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

KG1

Kaplan-Meier Graphs for One Treatment Arm

KG

  • Setup
  • Plot
  • Session Info

We will use the cadtte data set from the random.cdisc.data package to create the Kaplan-Meier (KM) plots. We start by filtering the time-to-event dataset for the overall survival observations and by one treatment arm (A), creating a new variable for event information, and curating a list of variables required to produce the plot.

Code
library(tern)
library(dplyr)
library(ggplot2)
library(grid)

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

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

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

Code
g_km(
  df = adtte_arm,
  variables = variables,
  annot_surv_med = FALSE,
  rel_height_plot = 0.85
)

We can also choose to annotate the graph with the median survival time for the overall population using the annot_surv_med = TRUE option.

Code
g_km(
  df = adtte_arm,
  variables = variables,
  annot_surv_med = TRUE,
  rel_height_plot = 0.85
)

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] grid      stats     graphics  grDevices utils     datasets  methods  
[8] base     

other attached packages:
[1] ggplot2_3.5.1         dplyr_1.1.4           tern_0.9.5.9022      
[4] rtables_0.6.9.9014    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] R6_2.5.1                      labeling_0.4.3               
[15] generics_0.1.3                knitr_1.48                   
[17] forcats_1.0.0                 rbibutils_2.2.16             
[19] htmlwidgets_1.6.4             backports_1.5.0              
[21] checkmate_2.3.2               tibble_3.2.1                 
[23] munsell_0.5.1                 pillar_1.9.0                 
[25] rlang_1.1.4                   utf8_1.2.4                   
[27] broom_1.0.6                   stringi_1.8.4                
[29] xfun_0.47                     cli_3.6.3                    
[31] withr_3.0.1                   Rdpack_2.6.1                 
[33] digest_0.6.37                 cowplot_1.1.3                
[35] lifecycle_1.0.4               vctrs_0.6.5                  
[37] evaluate_0.24.0               glue_1.7.0                   
[39] farver_2.1.2                  codetools_0.2-20             
[41] survival_3.7-0                random.cdisc.data_0.3.15.9009
[43] fansi_1.0.6                   colorspace_2.1-1             
[45] purrr_1.0.2                   rmarkdown_2.28               
[47] tools_4.4.1                   pkgconfig_2.0.3              
[49] htmltools_0.5.8.1            

Reuse

Copyright 2023, Hoffmann-La Roche Ltd.
DG4
KG1A
Source Code
---
title: KG1
subtitle: Kaplan-Meier Graphs for One Treatment Arm
categories: [KG]
---

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

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

## Plot

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

```{r, fig.width=9, fig.height=6}
g_km(
  df = adtte_arm,
  variables = variables,
  annot_surv_med = FALSE,
  rel_height_plot = 0.85
)
```

We can also choose to annotate the graph with the median survival time for the overall population using the `annot_surv_med = TRUE` option.

```{r, fig.width=9, fig.height=6}
g_km(
  df = adtte_arm,
  variables = variables,
  annot_surv_med = TRUE,
  rel_height_plot = 0.85
)
```

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

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