cpmt01.qmd

---
title: CPMT1
subtitle: Cox Proportional Hazards Model Tables with a Single Covariate
categories: [CPM]
---

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

::: panel-tabset
## Setup

We will use the `cadtte` data set from the `random.cdisc.data` package to illustrate the tables.
We start by selecting the biomarker evaluable population (BEP) and the time-to-event endpoint of interest, here overall survival.
We then convert time of overall survival to months and create a new variable for the event information.

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

adtte <- random.cdisc.data::cadtte %>%
  df_explicit_na() %>%
  filter(PARAMCD == "OS", BEP01FL == "Y") %>%
  mutate(
    AVAL = day2month(AVAL),
    AVALU = "Months",
    event = 1 - CNSR
  )
```

## Table

The `summarize_coxreg()` function in `tern` takes the same `variables`, `at`, and `control` arguments that are used to fit a model using the `fit_coxreg_univar()` function - specifying the time, event and covariate in a `variables` list, and `control` and `at` with any further customizations to the model.
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.

```{r}
lyt <- basic_table() %>%
  summarize_coxreg(variables = list(
    time = "AVAL",
    event = "event",
    covariates = "COUNTRY"
  ))

build_table(lyt, adtte)
```

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