---
title: "Introduction to the R package rsurv"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{Introduction to the R package rsurv}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  collapse = TRUE,
  comment = "#>"
)
```

## Example

In the code below we show how to simulate a sample of type I right-censored survival data, assuming that the failure times are generated from an accelerated failure time model with loglogistic baseline distribution. In addition, assume that we wish to consider two
exploratory variables, say age and sex, and we want to include an interaction effect between them. Such a task can be easily accomplished by using the function `raftreg()` along with the function `qllogis()` available in the package __flexsurv__.

```{r example, message = FALSE}
library(rsurv)
library(dplyr)
library(survstan)
library(flexsurv)

set.seed(1234567890)

n <-  1000
tau <- 10  # maximum follow up time
simdata <- data.frame(
  age = rnorm(n),
  sex = sample(c("f", "m"), size = n, replace = TRUE)
) %>%
  mutate(
    t = raftreg(runif(n), ~ age*sex, beta = c(1, 2, -0.5), 
                dist = "llogis", shape = 1.5, scale = 1),
  ) %>%
  rowwise() %>%
  mutate(
    time = min(t, tau),
    status = as.numeric(time == t)
  ) 

glimpse(simdata)

fit <- aftreg(
  Surv(time, status) ~ age*sex,
  data = simdata, dist = "loglogistic"
)
estimates(fit)

```
