ADAT04A

Baseline Prevalence and Incidence of Treatment Emergent NAbs

Summary of Treatment Emergent NAbs
Data Setup

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Code

# Layout for Baseline Prevalence of NAbs
lyt_bl <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(
    "ACTARM",
    split_fun = custom_column_split_fun
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = c("ADABLPFL", "PADABLPFL"),
    table_names = "t1",
    .stats = "count",
    var_labels = "Baseline Prevalence of NAbs",
    show_labels = "visible"
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = "PNABBLFL",
    table_names = "t2",
    .indent_mods = 1L
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = "NNABBLFL",
    .stats = "count",
    table_names = "t3",
    .indent_mods = 1L,
    show_labels = "hidden"
  )

# Layout for incidence of NAbs
lyt_pb <- basic_table(show_colcounts = TRUE) %>%
  split_cols_by(
    "ACTARM",
    split_fun = custom_column_split_fun
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = c("ADPBLPFL", "ADAPFL"),
    .stats = "count",
    var_labels = "Incidence of Treatment Emergent NAbs",
    show_labels = "visible",
    table_names = "tb1"
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = "TENABPFL",
    table_names = "tb2",
    .indent_mods = 1L,
    show_labels = "hidden"
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = c("TINPBFL", "TENPBFL"),
    .stats = "count",
    table_names = "tb3",
    .indent_mods = 1L,
    show_labels = "hidden"
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = "NABNFL",
    table_names = "tb4",
    .indent_mods = 1L,
    show_labels = "hidden"
  ) %>%
  count_patients_with_flags(
    "USUBJID",
    flag_variables = "TUNPBFL",
    .stats = "count",
    table_names = "tb5",
    .indent_mods = 1L,
    show_labels = "hidden"
  )

result_1 <- build_table(lyt_bl, df = adab_b, alt_counts_df = adsl)
result_2 <- build_table(lyt_pb, df = adab_pb, alt_counts_df = adsl)

# Combine tables
result <- rbind(result_1, result_2)

main_title(result) <- paste(
  "Baseline Prevalence and Incidence of Treatment Emergent NAbs"
)
subtitles(result) <- paste("Protocol:", unique(adab$PARCAT1)[1])

main_footer(result) <- "NAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s)
  Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s)
  Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample
  Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample
  Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.
Number of patients positive for Treatment Emergent NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have treatment-induced NAb or treatment-enhanced NAb during the study period.
Treatment-induced = a patient with negative or missing baseline result(s) and at least one positive post-baseline result. Treatment-enhanced = a patient with positive result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.
Number of patients negative for Treatment Emergent NAb = number of post-baseline evaluable patients with negative or missing baseline NAb result(s) and all negative post-baseline NAb results, or a patient who is NAb treatment unaffected.
Treatment unaffected = A post-baseline evaluable patient with a positive result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing. For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer."

result

Baseline Prevalence and Incidence of Treatment Emergent NAbs
Protocol: A: Drug X Antibody

————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
                                                     A: Drug X   C: Combination   All Drug X   B: Placebo   All Patients
                                                      (N=134)       (N=132)        (N=266)      (N=134)       (N=400)   
————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
Baseline Prevalence of NAbs                                                                                             
  Baseline evaluable patients for ADA                   134           132            266           0            266     
  Patients with a positive ADA sample at baseline       63             64            127           0            127     
  Patients with a positive NAb sample at baseline        0             0              0            0             0      
  Patient with no positive NAb samples at baseline       0             0              0            0             0      
Incidence of Treatment Emergent NAbs                                                                                    
  Post-baseline evaluable patients for ADA              134           132            266           0            266     
  Patients positive for ADA                             66             59            125           0            125     
  Patients positive for Treatment Emergent NAb           0             0              0            0             0      
  Treatment-induced NAb                                  0             0              0            0             0      
  Treatment-enhanced NAb                                 0             0              0            0             0      
  Patients negative for NAb                              0             0              0            0             0      
  Treatment unaffected                                   0             0              0            0             0      
————————————————————————————————————————————————————————————————————————————————————————————————————————————————————————

NAb = Neutralizing Antibodies ADA = Anti-Drug Antibodies (is also referred to as ATA, or Anti-Therapeutic Antibodies) Baseline evaluable patient for ADA = a patient with an ADA assay result from a baseline sample(s)
  Baseline evaluable patient for NAb = a patient with a NAb assay result from a baseline sample(s)
  Post-baseline evaluable patient for ADA = a patient with an ADA assay result from at least one post-baseline sample
  Post-baseline evaluable patient for NAb = a patient with a NAb assay result from at least one post-baseline sample
  Number of patients positive for ADA = the number of post-baseline evaluable patients for ADA determined to have Treatment Emergent ADA during the study period.
Number of patients positive for Treatment Emergent NAb = the number (and percentage) of post-baseline evaluable patients for ADA determined to have treatment-induced NAb or treatment-enhanced NAb during the study period.
Treatment-induced = a patient with negative or missing baseline result(s) and at least one positive post-baseline result. Treatment-enhanced = a patient with positive result at baseline who has one or more post-baseline titer results that are at least 0.60 t.u. greater than the baseline titer result.
Number of patients negative for Treatment Emergent NAb = number of post-baseline evaluable patients with negative or missing baseline NAb result(s) and all negative post-baseline NAb results, or a patient who is NAb treatment unaffected.
Treatment unaffected = A post-baseline evaluable patient with a positive result at baseline and (a) where all post-baseline titer results are less than 0.60 t.u. greater than the baseline titer result, OR (b) where all post-baseline results are negative or missing. For any positive sample with titer result less than the minimum reportable titer or any positive sample where a titer cannot be obtained, titer value is imputed as equal to the minimum reportable titer.

