---
title: "Separate 2 groups in Cox regression"
author: "André Veríssimo"
date: "`r Sys.Date()`"
output: 
  BiocStyle::html_document:
    number_sections: yes
    toc: true
vignette: >
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteIndexEntry{Separate 2 groups in Cox regression}
  %\VignetteEncoding{UTF-8}
--- 

```{r setup, include=FALSE}
knitr::opts_chunk$set(echo = TRUE)

```

## Instalation

```{r, eval=FALSE}
if (!require("BiocManager"))
  install.packages("BiocManager")
BiocManager::install("glmSparseNet")
```

# Required Packages

```{r packages, message=FALSE, warning=FALSE}
library(futile.logger)
library(ggplot2)
library(glmSparseNet)
library(survival)

# Some general options for futile.logger the debugging package
.Last.value <- flog.layout(layout.format('[~l] ~m'))
.Last.value <- glmSparseNet:::show.message(FALSE)
# Setting ggplot2 default theme as minimal
theme_set(ggplot2::theme_minimal())
```

## Prepare data

```{r}
data('cancer', package = 'survival')
xdata <- survival::ovarian[,c('age', 'resid.ds')]
ydata <- data.frame(
  time = survival::ovarian$futime,
  status = survival::ovarian$fustat
)
```

## Separate using age as co-variate 
_(group cutoff is median calculated relative risk)_

```{r}
res.age <- separate2GroupsCox(c(age = 1, 0), xdata, ydata)
```

### Kaplan-Meier survival results

```{r, echo=FALSE}
res.age$km
```

### Plot

A individual is attributed to low-risk group if its calculated 
relative risk _(using Cox Proportional model)_ is below or equal
the median risk.

The opposite for the high-risk groups, populated with individuals 
above the median relative-risk.

```{r, echo=FALSE}
res.age$plot
```

## Separate using age as co-variate _(group cutoff is 40% - 60%)_

```{r}
res.age.40.60 <- 
  separate2GroupsCox(c(age = 1, 0), 
                     xdata, 
                     ydata, 
                     probs = c(.4, .6)
  )
```

### Kaplan-Meier survival results

```{r, echo=FALSE}
res.age.40.60$km
```

### Plot

A individual is attributed to low-risk group if its calculated
relative risk _(using Cox Proportional model)_ is below the median risk.

The opposite for the high-risk groups, populated with individuals above 
the median relative-risk.

```{r, echo=FALSE}
res.age.40.60$plot
```

## Separate using age as co-variate _(group cutoff is 60% - 40%)_

This is a special case where you want to use a cutoff that includes
some sample on both high and low risks groups.

```{r}
res.age.60.40 <- separate2GroupsCox(
  chosen.btas = c(age = 1, 0), 
  xdata, 
  ydata, 
  probs = c(.6, .4),
  stop.when.overlap = FALSE
)
```

### Kaplan-Meier survival results

```{r, echo=FALSE}
cat('Kaplan-Meier results','\n')
res.age.60.40$km
```

### Plot

A individual is attributed to low-risk group if its calculated 
relative risk _(using Cox Proportional model)_ is below the median risk.

The opposite for the high-risk groups, populated with individuals above 
the median relative-risk.

```{r, echo=FALSE}
res.age.60.40$plot
```


# Session Info

```{r sessionInfo}
sessionInfo()
```