Time-to-Event Summary Tables
We will use the cadtte data set from the random.cdisc.data package for the time-to-event summary table. We start by filtering the adtte data set for the overall survival observations, converting time of overall survival to months, creating new variables for event and non-event information and binarizing the ARM variable.
library(tern)
library(dplyr)
adtte <- random.cdisc.data::cadtte %>%
df_explicit_na() %>%
filter(
PARAMCD == "OS"
) %>%
mutate(
AVAL = day2month(AVAL),
AVALU = "Months",
is_event = CNSR == 0,
is_not_event = CNSR == 1,
ARM_BIN = fct_collapse_only(
ARM,
CTRL = c("B: Placebo"),
TRT = c("A: Drug X", "C: Combination")
)
)This time-to-event summary table splits the columns by treatment arm using split_cols_by(), creates a summary for patients with and without event using summarize_vars(), summarizes survival time using surv_time() and summarizes the analysis from unstratified Cox Proportional Hazards models using coxph_pairwise().
lyt <- basic_table() %>%
split_cols_by(
var = "ARM_BIN",
ref_group = "CTRL"
) %>%
add_colcounts() %>%
analyze_vars(
vars = "is_event",
.stats = "count_fraction",
.labels = c(count_fraction = "Patients with event (%)"),
nested = FALSE,
show_labels = "hidden"
) %>%
analyze_vars(
vars = "is_not_event",
.stats = "count_fraction",
.labels = c(count_fraction = "Patients without event (%)"),
nested = FALSE,
show_labels = "hidden"
) %>%
surv_time(
vars = "AVAL",
var_labels = "Time to Event (months)",
is_event = "is_event",
table_names = "time_to_event"
) %>%
coxph_pairwise(
vars = "AVAL",
is_event = "is_event",
var_labels = c("Unstratified Analysis"),
control = control_coxph(pval_method = "log-rank"),
table_names = "coxph_unstratified"
)
build_table(lyt, adtte) CTRL TRT
(N=134) (N=266)
——————————————————————————————————————————————————————————————
Patients with event (%) 58 (43.3%) 127 (47.7%)
Patients without event (%) 76 (56.7%) 139 (52.3%)
Time to Event (months)
Median NA NA
95% CI (9.4, NA) (9.2, NA)
25% and 75%-ile 5.6, NA 5.4, NA
Range 0.9 to 16.3 {1} 0.5 to 16.4 {1}
Unstratified Analysis
p-value (log-rank) 0.4068
Hazard Ratio 1.14
95% CI (0.84, 1.56)
——————————————————————————————————————————————————————————————
{1} - Censored observation: range maximum
——————————————————————————————————————————————————————————————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] 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] Rdpack_2.6.1 digest_0.6.37
[33] grid_4.4.1 lifecycle_1.0.4
[35] vctrs_0.6.5 evaluate_0.24.0
[37] glue_1.7.0 codetools_0.2-20
[39] survival_3.7-0 random.cdisc.data_0.3.15.9009
[41] fansi_1.0.6 colorspace_2.1-1
[43] purrr_1.0.2 rmarkdown_2.28
[45] tools_4.4.1 pkgconfig_2.0.3
[47] htmltools_0.5.8.1 ---
title: TET1
subtitle: Time-to-Event Summary Tables
categories: [TET]
---
------------------------------------------------------------------------
::: panel-tabset
{{< include setup.qmd >}}
## Table
This time-to-event summary table splits the columns by treatment arm using `split_cols_by()`, creates a summary for patients with and without event using `summarize_vars()`, summarizes survival time using `surv_time()` and summarizes the analysis from unstratified Cox Proportional Hazards models using `coxph_pairwise()`.
```{r}
lyt <- basic_table() %>%
split_cols_by(
var = "ARM_BIN",
ref_group = "CTRL"
) %>%
add_colcounts() %>%
analyze_vars(
vars = "is_event",
.stats = "count_fraction",
.labels = c(count_fraction = "Patients with event (%)"),
nested = FALSE,
show_labels = "hidden"
) %>%
analyze_vars(
vars = "is_not_event",
.stats = "count_fraction",
.labels = c(count_fraction = "Patients without event (%)"),
nested = FALSE,
show_labels = "hidden"
) %>%
surv_time(
vars = "AVAL",
var_labels = "Time to Event (months)",
is_event = "is_event",
table_names = "time_to_event"
) %>%
coxph_pairwise(
vars = "AVAL",
is_event = "is_event",
var_labels = c("Unstratified Analysis"),
control = control_coxph(pval_method = "log-rank"),
table_names = "coxph_unstratified"
)
build_table(lyt, adtte)
```
{{< include ../../misc/session_info.qmd >}}
:::