Biomarker Analysis Catalog - Stable
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  4. SFG2B
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  1. Graphs
  2. SFG
  3. SFG2
  4. SFG2B

SFG2B

Survival Forest Graph for Overall Population and by Continuous Biomarker with “Above and Below Percentage” Cutoffs Biomarker

SFG

  • Setup
  • Plot
  • Session Info

We prepare the data similarly as in SFG1. In particular we use again the cut_quantile_bins() function, here to obtain quartile bins of the continuous biomarker BMRKR1.

Code
library(tern)
library(dplyr)

BMRKR1_probs <- c(0.25, 0.5, 0.75)

adtte <- random.cdisc.data::cadtte %>%
  df_explicit_na() %>%
  filter(
    PARAMCD == "OS"
  ) %>%
  mutate(
    AVAL = day2month(AVAL),
    AVALU = "Months",
    is_event = CNSR == 0,
    ARM_BIN = fct_collapse_only(
      ARM,
      CTRL = c("B: Placebo"),
      TRT = c("A: Drug X", "C: Combination")
    ),
    BMRKR1 = ifelse(BEP01FL == "N", NA, BMRKR1),
    BMRKR1_BIN = explicit_na(cut_quantile_bins(BMRKR1, BMRKR1_probs)),
    BMRKR2 = fct_explicit_na_if(BMRKR2, BEP01FL == "N")
  ) %>%
  var_relabel(
    BEP01FL = "BEP",
    BMRKR2 = "Biomarker (Categorical)",
    BMRKR1_BIN = "Biomarker (Binned)"
  )

In this template we are looking for each percentage cutoff at above vs. below subgroups: So we just provide yet another groups_lists specification for the BMRKR1_BIN binned variable.

Code
BMRKR1_BIN_levels <- levels(adtte$BMRKR1_BIN)

tbl <- extract_survival_subgroups(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    arm = "ARM_BIN",
    subgroups = c("BEP01FL", "BMRKR1_BIN")
  ),
  label_all = "ITT",
  groups_lists = list(
    BMRKR1_BIN = list(
      "[0%, 25%]" = BMRKR1_BIN_levels[1],
      "(25%, 100%]" = BMRKR1_BIN_levels[2:4],
      "[0%, 50%]" = BMRKR1_BIN_levels[1:2],
      "(50%, 100%]" = BMRKR1_BIN_levels[3:4],
      "[0%, 75%]" = BMRKR1_BIN_levels[1:3],
      "(75%, 100%]" = BMRKR1_BIN_levels[4]
    )
  ),
  data = adtte
)

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

We can now produce the forest plot using the g_forest() function.

Code
g_forest(result)

Code
sessionInfo()
R version 4.4.2 (2024-10-31)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 24.04.1 LTS

Matrix products: default
BLAS:   /usr/lib/x86_64-linux-gnu/openblas-pthread/libblas.so.3 
LAPACK: /usr/lib/x86_64-linux-gnu/openblas-pthread/libopenblasp-r0.3.26.so;  LAPACK version 3.12.0

locale:
 [1] LC_CTYPE=en_US.UTF-8       LC_NUMERIC=C              
 [3] LC_TIME=en_US.UTF-8        LC_COLLATE=en_US.UTF-8    
 [5] LC_MONETARY=en_US.UTF-8    LC_MESSAGES=en_US.UTF-8   
 [7] LC_PAPER=en_US.UTF-8       LC_NAME=C                 
 [9] LC_ADDRESS=C               LC_TELEPHONE=C            
[11] LC_MEASUREMENT=en_US.UTF-8 LC_IDENTIFICATION=C       

time zone: Etc/UTC
tzcode source: system (glibc)

attached base packages:
[1] stats     graphics  grDevices utils     datasets  methods   base     

other attached packages:
[1] dplyr_1.1.4       tern_0.9.7        rtables_0.6.11    magrittr_2.0.3   
[5] formatters_0.5.10

loaded via a namespace (and not attached):
 [1] Matrix_1.7-2             gtable_0.3.6             jsonlite_1.9.0          
 [4] compiler_4.4.2           tidyselect_1.2.1         stringr_1.5.1           
 [7] tidyr_1.3.1              splines_4.4.2            scales_1.3.0            
[10] yaml_2.3.10              fastmap_1.2.0            lattice_0.22-6          
[13] ggplot2_3.5.1            R6_2.6.1                 labeling_0.4.3          
[16] generics_0.1.3           knitr_1.49               forcats_1.0.0           
[19] rbibutils_2.3            htmlwidgets_1.6.4        backports_1.5.0         
[22] checkmate_2.3.2          tibble_3.2.1             munsell_0.5.1           
[25] pillar_1.10.1            rlang_1.1.5              broom_1.0.7             
[28] stringi_1.8.4            xfun_0.51                cli_3.6.4               
[31] withr_3.0.2              Rdpack_2.6.2             digest_0.6.37           
[34] grid_4.4.2               cowplot_1.1.3            lifecycle_1.0.4         
[37] vctrs_0.6.5              evaluate_1.0.3           glue_1.8.0              
[40] farver_2.1.2             nestcolor_0.1.3          codetools_0.2-20        
[43] survival_3.8-3           random.cdisc.data_0.3.16 colorspace_2.1-1        
[46] rmarkdown_2.29           purrr_1.0.4              tools_4.4.2             
[49] pkgconfig_2.0.3          htmltools_0.5.8.1       

Reuse

Copyright 2023, Hoffmann-La Roche Ltd.
SFG2A
SFG2C
Source Code
---
title: SFG2B
subtitle: Survival Forest Graph for Overall Population and by Continuous Biomarker with "Above and Below Percentage" Cutoffs Biomarker
categories: [SFG]
---

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

::: panel-tabset
{{< include setup.qmd >}}

## Plot

In this template we are looking for each percentage cutoff at above vs. below subgroups: So we just provide yet another `groups_lists` specification for the `BMRKR1_BIN` binned variable.

```{r}
BMRKR1_BIN_levels <- levels(adtte$BMRKR1_BIN)

tbl <- extract_survival_subgroups(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    arm = "ARM_BIN",
    subgroups = c("BEP01FL", "BMRKR1_BIN")
  ),
  label_all = "ITT",
  groups_lists = list(
    BMRKR1_BIN = list(
      "[0%, 25%]" = BMRKR1_BIN_levels[1],
      "(25%, 100%]" = BMRKR1_BIN_levels[2:4],
      "[0%, 50%]" = BMRKR1_BIN_levels[1:2],
      "(50%, 100%]" = BMRKR1_BIN_levels[3:4],
      "[0%, 75%]" = BMRKR1_BIN_levels[1:3],
      "(75%, 100%]" = BMRKR1_BIN_levels[4]
    )
  ),
  data = adtte
)

result <- basic_table() %>%
  tabulate_survival_subgroups(
    df = tbl,
    vars = c("n_tot_events", "n", "n_events", "median", "hr", "ci"),
    time_unit = adtte$AVALU[1]
  )
```

We can now produce the forest plot using the `g_forest()` function.

```{r, fig.width = 15}
g_forest(result)
```

{{< include ../../misc/session_info.qmd >}}
:::

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