TLG Catalog - Stable
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  2. ADA
  3. ADAT03
  • Introduction

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  • Appendix
    • Reproducibility

  • Index

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  • Output
  • Reproducibility
    • Timestamp
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  1. Tables
  2. ADA
  3. ADAT03

ADAT03

Summary of Serum Concentrations at Timepoints Where ADA Samples Were Collected and Analyzed


Output

  • Standard Table (μg/mL)
  • Data Setup
  • Preview
  • Try this using WebR
Code
# parameters in columns
adat03_stats <- c("n", "mean", "sd", "median", "min", "max", "cv", "geom_mean", "count_fraction")
adat03_lbls <- c(
  n = "Total Number\nof Measurable\n Samples",
  mean = "Mean",
  sd = "SD",
  median = "Median",
  min = "Minimum",
  max = "Maximum",
  cv = "CV (%)",
  geom_mean = "Geometric Mean",
  count_fraction = paste0("Samples with\nConcentration\n≤ ", max_conc, "μg/mL")
)
adat03_fmts <- c(
  n = "xx.",
  mean = format_sigfig(3),
  sd = format_sigfig(3),
  median = format_sigfig(3),
  min = format_sigfig(3),
  max = format_sigfig(3),
  cv = "xx.x",
  geom_mean = format_sigfig(3),
  count_fraction = format_count_fraction
)

afun_list <- lapply(
  1:9,
  function(i) make_afun(s_summary, .stats = adat03_stats[i], .formats = adat03_fmts[i], .labels = "Overall")
)

# lyt creation
lyt <- basic_table() %>%
  split_rows_by(
    var = "ARM",
    label_pos = "topleft",
    split_label = "Treatment Group",
    split_fun = drop_split_levels,
    section_div = ""
  ) %>%
  add_rowcounts() %>%
  split_rows_by(
    var = "VISIT",
    label_pos = "topleft",
    split_label = "Visit",
    split_fun = drop_split_levels,
    child_labels = "hidden"
  ) %>%
  analyze_vars_in_cols(
    vars = c(rep("AVAL", 8), "AVAL_LT"),
    .stats = adat03_stats,
    .labels = adat03_lbls,
    .formats = adat03_fmts
  ) %>%
  analyze_colvars(
    afun_list,
    nested = FALSE,
    extra_args = list(".labels" = "Overall")
  )

result <- build_table(lyt, anl, alt_counts_df = adsl)

main_title(result) <- paste(
  "Summary of Serum Concentrations (μg/mL) at Timepoints Where ADA Samples Were Collected and Analyzed\n
  Protocol:", unique(adab$PARCAT1)[1]
)
subtitles(result) <- paste("Analyte:", unique(adab$PARAMCD)[1])
fnotes_at_path(result, rowpath = NULL, colpath = c("multivars", "AVAL")) <- "Refers to the total no. of measurable ADA samples that have a corresponding measurable drug concentration sample (i.e. results with valid numeric values and LTRs). LTR results on post-dose samples are replaced by aaa µg/mL i.e. half of MQC value." 
fnotes_at_path(result, rowpath = NULL, colpath = c("multivars", "AVAL_LT")) <- "In validation, the assay was able to detect yyy ng/mL of surrogate ADA in the presence of zzz µg/mL of [drug]. BLQ = Below Limit of Quantitation, LTR = Lower than Reportable, MQC = Minimum Quantifiable Concentration, ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies). RXXXXXXX is also known as [drug]" 

result
Summary of Serum Concentrations (μg/mL) at Timepoints Where ADA Samples Were Collected and Analyzed

  Protocol: A: Drug X Antibody
Analyte: R1800000

———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
                         Total Number                                                                         Samples with 
Treatment Group          of Measurable                                                                        Concentration
  Visit                   Samples {1}    Mean    SD    Median   Minimum   Maximum   CV (%)   Geometric Mean   ≤ 15μg/mL {2}
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
A: Drug X (N=536)                                                                                                          
  Day 1                       402         0      0       0         0         0        NA           NA          402 (100%)  
  Day 2                       134        16.2   1.63    16.2     12.6      19.9      10.0         16.1         39 (29.1%)  

C: Combination (N=792)                                                                                                     
  Day 1                       528         0      0       0         0         0        NA           NA          528 (100%)  
  Day 2                       264        24.7   8.65    22.5     12.6      39.5      35.0         23.2         28 (10.6%)  

Overall                      1328        6.54   11.0     0         0       39.5     167.5          NA          997 (75.1%) 
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————

{1} - Refers to the total no. of measurable ADA samples that have a corresponding measurable drug concentration sample (i.e. results with valid numeric values and LTRs). LTR results on post-dose samples are replaced by aaa µg/mL i.e. half of MQC value.
{2} - In validation, the assay was able to detect yyy ng/mL of surrogate ADA in the presence of zzz µg/mL of [drug]. BLQ = Below Limit of Quantitation, LTR = Lower than Reportable, MQC = Minimum Quantifiable Concentration, ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies). RXXXXXXX is also known as [drug]
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
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.

