FSTG02

Subgroup Analysis of Survival Duration

---

Output

Standard Plot

Preview

anl1 <- anl

df <- extract_survival_subgroups(
  variables = list(tte = "AVAL", is_event = "is_event", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
  data = anl1
)

result <- basic_table() %>%
  tabulate_survival_subgroups(
    df = df,
    vars = c("n_tot", "n", "median", "hr", "ci"),
    time_unit = anl1$AVALU[1]
  )
result

                                               B: Placebo               A: Drug X                                    
Baseline Risk Factors           Total n    n    Median (Months)    n    Median (Months)   Hazard Ratio   95% Wald CI 
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
All Patients                      268     134         NA          134         NA              1.00       (0.69, 1.44)
Sex                                                                                                                  
  F                               161     82          NA          79          NA              0.79       (0.49, 1.28)
  M                               107     52          NA          55          9.6             1.39       (0.78, 2.47)
Categorical Level Biomarker 2                                                                                        
  LOW                             95      45          NA          50          9.3             1.14       (0.64, 2.02)
  MEDIUM                          93      56          NA          37          NA              0.97       (0.52, 1.82)
  HIGH                            80      33          NA          47          NA              0.97       (0.45, 2.12)

# Add plot.
plot <- g_forest(tbl = result)
plot

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Experimental use!

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Plot Specifying Class Variables and Options for the Treatment Variable

Preview

anl2 <- anl %>%
  mutate(
    # Recode levels of arm.
    ARM = forcats::fct_recode(
      ARM,
      "Placebo" = "B: Placebo",
      "Drug X" = "A: Drug X"
    ),
    # Reorder levels of `SEX`.
    SEX = forcats::fct_relevel(SEX, "M", "F"),
    # Reorder levels of `STRATA1`` by frequency.
    STRATA1 = forcats::fct_infreq(STRATA1)
  )

df <- extract_survival_subgroups(
  variables = list(tte = "AVAL", is_event = "is_event", arm = "ARM", subgroups = c("SEX", "STRATA1")),
  data = anl2
)

result <- basic_table() %>%
  tabulate_survival_subgroups(
    df = df,
    vars = c("n_tot", "n", "median", "hr", "ci"),
    time_unit = anl2$AVALU[1]
  )
result

                                           Placebo                 Drug X                                      
Baseline Risk Factors     Total n    n    Median (Months)    n    Median (Months)   Hazard Ratio   95% Wald CI 
———————————————————————————————————————————————————————————————————————————————————————————————————————————————
All Patients                268     134         NA          134         NA              1.00       (0.69, 1.44)
Sex                                                                                                            
  M                         107     52          NA          55          9.6             1.39       (0.78, 2.47)
  F                         161     82          NA          79          NA              0.79       (0.49, 1.28)
Stratification Factor 1                                                                                        
  C                         94      45          NA          49          NA              0.75       (0.41, 1.38)
  B                         92      45          NA          47          NA              1.34       (0.71, 2.54)
  A                         82      44          NA          38          NA              1.02       (0.53, 1.97)

plot <- g_forest(tbl = result)
plot

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Plot Selecting Columns and Changing the Alpha Level

Preview

anl3 <- anl

df <- extract_survival_subgroups(
  variables = list(tte = "AVAL", is_event = "is_event", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
  control = control_coxph(conf_level = 0.9),
  data = anl3
)

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

                                                                     
Baseline Risk Factors           Total n   Hazard Ratio   90% Wald CI 
—————————————————————————————————————————————————————————————————————
All Patients                      268         1.00       (0.74, 1.36)
Sex                                                                  
  F                               161         0.79       (0.53, 1.19)
  M                               107         1.39       (0.86, 2.25)
Categorical Level Biomarker 2                                        
  LOW                             95          1.14       (0.71, 1.84)
  MEDIUM                          93          0.97       (0.58, 1.64)
  HIGH                            80          0.97       (0.51, 1.87)

# Add plot.
plot <- g_forest(tbl = result)
plot

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Experimental use!

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Plot with Fixed Symbol Size

Preview

anl4 <- anl

df <- extract_survival_subgroups(
  variables = list(tte = "AVAL", is_event = "is_event", arm = "ARM", subgroups = c("SEX", "BMRKR2")),
  data = anl4
)

result <- basic_table() %>%
  tabulate_survival_subgroups(
    df = df,
    vars = c("n_tot", "n", "median", "hr", "ci"),
    time_unit = anl4$AVALU[1]
  )
result

                                               B: Placebo               A: Drug X                                    
Baseline Risk Factors           Total n    n    Median (Months)    n    Median (Months)   Hazard Ratio   95% Wald CI 
—————————————————————————————————————————————————————————————————————————————————————————————————————————————————————
All Patients                      268     134         NA          134         NA              1.00       (0.69, 1.44)
Sex                                                                                                                  
  F                               161     82          NA          79          NA              0.79       (0.49, 1.28)
  M                               107     52          NA          55          9.6             1.39       (0.78, 2.47)
Categorical Level Biomarker 2                                                                                        
  LOW                             95      45          NA          50          9.3             1.14       (0.64, 2.02)
  MEDIUM                          93      56          NA          37          NA              0.97       (0.52, 1.82)
  HIGH                            80      33          NA          47          NA              0.97       (0.45, 2.12)

