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

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

## Setting up your Analysis

There are some assumptions which must be met to avoid unintended errors when using SEQuential. These are: 

1. User provided `time.col` begins at 0 per unique `id.col` entries, we also assume that the column contains only integers and continues by 1 for every time step. e.g. (0, 1, 2, 3, …) is allowed and (0, 1, 2, 2.5, …) or (0, 1, 2, 4, 5, …) are not.
2. Provided `time.col` entries may be out of order as a sort is enforced at the beginning of the function, e.g. (0, 2, 1, 4, 3, …) is valid because it begins at 0 and is continuously increasing by increments of 1, even though it is not ordered.
3. `eligible` and column names provided to `excused.cols` are binary (0/1) flag variables (with respect to `time.col`)

### Step 1 - Defining your options

In your R script, you will always start by defining your options object, through the `SEQopts()` helper. There are many defaults which allow you to target exactly how you would like to change your analysis. Through this wiki there are specific pages dedicated to each causal contrast and the parameters which affect them, but for simplicity let's start with an intention-to-treat analysis with 20 bootstrap samples.

```{r setup}
library(SEQTaRget)

options <- SEQopts(km.curves = TRUE, #asks the function to return survival and risk estimates
                   bootstrap = TRUE, #asks the model to preform bootstrapping
                   bootstrap.nboot = 20) #asks the model for 20 bootstrap samples
```

In general, options will be in the form `{option}.{parameter}` - here you may notice that we use `bootstrap.nboot` indicating that this parameter affects the `bootstrap`

### Step 2 - Running the Primary Function

The next step is running the primary R function, `SEQuential()`. Here you will give your options, data, and data-level information. We provide some small simulated datasets to test on.

```{r running}
data <- SEQdata
model <- SEQuential(data, id.col = "ID", 
                          time.col = "time", 
                          eligible.col = "eligible",
                          treatment.col = "tx_init",
                          outcome.col = "outcome",
                          time_varying.cols = c("N", "L", "P"),
                          fixed.cols = "sex",
                          method = "ITT", options = options)
```

We provide some print statements to help track where the `SEQuential()` function is processing at any given point in time.

### Step 3 - Recovering your results

`SEQuential()` produces a lot of internal diagnostics, models, and dataframes out of its main function in an S4 class. We provide a few different methods to handle obtaining your results.

```{r outcome, fig.alt="Survival curve by treatment group."}
outcome(model)     # Returns a list of only the outcome models 
km_curve(model)    # Prints the survival curve
risk_data(model)
risk_comparison(model)
```
