Characterization is part of HADES.

Introduction

Characterization is an R package for performing characterization of a target and a comparator cohort.

Features

Compute time to event
Compute dechallenge and rechallenge
Computer characterization of target cohort with and without occurring in an outcome cohort during some time at risk
Run multiple characterization analyses efficiently
upload results to database
export results as csv files

Examples

library(Characterization)
connectionDetails <- Characterization::exampleOmopConnectionDetails()
targetIds <- c(1,2,4)
  outcomeIds <- c(3)
  timeToEventSettings <- createTimeToEventSettings(
    studyPopulationSettings = createStudyPopulationSettings(
     targetIds = c(1,2),
     limitToFirstInNDays = 0,
     minPriorObservation = 0
     ),
    outcomeIds = c(3,4)
  )
  dechallengeRechallengeSettings <- createDechallengeRechallengeSettings(
    studyPopulationSettings = createStudyPopulationSettings(
     targetIds = c(1,2),
     limitToFirstInNDays = 0,
     minPriorObservation = 0
     ),
    outcomeIds = outcomeIds,
    dechallengeStopInterval = 30,
    dechallengeEvaluationWindow = 31
  )
  riskFactorSettings1 <- createRiskFactorSettings(
    studyPopulationSettings = createStudyPopulationSettings(
      targetIds = targetIds,
      limitToFirstInNDays = 99999, # first exposure
      minPriorObservation = 365 # requiring 365 days prior obs
     ),
    outcomeIds = outcomeIds,
    riskWindowStart = 1,
    startAnchor = 'cohort start',
    riskWindowEnd = 365,
    endAnchor = 'cohort start',
    covariateSettings = FeatureExtraction::createCovariateSettings(
      useDemographicsGender = TRUE,
      useDemographicsAge = TRUE,
      useDemographicsRace = TRUE
    )
  )
  riskFactorSettings2 <- createRiskFactorSettings(
    studyPopulationSettings = createStudyPopulationSettings(
      targetIds = targetIds,
      limitToFirstInNDays = 99999, # first exposure
      minPriorObservation = 365 # requiring 365 days prior obs
     ),
    outcomeIds = outcomeIds,
    riskWindowStart = 1,
    startAnchor = 'cohort start',
    riskWindowEnd = 365,
    endAnchor = 'cohort start',
    covariateSettings = FeatureExtraction::createCovariateSettings(
      useConditionOccurrenceLongTerm = TRUE
    )
  )
  characterizationSettings <- createCharacterizationSettings(
    timeToEventSettings = list(
      timeToEventSettings
      ),
    dechallengeRechallengeSettings = list(
      dechallengeRechallengeSettings
    ),
    riskFactorSettings =  list(
      riskFactorSettings1,
      riskFactorSettings2
      )
  )
runCharacterizationAnalyses(
  connectionDetails = connectionDetails,
  cdmDatabaseSchema = 'main',
  targetDatabaseSchema = 'main',
  targetTable = 'cohort',
  outcomeDatabaseSchema = 'main',
  outcomeTable = 'cohort',
  outputDatabaseSchema = 'main',
  outputTable = 'char_cohort',
  characterizationSettings = characterizationSettings,
  outputDirectory = file.path(tempdir(), 'example', 'results'),
  executionPath = file.path(tempdir(), 'example', 'execution'),
  csvFilePrefix = 'c_',
  databaseId = 'Eunomia',
  minSMD = 0.1
)

Technology

Characterization is an R package.

System Requirements

Requires R (version 4.0.0 or higher). Libraries used in Characterization require Java.

Installation

See the instructions here for configuring your R environment, including Java.
In R, use the following commands to download and install Characterization:

# CRAN
install.packages('Characterization')
# GitHub
install.packages("remotes")
remotes::install_github("ohdsi/Characterization")

User Documentation

Documentation can be found on the package website.

Support

Developer questions/comments/feedback: OHDSI Forum
We use the GitHub issue tracker for all bugs/issues/enhancements

Contributing

Read here how you can contribute to this package.

License

Characterization is licensed under Apache License 2.0

Development

Characterization is being developed in R Studio.