CPMT2

Cox Proportional Hazards Model Tables with Multiple Covariates

CPM

We prepare the data similarly as in CPMT1.

Code

library(tern)
library(dplyr)

adtte <- random.cdisc.data::cadtte %>%
  df_explicit_na() %>%
  filter(PARAMCD == "OS", BEP01FL == "Y") %>%
  mutate(
    AVAL = day2month(AVAL),
    AVALU = "Months",
    is_event = CNSR == 0
  ) %>%
  var_relabel(
    BMRKR1 = "Biomarker (Continuous)",
    BMRKR2 = "Biomarker (Categorical)"
  )

The multivariate Cox Proportional Hazards model can be displayed in a summary table using the summarize_coxreg function from tern with the multivar argument set to TRUE. Like the corresponding model fitting function fit_coxreg_multivar(), we specify the time, event, arm and covariates in a variables list, and any further customizations via the control argument. Note that the default confidence level is 95% but this can be customized via the conf_level element in control.

Rather than fitting the model and then tidying the output via the broom::tidy() function, we can directly input these three arguments into the summarize_coxreg() function to summarize the model fit in a table layout, building the table with our pre-processed adtte data set.

Code

result <- basic_table() %>%
  summarize_coxreg(
    variables = list(
      time = "AVAL",
      event = "is_event",
      arm = "ARM",
      covariates = c("AGE", "BMRKR1", "BMRKR2")
    ),
    multivar = TRUE
  ) %>%
  append_topleft("Effect/Covariate Included in the Model") %>%
  build_table(adtte)

result

Effect/Covariate Included in the Model                 Hazard Ratio      95% CI      p-value
————————————————————————————————————————————————————————————————————————————————————————————
Treatment:                                                                                  
  Description of Planned Arm (reference = A: Drug X)                                 0.0164 
    B: Placebo                                             1.15       (0.66, 1.99)   0.6217 
    C: Combination                                         1.95       (1.19, 3.21)   0.0080 
Covariate:                                                                                  
  Age                                                                                       
    All                                                    1.01       (0.98, 1.04)   0.4993 
  Biomarker (Continuous)                                                                    
    All                                                    1.01       (0.96, 1.07)   0.6755 
  Biomarker (Categorical) (reference = LOW)                                          0.7350 
    MEDIUM                                                 1.01       (0.62, 1.67)   0.9558 
    HIGH                                                   0.84       (0.50, 1.40)   0.5054

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                 generics_0.1.3          
[16] Formula_1.2-5            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             car_3.1-3               
[25] munsell_0.5.1            pillar_1.10.1            rlang_1.1.5             
[28] broom_1.0.7              stringi_1.8.4            xfun_0.51               
[31] cli_3.6.4                Rdpack_2.6.2             digest_0.6.37           
[34] grid_4.4.2               lifecycle_1.0.4          vctrs_0.6.5             
[37] evaluate_1.0.3           glue_1.8.0               nestcolor_0.1.3         
[40] codetools_0.2-20         abind_1.4-8              survival_3.8-3          
[43] carData_3.0-5            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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