FSTG01

Subgroup Analysis of Best Overall Response

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Code

df <- extract_rsp_subgroups(
  variables = list(
    rsp = "is_rsp",
    arm = "ARM",
    subgroups = c("SEX", "BMRKR2"),
    strata = "STRATA2"
  ),
  data = anl_rsp_arms_ab,
  conf_level = 0.95
)

result <- basic_table() %>%
  tabulate_rsp_subgroups(df, vars = c("n_tot", "n", "n_rsp", "prop", "or", "ci"))

plot <- g_forest(tbl = result)
plot

Experimental use!

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Code

anl_rsp_comb_arms_ac <- anl %>%
  mutate(is_rsp = AVALC %in% c("CR", "PR")) %>%
  filter(ARMCD %in% c("ARM B", "ARM A", "ARM C")) %>%
  mutate(
    ARMCD = relevel(ARMCD, ref = "ARM B") %>%
      droplevels() %>%
      combine_levels(levels = c("ARM A", "ARM C")),
    ARM = relevel(ARM, ref = "B: Placebo") %>%
      droplevels() %>%
      combine_levels(levels = c("A: Drug X", "C: Combination")),
    # reorder levels of SEX
    SEX = forcats::fct_relevel(SEX, "M", "F"),
    # reorder levels of STRATA1 by frequency
    STRATA1 = forcats::fct_infreq(STRATA1)
  ) %>%
  droplevels()
var_labels(anl_rsp_comb_arms_ac) <- c(anl_labels, is_rsp = "Is Responder")

df <- extract_rsp_subgroups(
  variables = list(
    rsp = "is_rsp",
    arm = "ARMCD",
    subgroups = c("SEX", "BMRKR2"),
    strata = "STRATA2"
  ),
  data = anl_rsp_comb_arms_ac,
  conf_level = 0.95
)

result <- basic_table() %>%
  tabulate_rsp_subgroups(df, vars = c("n_tot", "n", "n_rsp", "prop", "or", "ci"))

plot <- g_forest(tbl = result)
plot

Experimental use!

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Code

df <- extract_rsp_subgroups(
  variables = list(
    rsp = "is_rsp",
    arm = "ARM",
    subgroups = c("SEX", "BMRKR2"),
    strata = "STRATA2"
  ),
  data = anl_rsp_arms_ab,
  conf_level = 0.90
)

result <- basic_table() %>%
  tabulate_rsp_subgroups(df, vars = c("n_tot", "or", "ci"))

plot <- g_forest(tbl = result)
plot

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.

Preview
Try this using WebR

Code

df <- extract_rsp_subgroups(
  variables = list(
    rsp = "is_rsp",
    arm = "ARM",
    subgroups = c("SEX", "BMRKR2"),
    strata = "STRATA2"
  ),
  data = anl_rsp_arms_ab,
  conf_level = 0.95
)

result <- basic_table() %>%
  tabulate_rsp_subgroups(df, vars = c("n_tot", "n", "n_rsp", "prop", "or", "ci"))

plot <- g_forest(
  tbl = result,
  col_symbol_size = NULL
)
plot

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.

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Code

anl_cr_arms_ab <- anl %>%
  mutate(is_rsp = AVALC == "CR") %>%
  filter(ARMCD %in% c("ARM B", "ARM A")) %>%
  mutate(
    ARMCD = relevel(ARMCD, ref = "ARM B") %>%
      droplevels(),
    ARM = relevel(ARM, ref = "B: Placebo") %>%
      droplevels()
  ) %>%
  droplevels()
var_labels(anl_cr_arms_ab) <- c(anl_labels, is_rsp = "Is CR")

df <- extract_rsp_subgroups(
  variables = list(
    rsp = "is_rsp",
    arm = "ARM",
    subgroups = c("SEX", "BMRKR2"),
    strata = "STRATA2"
  ),
  data = anl_cr_arms_ab,
  conf_level = 0.95
)

result <- basic_table() %>%
  tabulate_rsp_subgroups(df, vars = c("n_tot", "n", "n_rsp", "prop", "or", "ci"))

plot <- g_forest(tbl = result)
plot

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)
library(nestcolor)

adsl_f <- random.cdisc.data::cadsl %>%
  select(STUDYID, USUBJID, ARMCD, ARM, SEX, BMRKR2, STRATA1, STRATA2)

adrs_f <- random.cdisc.data::cadrs %>%
  filter(PARAMCD == "INVET") %>%
  select(STUDYID, USUBJID, PARAMCD, AVISIT, AVALC)

anl <- inner_join(adsl_f, adrs_f, by = c("STUDYID", "USUBJID"))
anl <- df_explicit_na(anl)
anl_labels <- var_labels(anl)

anl_rsp_arms_ab <- anl %>%
  mutate(is_rsp = AVALC %in% c("CR", "PR")) %>%
  filter(ARMCD %in% c("ARM B", "ARM A")) %>%
  mutate(
    ARMCD = relevel(ARMCD, ref = "ARM B") %>%
      droplevels(),
    ARM = relevel(ARM, ref = "B: Placebo") %>%
      droplevels()
  ) %>%
  droplevels()
var_labels(anl_rsp_arms_ab) <- c(anl_labels, is_rsp = "Is Responder")

