11-plots-examples-survival.Rmd

### Survival Plot 
#### Kaplan-Meier statistics and plotting

In the following example the _survfit_ function from the _survival_ package is used to calculate Kaplan-Meier statistics from time-to-event data. The model statistics can be inspected (using _broom_ - a generic function to extract statistics from R models). 

Passing the model to the _ggsurvplot_ function from the _survminer_ package creates a Kaplan-Meier curve. The configuration tries to mimic the known SAS output as close as possible (e.g. number at risk table style etc.).

#### Packages and Sample Data
```{r, message = FALSE, warning = FALSE, error = FALSE}
# Packages
library(survminer)
library(survival)
library(broom)
library(flextable)

# Data
adtte <- haven::read_xpt(
  paste0("https://github.com/phuse-org/TestDataFactory/",
         "raw/main/Updated/TDF_ADaM/adtte.xpt"))
``` 

Filter time-to-event parameter and select required variables. The piping command directly passed filtered and selected data to the `survfit` function and creates the model. 

>**Note:** The event parameter from survival function (survival::Surv) is the status   indicator, normally 0=alive, 1=death; while ADTTE.CNSR=0 usually means event  occurred(e.g., death), ADTTE.CNSR=1 represents censoring. Thus, “1-CNSR” is used here to  accommodate the CDISC ADaM standard.

::: {.attention}
This section is under construction
:::
```{r}
surv_model <- adtte %>%
  filter(PARAMCD == "TTDE") %>%
  select(STUDYID, USUBJID, PARAMCD, AVAL, CNSR, TRTA) %>%
  survfit(Surv(AVAL, 1-CNSR) ~ TRTA, data = .)
```

#### Inspecting fitted survival model

```{r, eval = F, echo = T}
head(tidy(surv_model))  
```

```{r, echo = F, eval = T}
head(tidy(surv_model)) %>%
 kableExtra::kable(align = 'c')
```
#### Plotting custom configured Kaplan-Meier curves without confidence intervals

>**Note:** Let´s assume a month is defined as 30.4375 (days); 
The xscale parameter d_m converts days (input) to month.
If six months breaks are required for the numbers-at-risk table this would mean: 
break.x.by = 182.625 (6*30.4375).

```{r, fig.width=15, fig.height=10, out.width="680px"}
 ggsurvplot(
   fit = surv_model,
   data = adtte,
   risk.table = TRUE,
   #ylab = ylabs,
   xlab = "Months",
   linetype = "strata",
   conf.int = F,
   legend.title = "Randomized Treatment",
   legend = c(0.1, 0.1),
   #palette = c(color_trt1,color_trt2),
   risk.table.title = "Number of subjects at risk",
   risk.table.y.text = F,
   risk.table.height = .15,
   censor.shape = 1,
   censor.size = 3,
   ncensor.plot = F,
   xlim = c(0,250),
   xscale = "d_m",
   break.x.by = 30.4375,
   break.y.by = .1,
   ggtheme = theme_survminer(
     font.main = c(10, "bold"),
     font.submain = c(10, "bold"),
     font.x = c(12, "bold"),
     font.y = c(12, "bold"),
   ) + theme(panel.border = element_rect(fill = NA)),
   tables.theme = theme_cleantable()
 )
```

#### Plotting custom configured Kaplan-Meier curves with confidence intervals

```{r, fig.width=15, fig.height=10, out.width="680px"}
 ggsurvplot(
   fit = surv_model,
   data = adtte,
   risk.table = TRUE,
   #ylab = ylabs,
   xlab = "Months",
   linetype = "strata",
   conf.int = T,
   legend.title = "Randomized Treatment",
   legend = c(0.1, 0.1),
   #palette = c(color_trt1,color_trt2),
   risk.table.title = "Number of subjects at risk",
   risk.table.y.text = F,
   risk.table.height = .15,
   censor.shape = 1,
   censor.size = 3,
   ncensor.plot = F,
   #xlim = c(0,250),
   xscale = "d_m",
   break.x.by = 30.4375,
   break.y.by = .1,
   ggtheme = theme_survminer(
     font.main = c(10, "bold"),
     font.submain = c(10, "bold"),
     font.x = c(12, "bold"),
     font.y = c(12, "bold"),
   ) + theme(panel.border = element_rect(fill = NA)),
   tables.theme = theme_cleantable()
 )
```