PKPT11

Pharmacokinetic Parameter Estimated Ratios of Geometric Means and 90% Confidence Intervals for AUC and CMAX

Output

Standard Table – Plasma
Data Setup

Preview
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Code

# create layout
lyt <- basic_table() %>%
  split_rows_by(
    var = "PKPARAM",
    label_pos = "topleft",
    split_fun = keep_split_levels(c("AUCinf obs (day*ug/mL)", "Cmax (ug/mL)")),
    split_label = "PK Parameter"
  ) %>%
  split_rows_by(
    var = "COMP",
    split_fun = remove_split_levels(paste0(comp_dose, "/", comp_dose)),
    indent_mod = 11L,
    child_labels = "hidden"
  ) %>%
  split_cols_by_multivar(
    vars = rep("AVAL", 4),
    varlabels = c(
      "n",
      "Geometric Mean Ratio",
      "90% CI Lower Bound",
      "90% CI Upper Bound"
    )
  ) %>%
  analyze_colvars(
    afun = afun_pk_gmr(),
    extra_args = list(
      compare_dose = comp_dose,
      denom = TRUE,
      arm_var = arm_var
    )
  ) %>%
  append_varlabels(adpp0, "COMP", 12L)

result <- build_table(lyt, df = adpp0)

main_title(result) <- paste0(
  "Estimated Ratios of Geometric Means and 90% Confidence Intervals for AUC and CMAX Following ",
  unique(adpp0$REGIMEN), "\nof ", comp_dose, " in Comparison with ",
  paste(other_doses, collapse = " & "), ", PK Population"
)
subtitles(result) <- paste("Analyte:", unique(adpp0$PPCAT))

result

Estimated Ratios of Geometric Means and 90% Confidence Intervals for AUC and CMAX Following BID
of A: Drug X in Comparison with C: Combination, PK Population
Analyte: Plasma Drug X

———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
PK Parameter                                                                                                           
                        Comparison                  n    Geometric Mean Ratio   90% CI Lower Bound   90% CI Upper Bound
———————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
AUCinf obs (day*ug/mL)                                                                                                 
                        C: Combination/A: Drug X   266          0.962                 0.925                 1.00       
Cmax (ug/mL)                                                                                                           
                        C: Combination/A: Drug X   266          0.997                 0.958                 1.04

Experimental use!

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Code

library(dplyr)
library(tern)

adpp <- random.cdisc.data::cadpp

# Comparator Dose - A: Drug X
arm_var <- "TRT01A"
comp_dose <- "A: Drug X"
other_doses <- as.character(unique(adpp[[arm_var]])[unique(adpp[[arm_var]]) != comp_dose])

adpp <- adpp %>%
  filter(AVISIT == "CYCLE 1 DAY 1") %>%
  mutate(
    COMP = paste0(TRT01A, "/", comp_dose),
    COMP_AVAL = ifelse(TRT01A == comp_dose, paste0(AVAL, comp_dose), AVAL)
  )

for (dose in other_doses) {
  temp_df <- adpp[adpp[[arm_var]] == comp_dose, ]
  temp_df$COMP <- paste0(dose, "/", comp_dose)
  adpp <- rbind(adpp, temp_df)
}

# Plasma Drug X
adpp0 <- adpp %>%
  filter(PPCAT == "Plasma Drug X") %>%
  h_pkparam_sort() %>%
  mutate(PKPARAM = factor(paste0(TLG_DISPLAY, " (", AVALU, ")"))) %>%
  var_relabel(COMP = "Comparison")

# statistics function
s_gmr <- function(df,
                  compare_dose = comp_dose, # comparator, defaults to comp_dose defined above (string)
                  denom = TRUE, # whether to use comparator as denominator, defaults to TRUE (logical)
                  arm_var = arm_var) { # arm variable, defaults to arm_var defined above (string)
  which_num <- !grepl(compare_dose, df[[arm_var]])

  x_num <- as.numeric(df[which_num, ][["AVAL"]])
  x_num <- x_num[!is.na(x_num)]
  x_num_no_negative_vals <- x_num
  x_num_no_negative_vals[x_num_no_negative_vals <= 0] <- NA

  x_denom <- as.numeric(gsub(compare_dose, "", df[!which_num, ][["AVAL"]]))
  x_denom <- x_denom[!is.na(x_denom)]
  x_denom_no_negative_vals <- x_denom
  x_denom_no_negative_vals[x_denom_no_negative_vals <= 0] <- NA

