SFG5A

Survival Forest Graph Comparing Categorical Biomarker Groups on Subgroups

SFG

We prepare the data similarly as in SFG1.

Code

library(tern)
library(dplyr)

AGE_probs <- 0.5

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 = "B: Placebo",
      TRT = c("A: Drug X", "C: Combination")
    ),
    AGE_BIN = cut_quantile_bins(AGE, probs = AGE_probs),
    BMRKR2 = fct_explicit_na_if(BMRKR2, BEP01FL == "N"),
    BMRKR2_BIN = fct_collapse_only(
      BMRKR2,
      High = "HIGH",
      Low = c("MEDIUM", "LOW")
    )
  ) %>%
  var_relabel(
    BEP01FL = "BEP",
    BMRKR2_BIN = "Biomarker (Binary)"
  )

The main difference here is that we ensure that col_info is aligned across all subtables before using rbind() to combine subtables. (Generally this is worth trying out if the rbind() does not work out of the box to combine subtables.)

Code

tables_all <- function(filter_var, filter_condition, sub_var) {
  dataset <- adtte %>%
    filter(!!as.name(filter_var) == filter_condition)
  if (nrow(dataset) == 0) {
    stop(paste("Subset", filter_var, "==", filter_condition, "is empty"))
  }
  tbl <- extract_survival_subgroups(
    variables = list(
      tte = "AVAL",
      is_event = "is_event",
      arm = "ARM_BIN",
      subgroups = sub_var
    ),
    label_all = "ITT",
    data = dataset
  )
  basic_table() %>%
    tabulate_survival_subgroups(
      df = tbl,
      vars = c("n_tot_events", "n", "n_events", "median", "hr", "ci"),
      time_unit = dataset$AVALU[1]
    )
}

add_subtitle <- function(sub_tab, sub_title) {
  label_at_path(sub_tab, path = row_paths(sub_tab)[[1]][1]) <- sub_title
  sub_tab
}

tables_list <- list(
  tables_all(filter_var = "BMRKR2_BIN", filter_condition = "High", sub_var = "SEX"),
  tables_all(filter_var = "BMRKR2_BIN", filter_condition = "Low", sub_var = "SEX"),
  tables_all(filter_var = "BMRKR2_BIN", filter_condition = "High", sub_var = "AGE_BIN"),
  tables_all(filter_var = "BMRKR2_BIN", filter_condition = "Low", sub_var = "AGE_BIN")
)
col_info(tables_list[[2]]) <- col_info(tables_list[[1]])
result <- rbind(
  add_subtitle(tables_list[[1]], "BMRKR2_BIN = High"),
  add_subtitle(tables_list[[2]], "BMRKR2_BIN = Low"),
  add_subtitle(tables_list[[3]], "BMRKR2_BIN = High"),
  add_subtitle(tables_list[[4]], "BMRKR2_BIN = Low")
)

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

Code

one_table <- tables_list[[1]]
g_forest(
  result,
  col_x = attr(one_table, "col_x"),
  col_ci = attr(one_table, "col_ci"),
  forest_header = attr(one_table, "forest_header"),
  col_symbol_size = attr(one_table, "col_symbol_size")
)

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

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