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

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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(tern)
library(dplyr)
library(forcats)
library(nestcolor)

preprocess_adtte <- function(adtte) {
  # Save variable labels before data processing steps.
  adtte_labels <- var_labels(adtte)

  adtte <- adtte %>%
    df_explicit_na() %>%
    dplyr::filter(
      PARAMCD == "OS",
      ARM %in% c("B: Placebo", "A: Drug X"),
      SEX %in% c("M", "F")
    ) %>%
    dplyr::mutate(
      # Reorder levels of ARM to display reference arm before treatment arm.
      ARM = droplevels(forcats::fct_relevel(ARM, "B: Placebo")),
      SEX = droplevels(SEX),
      is_event = CNSR == 0,
      # Convert time to MONTH
      AVAL = day2month(AVAL),
      AVALU = "Months"
    ) %>%
    var_relabel(
      ARM = adtte_labels["ARM"],
      SEX = adtte_labels["SEX"],
      is_event = "Event Flag",
      AVAL = adtte_labels["AVAL"],
      AVALU = adtte_labels["AVALU"]
    )

  adtte
}

anl <- random.cdisc.data::cadtte %>%
  preprocess_adtte()

teal App

Preview

library(teal.modules.clinical)

## Data reproducible code
data <- teal_data()
data <- within(data, {
  library(dplyr)
  library(forcats)

  ADSL <- random.cdisc.data::cadsl
  ADSL <- ADSL %>%
    filter(ARM %in% c("B: Placebo", "A: Drug X")) %>%
    mutate(ARM = droplevels(fct_relevel(ARM, "B: Placebo"))) %>%
    mutate(ARMCD = droplevels(fct_relevel(ARMCD, "ARM B")))
  ADSL$RACE <- droplevels(ADSL$RACE)

  ADTTE <- random.cdisc.data::cadtte
  adtte_labels <- col_labels(ADTTE)

  ADTTE <- ADTTE %>%
    filter(
      PARAMCD == "OS",
      ARM %in% c("B: Placebo", "A: Drug X"),
      SEX %in% c("M", "F")
    ) %>%
    mutate(
      # Reorder levels of ARM to display reference arm before treatment arm.
      ARM = droplevels(fct_relevel(ARM, "B: Placebo")),
      SEX = droplevels(SEX),
      is_event = CNSR == 0,
      # Convert time to MONTH
      AVAL = day2month(AVAL),
      AVALU = "Months"
    ) %>%
    col_relabel(
      ARM = adtte_labels["ARM"],
      SEX = adtte_labels["SEX"],
      is_event = "Event Flag",
      AVAL = adtte_labels["AVAL"],
      AVALU = adtte_labels["AVALU"]
    )
})
datanames <- c("ADSL", "ADTTE")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_g_forest_tte(
      label = "Forest Survival",
      dataname = "ADTTE",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      subgroup_var = choices_selected(names(ADSL), c("SEX", "BMRKR2")),
      paramcd = choices_selected(value_choices(ADTTE, "PARAMCD", "PARAM"), "OS"),
      strata_var = choices_selected(c("STRATA1", "STRATA2"), "STRATA2"),
      plot_height = c(600, 200, 2000),
      plot_width = c(1500, 200, 5000)
    )
  )
)

shinyApp(app$ui, app$server)

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Experimental use!

shinylive allow you to modify to run shiny application entirely in the web browser. Modify the code below and click re-run the app to see the results. The performance is slighly worse and some of the features (e.g. downloading) might not work at all.

#| '!! shinylive warning !!': |
#|   shinylive does not work in self-contained HTML documents.
#|   Please set `embed-resources: false` in your metadata.
#| 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, {
  library(dplyr)
  library(forcats)

  ADSL <- random.cdisc.data::cadsl
  ADSL <- ADSL %>%
    filter(ARM %in% c("B: Placebo", "A: Drug X")) %>%
    mutate(ARM = droplevels(fct_relevel(ARM, "B: Placebo"))) %>%
    mutate(ARMCD = droplevels(fct_relevel(ARMCD, "ARM B")))
  ADSL$RACE <- droplevels(ADSL$RACE)

  ADTTE <- random.cdisc.data::cadtte
  adtte_labels <- col_labels(ADTTE)

  ADTTE <- ADTTE %>%
    filter(
      PARAMCD == "OS",
      ARM %in% c("B: Placebo", "A: Drug X"),
      SEX %in% c("M", "F")
    ) %>%
    mutate(
      # Reorder levels of ARM to display reference arm before treatment arm.
      ARM = droplevels(fct_relevel(ARM, "B: Placebo")),
      SEX = droplevels(SEX),
      is_event = CNSR == 0,
      # Convert time to MONTH
      AVAL = day2month(AVAL),
      AVALU = "Months"
    ) %>%
    col_relabel(
      ARM = adtte_labels["ARM"],
      SEX = adtte_labels["SEX"],
      is_event = "Event Flag",
      AVAL = adtte_labels["AVAL"],
      AVALU = adtte_labels["AVALU"]
    )
})
datanames <- c("ADSL", "ADTTE")
datanames(data) <- datanames
join_keys(data) <- default_cdisc_join_keys[datanames]

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

## Setup App
app <- init(
  data = data,
  modules = modules(
    tm_g_forest_tte(
      label = "Forest Survival",
      dataname = "ADTTE",
      arm_var = choices_selected(c("ARM", "ARMCD"), "ARM"),
      subgroup_var = choices_selected(names(ADSL), c("SEX", "BMRKR2")),
      paramcd = choices_selected(value_choices(ADTTE, "PARAMCD", "PARAM"), "OS"),
      strata_var = choices_selected(c("STRATA1", "STRATA2"), "STRATA2"),
      plot_height = c(600, 200, 2000),
      plot_width = c(1500, 200, 5000)
    )
  )
)

shinyApp(app$ui, app$server)

Reproducibility

Timestamp

[1] "2024-11-20 09:16:32 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 ───────────────────────────────────────────────────────────────────
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 [1] /usr/local/lib/R/site-library
 [2] /usr/local/lib/R/library

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