---
title: "Step 2. Obtain the sequence ratios"
output: rmarkdown::html_vignette
vignette: >
  %\VignetteIndexEntry{a03_Summarise_sequence_ratios}
  %\VignetteEngine{knitr::rmarkdown}
  %\VignetteEncoding{UTF-8}
---

```{r, include = FALSE}
knitr::opts_chunk$set(
  warning = FALSE,
  message = FALSE,
  collapse = TRUE,
  comment = "#>",
  fig.width = 7,
  fig.height = 5,
  eval = Sys.getenv("$RUNNER_OS") != "macOS"
)
```

```{r, include = FALSE}
if (Sys.getenv("EUNOMIA_DATA_FOLDER") == "") Sys.setenv("EUNOMIA_DATA_FOLDER" = tempdir())
if (!dir.exists(Sys.getenv("EUNOMIA_DATA_FOLDER"))) dir.create(Sys.getenv("EUNOMIA_DATA_FOLDER"))
if (!CDMConnector::eunomia_is_available()) CDMConnector::downloadEunomiaData()
```

# Introduction
In this vignette we will explore the functionality and arguments of `summariseSequenceRatios()` function, which is used to generate the sequence ratios of the SSA. As this function uses the output of `generateSequenceCohortSet()` function (explained in detail in the vignette: **Step 1. Generate a sequence cohort**), we will pick up the explanation from where we left off in the previous vignette. 

```{r message= FALSE, warning=FALSE, include=FALSE}
# Load libraries
library(CDMConnector)
library(dplyr)
library(DBI)
library(CohortSymmetry)
library(duckdb)
library(DrugUtilisation)

# Connect to the database
db <- DBI::dbConnect(duckdb::duckdb(), 
                     dbdir = CDMConnector::eunomia_dir())
cdm <- cdm_from_con(
  con = db,
  cdm_schema = "main",
  write_schema = "main"
)

cdm <- DrugUtilisation::generateIngredientCohortSet(
  cdm = cdm,
  name = "aspirin",
  ingredient = "aspirin")

cdm <- DrugUtilisation::generateIngredientCohortSet(
  cdm = cdm,
  name = "acetaminophen",
  ingredient = "acetaminophen")
```

Recall that in the previous vignette: Step 1. Generate a sequence cohort, we've generated `cdm$aspirin` and `cdm$acetaminophen` before and using them we could generate `cdm$intersect` like so:

```{r message= FALSE, warning=FALSE}
# Generate a sequence cohort
cdm <- generateSequenceCohortSet(
  cdm = cdm,
  indexTable = "aspirin",
  markerTable = "acetaminophen",
  name = "intersect",
  combinationWindow = c(0,Inf))
```


# Obtain sequence ratios
One can obtain the crude and adjusted sequence ratios (with its corresponding confidence intervals) using `summariseSequenceRatios()` function:
```{r message = FALSE, warning = FALSE}
summariseSequenceRatios(
  cohort = cdm$intersect
) |> 
  dplyr::glimpse()
```

The obtained output has a summarised result format. In the later vignette (**Step 3. Visualise results**) we will explore how to visualise the results in a more intuitive way. 

## Modify the cohort based on `cohort_definition_id`

This parameter is used to subset the cohort table inputted to the `summariseSequenceRatios()`. Imagine the user only wants to include `cohort_definition_id` $= 1$ from `cdm$intersect` in the `summariseSequenceRatios()`, then one could do the following:

```{r message= FALSE, warning=FALSE}
summariseSequenceRatios(cohort = cdm$intersect,
                          cohortId = 1) |> 
  dplyr::glimpse()
```

Of course in this case this does nothing because every entry in `cdm$intersect` has `cohort_definition_id` $= 1$.

## Modify `confidenceInterval`
By default, the `summariseSequenceRatios()` function will use 95% (two-sided) confidence interval. If another confidence interval is desired, for example 99% confidence interval, one can use the `confidenceInterval` argument:
```{r message = FALSE, warning = FALSE}
summariseSequenceRatios(
  cohort = cdm$intersect,
  confidenceInterval = 99) |> 
  dplyr::glimpse()
```

## Modify `movingAverageRestriction`
The idea of moving average restriction is necessary only for the null sequence ratio calculation, please refer to Lai et al. (2017) for more details on this parameter (parameter d when calculating P in page 578). Following Tsiropoulos et al. (2009), by default, the argument `movingAverageRestriction` is set to be  $548$ ($18$ months). 

## Modify `minCellCount`

By default, the minimum number of events to reported is 5, below which results will be obscured. If 0, all results will be reported and the user could do this via: 

```{r message= FALSE, warning=FALSE}
summariseSequenceRatios(cohort = cdm$intersect,
                        minCellCount = 0) |> 
  dplyr::glimpse()
```

```{r message= FALSE, warning=FALSE, eval=FALSE}
CDMConnector::cdmDisconnect(cdm = cdm)
```