Experimental use!

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Code

library(tern)
library(dplyr)
library(tibble)

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


# Order needed for the columns is c(1, 3, 4, 2, 5)
reorder_facets <- function(splret, spl, fulldf, ...) {
  ord <- c(1, 3, 4, 2, 5)
  make_split_result(
    splret$values[ord],
    splret$datasplit[ord],
    splret$labels[ord]
  )
}

# Create a custom split function for adding the new columns (facets) and sorting them
custom_column_split_fun <- make_split_fun(
  post = list(
    add_combo_facet("all_X",
      label = "All Drug X",
      levels = c("A: Drug X", "C: Combination")
    ),
    add_combo_facet("all_pt",
      label = "All Patients",
      levels = c("A: Drug X", "B: Placebo", "C: Combination")
    ),
    reorder_facets
  )
)

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

# Baseline Pts
adab_b <- df_explicit_na(adab) %>%
  filter(
    ABLFL == "Y",
    ADABLPFL == "Y",
    PARAM %in% c(
      "ADA interpreted per sample result",
      "NAB interpreted per sample result"
    )
  ) %>%
  select(-PARAMCD, -AVALC, -AVALU) %>%
  tidyr::pivot_wider(
    names_from = PARAM,
    values_from = AVAL
  ) %>%
  mutate(
    across(
      any_of(c(
        "ADA interpreted per sample result",
        "NAB interpreted per sample result"
      )),
      as.logical
    )
  ) %>%
  mutate(
    ADABLPFL = ADABLPFL == "Y",
    PADABLPFL = if (exists("ADA interpreted per sample result", where = .)) {
      `ADA interpreted per sample result` == "TRUE"
    } else {
      FALSE
    },
    PNABBLFL = if (exists("NAB interpreted per sample result", where = .)) {
      `NAB interpreted per sample result` == "TRUE"
    } else {
      FALSE
    },
    NNABBLFL = if (exists("NAB interpreted per sample result", where = .)) {
      `NAB interpreted per sample result` == "FALSE"
    } else {
      FALSE
    }
  ) %>%
  var_relabel(
    ADABLPFL = "Baseline evaluable patients for ADA",
    PADABLPFL = "Patients with a positive ADA sample at baseline",
    PNABBLFL = "Patients with a positive NAb sample at baseline",
    NNABBLFL = "Patient with no positive NAb samples at baseline"
  )

# Post Baseline Treatment Enhanced NAb positive Pts
adab_pb <- df_explicit_na(adab) %>%
  filter(
    ABLFL != "Y",
    ADPBLPFL == "Y",
    PARAM %in% c(
      "ADA interpreted per sample result",
      "Treatment Emergent - Positive, Neutralizing Antibody",
      "Treatment induced ADA, Neutralizing Antibody",
      "Treatment enhanced ADA, Neutralizing Antibody",
      "NAB interpreted per sample result",
      "Treatment unaffected, Neutralizing Antibody"
    )
  ) %>%
  select(-PARAMCD, -AVALC, -AVALU, -ARRLT, -NRRLT) %>%
  unique() %>%
  tidyr::pivot_wider(
    names_from = PARAM,
    values_from = AVAL
  ) %>%
  mutate(
    across(
      any_of(c(
        "ADA interpreted per sample result", "NAB interpreted per sample result",
        "Treatment Emergent - Positive, Neutralizing Antibody",
        "Treatment induced ADA, Neutralizing Antibody",
        "Treatment enhanced ADA, Neutralizing Antibody",
        "Treatment unaffected, Neutralizing Antibody"
      )),
      as.logical
    )
  ) %>%
  mutate(
    ADPBLPFL = ADPBLPFL == "Y",
    ADAPFL = if (exists("ADA interpreted per sample result", where = .)) {
      `ADA interpreted per sample result` == "TRUE"
    } else {
      FALSE
    },
    TENABPFL = if (exists("Treatment Emergent - Positive, Neutralizing Antibody", where = .)) {
      `Treatment Emergent - Positive, Neutralizing Antibody` == "TRUE"
    } else {
      FALSE
    },
    TINPBFL = if (exists("Treatment induced ADA, Neutralizing Antibody", where = .)) {
      `Treatment induced ADA, Neutralizing Antibody` == "TRUE"
    } else {
      FALSE
    },
    TENPBFL = if (exists("Treatment enhanced ADA, Neutralizing Antibody", where = .)) {
      `Treatment enhanced ADA, Neutralizing Antibody` == "TRUE"
    } else {
      FALSE
    },
    NABNFL = if (exists("NAB interpreted per sample result", where = .)) {
      `NAB interpreted per sample result` == "FALSE"
    } else {
      FALSE
    },
    TUNPBFL = if (exists("Treatment unaffected, Neutralizing Antibody", where = .)) {
      `Treatment unaffected, Neutralizing Antibody` == "TRUE"
    } else {
      FALSE
    }
  ) %>%
  var_relabel(
    ADPBLPFL = "Post-baseline evaluable patients for ADA",
    ADAPFL = "Patients positive for ADA",
    TENABPFL = "Patients positive for Treatment Emergent NAb",
    TINPBFL = "Treatment-induced NAb",
    TENPBFL = "Treatment-enhanced NAb",
    NABNFL = "Patients negative for NAb",
    TUNPBFL = "Treatment unaffected"
  )

Reproducibility

Timestamp

[1] "2024-11-02 18:36:50 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     2024-11-02
 pandoc   3.4 @ /usr/bin/ (via rmarkdown)

─ Packages ───────────────────────────────────────────────────────────────────
 package           * version date (UTC) lib source
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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

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Reproducibility

Timestamp

Session Info

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`.lock` file