Step 4: Obtain aggregated data on temporal symmetry"

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 summariseTemporalSymmetry() function. This function uses cdm$intersect introduced in the previous vignette Step 1. Generate a sequence cohort to produce aggregated statistics containing the frequency for different time gaps between the initiation of the marker and the initiation of the index (marker_date $-$ index_date). The work of this function is best illustrated via an example.

# 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"
)

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

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

Recall that in the previous vignette, we've used cdm$aspirin and cdm$acetaminophen to generate cdm$intersect like so:

# Generate a sequence cohort
cdm <- generateSequenceCohortSet(
  cdm = cdm,
  indexTable = "aspirin",
  markerTable = "acetaminophen",
  name = "intersect",
  combinationWindow = c(0,Inf))

Obtaining temporal symmetry

summariseTemporalSymmetry(cohort = cdm$intersect) |> 
  dplyr::glimpse()

The default unit of the difference of two initiations is measured in months. In this example, the first row is showing there are $6$ cases of index happening after marker with the gap being $29$ months whereas the second row is showing there are $7$ cases of index happening before marker with the gap being $40$ months.

Modify the cohort based on cohort_definition_id

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

summariseTemporalSymmetry(cohort = cdm$intersect,
                          cohortId = 1) |> 
  dplyr::glimpse()

Of course and once again this does nothing because every entry in cdm$intersect has cohort_definition_id $= 1$.

Modify timescale

Recall the default for the timescale is month, one could also change this to either day or year.

summariseTemporalSymmetry(cohort = cdm$intersect,
                          timescale = "day") |> 
  dplyr::glimpse()

summariseTemporalSymmetry(cohort = cdm$intersect,
                          timescale = "year") |> 
  dplyr::glimpse()

CDMConnector::cdmDisconnect(cdm = cdm)

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