Biomarker Analysis Catalog - Dev
  • Dev
    • Stable
  1. Graphs
  2. KG
  3. KG3
  • Index

  • Tables
    • CPMT
      • CPMT1
      • CPMT2
        • CPMT2A
      • CPMT3
    • DT
      • DT1
        • DT1A
        • DT1B
        • DT1C
      • DT2
        • DT2A
    • TET
      • TET1
        • TET1A

  • Graphs
    • AG
      • AG1
    • DG
      • DG1
        • DG1A
        • DG1B
      • DG2
      • DG3
        • DG3A
      • DG4
    • KG
      • KG1
        • KG1A
        • KG1B
      • KG2
        • KG2A
      • KG3
      • KG4
        • KG4A
        • KG4B
      • KG5
        • KG5A
        • KG5B
    • RFG
      • RFG1
        • RFG1A
      • RFG2
        • RFG2A
        • RFG2B
        • RFG2C
      • RFG3
    • RG
      • RG1
        • RG1A
        • RG1B
        • RG1C
      • RG2
        • RG2A
      • RG3
        • RG3A
        • RG3B
    • SPG
      • SPG1
      • SPG2
    • RNAG
      • RNAG1
      • RNAG2
      • RNAG3
      • RNAG4
      • RNAG5
      • RNAG6
      • RNAG7
      • RNAG8
      • RNAG9
      • RNAG10
    • SFG
      • SFG1
        • SFG1A
        • SFG1B
      • SFG2
        • SFG2A
        • SFG2B
        • SFG2C
        • SFG2D
      • SFG3
        • SFG3A
      • SFG4
      • SFG5
        • SFG5A
        • SFG5B
        • SFG5C
      • SFG6
        • SFG6A
        • SFG6B
        • SFG6C
  1. Graphs
  2. KG
  3. KG3

KG3

Kaplan-Meier Graphs by Biomarker Subgroups

KG

  • Setup
  • Plot
  • Session Info

The same data set as in KG1A is used. The difference is that here we use the categorical biomarker variable BMRKR2 as the treatment arm in variables which is then used by g_km() below.

Code
library(tern)
library(dplyr)

adtte_arm_bep <- random.cdisc.data::cadtte %>%
  df_explicit_na() %>%
  filter(PARAMCD == "OS", ARM == "A: Drug X", BEP01FL == "Y") %>%
  mutate(is_event = CNSR == 0)

variables <- list(tte = "AVAL", is_event = "is_event", arm = "BMRKR2")

We can produce the basic plot using the g_km() function from tern.

Code
g_km(
  df = adtte_arm_bep,
  variables = variables,
  annot_surv_med = FALSE
)

We can also choose to annotate the plot with the median survival time for each of the biomarker subgroups using the annot_surv_med = TRUE option.

Code
g_km(
  df = adtte_arm_bep,
  variables = variables,
  annot_surv_med = TRUE
)

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_0.9.5.9022       rtables_0.6.9.9014   
[4] magrittr_2.0.3        formatters_0.5.9.9001

loaded via a namespace (and not attached):
 [1] Matrix_1.7-0                  gtable_0.3.5                 
 [3] jsonlite_1.8.8                compiler_4.4.1               
 [5] tidyselect_1.2.1              stringr_1.5.1                
 [7] tidyr_1.3.1                   splines_4.4.1                
 [9] scales_1.3.0                  yaml_2.3.10                  
[11] fastmap_1.2.0                 lattice_0.22-6               
[13] ggplot2_3.5.1                 R6_2.5.1                     
[15] labeling_0.4.3                generics_0.1.3               
[17] knitr_1.48                    forcats_1.0.0                
[19] rbibutils_2.2.16              htmlwidgets_1.6.4            
[21] backports_1.5.0               checkmate_2.3.2              
[23] tibble_3.2.1                  munsell_0.5.1                
[25] pillar_1.9.0                  rlang_1.1.4                  
[27] utf8_1.2.4                    broom_1.0.6                  
[29] stringi_1.8.4                 xfun_0.47                    
[31] cli_3.6.3                     withr_3.0.1                  
[33] Rdpack_2.6.1                  digest_0.6.37                
[35] grid_4.4.1                    cowplot_1.1.3                
[37] lifecycle_1.0.4               vctrs_0.6.5                  
[39] evaluate_0.24.0               glue_1.7.0                   
[41] farver_2.1.2                  codetools_0.2-20             
[43] survival_3.7-0                random.cdisc.data_0.3.15.9009
[45] fansi_1.0.6                   colorspace_2.1-1             
[47] purrr_1.0.2                   rmarkdown_2.28               
[49] tools_4.4.1                   pkgconfig_2.0.3              
[51] htmltools_0.5.8.1            

Reuse

Copyright 2023, Hoffmann-La Roche Ltd.
KG2A
KG4
Source Code
---
title: KG3
subtitle: Kaplan-Meier Graphs by Biomarker Subgroups
categories: [KG]
---

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

::: panel-tabset
## Setup

The same data set as in [KG1A](../graphs/KG1/kg01a.qmd) is used.
The difference is that here we use the categorical biomarker variable `BMRKR2` as the treatment arm in `variables` which is then used by `g_km()` below.

```{r, message = FALSE}
library(tern)
library(dplyr)

adtte_arm_bep <- random.cdisc.data::cadtte %>%
  df_explicit_na() %>%
  filter(PARAMCD == "OS", ARM == "A: Drug X", BEP01FL == "Y") %>%
  mutate(is_event = CNSR == 0)

variables <- list(tte = "AVAL", is_event = "is_event", arm = "BMRKR2")
```

## Plot

We can produce the basic plot using the `g_km()` function from `tern`.

```{r, fig.width=9, fig.height=6}
g_km(
  df = adtte_arm_bep,
  variables = variables,
  annot_surv_med = FALSE
)
```

We can also choose to annotate the plot with the median survival time for each of the biomarker subgroups using the `annot_surv_med = TRUE` option.

```{r, fig.width=9, fig.height=6}
g_km(
  df = adtte_arm_bep,
  variables = variables,
  annot_surv_med = TRUE
)
```

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

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