Step 2. Obtain the sequence ratios"

In CohortSymmetry: Sequence Symmetry Analysis Using the Observational Medical Outcomes Partnership Common Data Model

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

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::eunomiaIsAvailable())
  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.

# 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::eunomiaDir())
cdm <- cdmFromCon(
  con = db,
  cdmSchema = "main",
  writeSchema = "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:

# 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:

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.

CDMConnector::cdmDisconnect(cdm = cdm)

CohortSymmetry documentation built on April 3, 2025, 5:26 p.m.