  x_num_log <- log(x_num_no_negative_vals)
  x_denom_log <- log(x_denom_no_negative_vals)

  if (denom) {
    geom_mean_ratio <- exp(mean(x_num_log, na.rm = FALSE)) / exp(mean(x_denom_log, na.rm = FALSE))
    geom_mean_ci <- t.test(x_num_log, x_denom_log, conf.level = 0.90)$conf.int
  } else {
    geom_mean_ratio <- exp(mean(x_denom_log, na.rm = FALSE)) / exp(mean(x_num_log, na.rm = FALSE))
    geom_mean_ci <- t.test(x_denom_log, x_num_log, conf.level = 0.90)$conf.int
  }

  list(
    n = nrow(df),
    geom_mean_ratio = geom_mean_ratio,
    gmr_ci_lwr = exp(geom_mean_ci[1]),
    gmr_ci_upr = exp(geom_mean_ci[2])
  )
}

afun_pk_gmr <- function(
    .formats = list(
      n = "xx.",
      geom_mean_ratio = format_sigfig(3),
      gmr_ci_lwr = format_sigfig(3),
      gmr_ci_upr = format_sigfig(3)
    ),
    compare_dose = comp_dose,
    denom = TRUE) {
  checkmate::assert_list(.formats)
  checkmate::assert_subset(names(.formats), c("n", "geom_mean_ratio", "gmr_ci_lwr", "gmr_ci_upr"))

  afun_lst <- Map(
    function(stat, fmt, compare_dose, denom, arm_var) {
      function(df, .spl_context) {
        x_stat <- s_gmr(df, compare_dose = compare_dose, denom = denom, arm_var = arm_var)[[stat]]
        rcell(x_stat, format = fmt, label = tail(.spl_context$value, 1))
      }
    },
    stat = names(.formats),
    fmt = .formats,
    compare_dose = compare_dose,
    denom = denom,
    arm_var = arm_var
  )

  afun_lst
}

Reproducibility

Timestamp

[1] "2024-11-20 18:12:06 UTC"

Session Info

─ Session info ───────────────────────────────────────────────────────────────
 setting  value
 version  R version 4.4.1 (2024-06-14)
 os       Ubuntu 22.04.5 LTS
 system   x86_64, linux-gnu
 ui       X11
 language (EN)
 collate  en_US.UTF-8
 ctype    en_US.UTF-8
 tz       Etc/UTC
 date     2024-11-20
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package           * version     date (UTC) lib source
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 brio                1.1.5       2024-04-24 [1] RSPM
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 checkmate           2.3.2       2024-07-29 [1] RSPM
 cli                 3.6.3       2024-06-21 [1] RSPM
 codetools           0.2-20      2024-03-31 [2] CRAN (R 4.4.1)
 colorspace          2.1-1       2024-07-26 [1] RSPM
 curl                6.0.1       2024-11-14 [1] RSPM
 digest              0.6.37      2024-08-19 [1] RSPM
 dplyr             * 1.1.4       2023-11-17 [1] RSPM
 evaluate            1.0.1       2024-10-10 [1] RSPM
 fansi               1.0.6       2023-12-08 [1] RSPM
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 knitr               1.49        2024-11-08 [1] RSPM
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 lifecycle           1.0.4       2023-11-07 [1] RSPM
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 nestcolor           0.1.2.9017  2024-11-20 [1] https://p~
 pillar              1.9.0       2023-03-22 [1] RSPM
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 purrr               1.0.2       2023-08-10 [1] RSPM
 R6                  2.5.1       2021-08-19 [1] RSPM
 random.cdisc.data   0.3.16.9001 2024-11-20 [1] https://p~
 rbibutils           2.3         2024-10-04 [1] RSPM
 Rdpack              2.6.2       2024-11-15 [1] RSPM
 rlang               1.1.4       2024-06-04 [1] RSPM
 rmarkdown           2.29        2024-11-04 [1] RSPM
 rtables           * 0.6.10.9005 2024-11-20 [1] https://p~
 scales              1.3.0       2023-11-28 [1] RSPM
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 stringr             1.5.1       2023-11-14 [1] RSPM
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 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
 utf8                1.2.4       2023-10-22 [1] RSPM
 vctrs               0.6.5       2023-12-01 [1] RSPM
 xfun                0.49        2024-10-31 [1] RSPM
 yaml                2.3.10      2024-07-26 [1] RSPM

 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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

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Output

Reproducibility

Timestamp

Session Info

.lock file

`.lock` file