SFG2C

Survival Forest Graph for Overall Population and by Percentiles of Continuous Biomarker Within Percentile Cutoff Bins

SFG

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)"
  )

Here we use the biomarker variable BMRKR1_BIN with its original percentage bins to tabulate the statistics to be able to use as an input for the forest plot.

Code

tbl <- extract_survival_subgroups(
  variables = list(
    tte = "AVAL",
    is_event = "is_event",
    arm = "ARM_BIN",
    subgroups = c("BEP01FL", "BMRKR1_BIN")
  ),
  label_all = "ITT",
  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 remove the last line with the <Missing> level.

Code

result <- result[-10, , keep_topleft = TRUE]

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

Code

g_forest(result)

Code

sessionInfo()

R version 4.4.1 (2024-06-14)
Platform: x86_64-pc-linux-gnu
Running under: Ubuntu 22.04.4 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-r0.3.20.so;  LAPACK version 3.10.0

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_0.9.5       rtables_0.6.9    magrittr_2.0.3  
[5] formatters_0.5.9

loaded via a namespace (and not attached):
 [1] Matrix_1.7-0             gtable_0.3.5             jsonlite_1.8.8          
 [4] compiler_4.4.1           tidyselect_1.2.1         stringr_1.5.1           
 [7] tidyr_1.3.1              splines_4.4.1            scales_1.3.0            
[10] yaml_2.3.10              fastmap_1.2.0            lattice_0.22-6          
[13] ggplot2_3.5.1            R6_2.5.1                 labeling_0.4.3          
[16] generics_0.1.3           knitr_1.48               forcats_1.0.0           
[19] rbibutils_2.2.16         htmlwidgets_1.6.4        backports_1.5.0         
[22] checkmate_2.3.2          tibble_3.2.1             munsell_0.5.1           
[25] pillar_1.9.0             rlang_1.1.4              utf8_1.2.4              
[28] broom_1.0.6              stringi_1.8.4            xfun_0.47               
[31] cli_3.6.3                withr_3.0.1              Rdpack_2.6.1            
[34] digest_0.6.37            grid_4.4.1               cowplot_1.1.3           
[37] lifecycle_1.0.4          vctrs_0.6.5              evaluate_0.24.0         
[40] glue_1.7.0               farver_2.1.2             codetools_0.2-20        
[43] survival_3.7-0           random.cdisc.data_0.3.15 fansi_1.0.6             
[46] colorspace_2.1-1         purrr_1.0.2              rmarkdown_2.28          
[49] tools_4.4.1              pkgconfig_2.0.3          htmltools_0.5.8.1

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