Code
library(dplyr)
library(tern)

adsl <- random.cdisc.data::cadsl
adab <- random.cdisc.data::cadab
adpc <- random.cdisc.data::cadpc

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adab <- df_explicit_na(adab)
adpc <- df_explicit_na(adpc)

# Adjust zzz parameter
max_conc <- 15

adpc <- adpc %>% select(USUBJID, NFRLT, AVAL, AVALU, AVALCAT1)

anl <- adab %>%
  filter(., PARAM == "ADA interpreted per sample result") %>%
  select(-AVAL, AVALC, AVALU)

anl <- merge(anl, adpc, by = c("USUBJID", "NFRLT")) %>%
  mutate(AVAL_LT = ifelse(AVAL <= max_conc, TRUE, FALSE))

Reproducibility

Timestamp

[1] "2025-07-05 17:52:01 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
 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.

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

.lock file

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

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ADAT02
ADAT04A
Source Code
---
title: ADAT03
subtitle: Summary of Serum Concentrations at Timepoints Where ADA Samples Were Collected and Analyzed
---

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

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

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

adsl <- random.cdisc.data::cadsl
adab <- random.cdisc.data::cadab
adpc <- random.cdisc.data::cadpc

# Ensure character variables are converted to factors and empty strings and NAs are explicit missing levels.
adsl <- df_explicit_na(adsl)
adab <- df_explicit_na(adab)
adpc <- df_explicit_na(adpc)

# Adjust zzz parameter
max_conc <- 15

adpc <- adpc %>% select(USUBJID, NFRLT, AVAL, AVALU, AVALCAT1)

anl <- adab %>%
  filter(., PARAM == "ADA interpreted per sample result") %>%
  select(-AVAL, AVALC, AVALU)

anl <- merge(anl, adpc, by = c("USUBJID", "NFRLT")) %>%
  mutate(AVAL_LT = ifelse(AVAL <= max_conc, TRUE, FALSE))
```

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

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

## Output

:::: panel-tabset
## Standard Table (μg/mL)

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

```{r variant1, test = list(result_v1 = "result")}
# parameters in columns
adat03_stats <- c("n", "mean", "sd", "median", "min", "max", "cv", "geom_mean", "count_fraction")
adat03_lbls <- c(
  n = "Total Number\nof Measurable\n Samples",
  mean = "Mean",
  sd = "SD",
  median = "Median",
  min = "Minimum",
  max = "Maximum",
  cv = "CV (%)",
  geom_mean = "Geometric Mean",
  count_fraction = paste0("Samples with\nConcentration\n≤ ", max_conc, "μg/mL")
)
adat03_fmts <- c(
  n = "xx.",
  mean = format_sigfig(3),
  sd = format_sigfig(3),
  median = format_sigfig(3),
  min = format_sigfig(3),
  max = format_sigfig(3),
  cv = "xx.x",
  geom_mean = format_sigfig(3),
  count_fraction = format_count_fraction
)

afun_list <- lapply(
  1:9,
  function(i) make_afun(s_summary, .stats = adat03_stats[i], .formats = adat03_fmts[i], .labels = "Overall")
)

# lyt creation
lyt <- basic_table() %>%
  split_rows_by(
    var = "ARM",
    label_pos = "topleft",
    split_label = "Treatment Group",
    split_fun = drop_split_levels,
    section_div = ""
  ) %>%
  add_rowcounts() %>%
  split_rows_by(
    var = "VISIT",
    label_pos = "topleft",
    split_label = "Visit",
    split_fun = drop_split_levels,
    child_labels = "hidden"
  ) %>%
  analyze_vars_in_cols(
    vars = c(rep("AVAL", 8), "AVAL_LT"),
    .stats = adat03_stats,
    .labels = adat03_lbls,
    .formats = adat03_fmts
  ) %>%
  analyze_colvars(
    afun_list,
    nested = FALSE,
    extra_args = list(".labels" = "Overall")
  )

result <- build_table(lyt, anl, alt_counts_df = adsl)

main_title(result) <- paste(
  "Summary of Serum Concentrations (μg/mL) at Timepoints Where ADA Samples Were Collected and Analyzed\n
  Protocol:", unique(adab$PARCAT1)[1]
)
subtitles(result) <- paste("Analyte:", unique(adab$PARAMCD)[1])
fnotes_at_path(result, rowpath = NULL, colpath = c("multivars", "AVAL")) <- "Refers to the total no. of measurable ADA samples that have a corresponding measurable drug concentration sample (i.e. results with valid numeric values and LTRs). LTR results on post-dose samples are replaced by aaa µg/mL i.e. half of MQC value." # nolint: line_length.
fnotes_at_path(result, rowpath = NULL, colpath = c("multivars", "AVAL_LT")) <- "In validation, the assay was able to detect yyy ng/mL of surrogate ADA in the presence of zzz µg/mL of [drug]. BLQ = Below Limit of Quantitation, LTR = Lower than Reportable, MQC = Minimum Quantifiable Concentration, ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies). RXXXXXXX is also known as [drug]" # nolint: line_length.

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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