Code

library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- random.cdisc.data::cadsl
  ADRS <- random.cdisc.data::cadrs
})
datanames <- c("ADSL", "ADRS")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

arm_ref_comp <- list(
  ARM = list(
    ref = "B: Placebo",
    comp = c("A: Drug X", "C: Combination")
  ),
  ARMCD = list(
    ref = "ARM B",
    comp = c("ARM A", "ARM C")
  )
)

## Reusable Configuration For Modules
ADSL <- data[["ADSL"]]
ADRS <- data[["ADRS"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_g_forest_rsp(
      label = "Forest Response",
      dataname = "ADRS",
      arm_var = choices_selected(
        variable_choices(ADSL, c("ARM", "ARMCD")),
        "ARMCD"
      ),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        value_choices(ADRS, "PARAMCD", "PARAM"),
        "BESRSPI"
      ),
      subgroup_var = choices_selected(
        variable_choices(ADSL, names(ADSL)),
        c("BMRKR2", "SEX")
      ),
      strata_var = choices_selected(
        variable_choices(ADSL, c("STRATA1", "STRATA2")),
        "STRATA2"
      ),
      plot_height = c(600L, 200L, 2000L),
      plot_width = c(1100L, 200L, 2000L)
    )
  )
)

shinyApp(app$ui, app$server)

Experimental use!

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#| standalone: true
#| viewerHeight: 800
#| editorHeight: 200
#| components: [viewer, editor]
#| layout: vertical

# -- WEBR HELPERS --
options(webr_pkg_repos = c("r-universe" = "https://insightsengineering.r-universe.dev", getOption("webr_pkg_repos")))

# -- APP CODE --
library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  ADSL <- random.cdisc.data::cadsl
  ADRS <- random.cdisc.data::cadrs
})
datanames <- c("ADSL", "ADRS")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

arm_ref_comp <- list(
  ARM = list(
    ref = "B: Placebo",
    comp = c("A: Drug X", "C: Combination")
  ),
  ARMCD = list(
    ref = "ARM B",
    comp = c("ARM A", "ARM C")
  )
)

## Reusable Configuration For Modules
ADSL <- data[["ADSL"]]
ADRS <- data[["ADRS"]]

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_g_forest_rsp(
      label = "Forest Response",
      dataname = "ADRS",
      arm_var = choices_selected(
        variable_choices(ADSL, c("ARM", "ARMCD")),
        "ARMCD"
      ),
      arm_ref_comp = arm_ref_comp,
      paramcd = choices_selected(
        value_choices(ADRS, "PARAMCD", "PARAM"),
        "BESRSPI"
      ),
      subgroup_var = choices_selected(
        variable_choices(ADSL, names(ADSL)),
        c("BMRKR2", "SEX")
      ),
      strata_var = choices_selected(
        variable_choices(ADSL, c("STRATA1", "STRATA2")),
        "STRATA2"
      ),
      plot_height = c(600L, 200L, 2000L),
      plot_width = c(1100L, 200L, 2000L)
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-02 17:48:08 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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 brio                    1.1.5    2024-04-24 [1] CRAN (R 4.4.1)
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 logger                  0.4.0    2024-10-22 [1] CRAN (R 4.4.1)
 magrittr              * 2.0.3    2022-03-30 [1] CRAN (R 4.4.1)
 MASS                    7.3-61   2024-06-13 [2] CRAN (R 4.4.1)
 Matrix                  1.7-0    2024-04-26 [1] CRAN (R 4.4.1)
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 promises                1.3.0    2024-04-05 [1] CRAN (R 4.4.1)
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 R6                      2.5.1    2021-08-19 [1] CRAN (R 4.4.1)
 ragg                    1.3.3    2024-09-11 [1] CRAN (R 4.4.1)
 random.cdisc.data       0.3.16   2024-11-02 [1] https://insightsengineering.r-universe.dev (R 4.4.1)
 rbibutils               2.3      2024-10-04 [1] CRAN (R 4.4.1)
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 rlang                   1.1.4    2024-06-04 [1] CRAN (R 4.4.1)
 rmarkdown               2.28     2024-08-17 [1] CRAN (R 4.4.1)
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 teal.logger             0.3.0    2024-11-02 [1] https://insightsengineering.r-universe.dev (R 4.4.1)
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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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`teal` App

`.lock` file