND        ND         ND              ND                 ND              ND         ND       0     
    0.5 / 0.5H                      134        0      12.6      1.51        12.0          12.5                  12.2          12.6       9.72      15.6   
    1 / 1H                          134        0      16.2      1.63        10.0          16.1                  10.1          16.2      12.6       19.9   
    1.5 / 1.5H                      134        0      15.6      1.46         9.3          15.6                   9.3          15.5      12.3       19.0   
    2 / 2H                          134        0      13.4      1.35        10.1          13.4                  10.0          13.3      10.8       16.5   
    3 / 3H                          134        0       8.47     1.25        14.7           8.38                 15.0           8.40      5.88      10.9   
    4 / 4H                          134        0       4.79     1.02        21.2           4.69                 22.0           4.79      2.70       7.09  
    8 / 8H                          134        0       0.348    0.180       51.7           0.303                58.4           0.318     0.0760     0.866 
    12 / 12H                        134        0       0.0224   0.0189      84.6           0.0156              111.6           0.0170    0.00200    0.0830
  Day 2                                                                                                                                                   
    24 / 24H                        134      134        ND        ND         ND              ND                 ND              ND         ND       0     
——————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————

ND: Not Derived

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

library(dplyr)
library(tern)

adsl <- random.cdisc.data::cadsl %>%
  filter(ACTARM == "A: Drug X")
adpc <- random.cdisc.data::cadpc %>%
  filter(ACTARM == "A: Drug X", PARAM == "Plasma Drug X")

# Setting up the data
adpc_1 <- adpc %>%
  mutate(
    NFRLT = as.factor(NFRLT),
    AVALCAT1 = as.factor(AVALCAT1),
    NOMTPT = as.factor(paste(NFRLT, "/", PCTPT))
  ) %>%
  select(NOMTPT, ACTARM, VISIT, AVAL, PARAM, AVALCAT1)

adpc_1$NOMTPT <- factor(
  adpc_1$NOMTPT,
  levels = levels(adpc_1$NOMTPT)[order(as.numeric(gsub(".*?([0-9\\.]+).*", "\\1", levels(adpc_1$NOMTPT))))]
)

# Row structure
lyt_rows <- basic_table() %>%
  split_rows_by(
    var = "ACTARM",
    split_fun = drop_split_levels,
    split_label = "Treatment Group",
    label_pos = "topleft"
  ) %>%
  add_rowcounts(alt_counts = TRUE) %>%
  split_rows_by(
    var = "VISIT",
    split_fun = drop_split_levels,
    split_label = "Visit",
    label_pos = "topleft"
  ) %>%
  split_rows_by(
    var = "NOMTPT",
    split_fun = drop_split_levels,
    split_label = "Nominal Time (hr) / Timepoint",
    label_pos = "topleft",
    child_labels = "hidden"
  )

Reproducibility

Timestamp

[1] "2025-07-09 17:56:16 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     2025-07-09
 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
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 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
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 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
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 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
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 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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