runCharacterizationAnalyses: execute a large-scale characterization study

In Characterization: Implement Descriptive Studies Using the Common Data Model

execute a large-scale characterization study

Description

Specify the database connection containing the CDM data, the cohort database schemas/tables, the characterization settings and the directory to save the results to

Usage

runCharacterizationAnalyses(
  connectionDetails,
  targetDatabaseSchema,
  targetTable,
  outcomeDatabaseSchema,
  outcomeTable,
  tempEmulationSchema = getOption("sqlRenderTempEmulationSchema"),
  cdmDatabaseSchema,
  characterizationSettings,
  outputDirectory,
  executionPath = file.path(outputDirectory, "execution"),
  csvFilePrefix = "c_",
  databaseId = "1",
  showSubjectId = FALSE,
  minCellCount = 0,
  incremental = TRUE,
  threads = 1,
  minCharacterizationMean = 0.01
)

Arguments

connectionDetails	The connection details to the database containing the OMOP CDM data
targetDatabaseSchema	Schema name where your target cohort table resides. Note that for SQL Server, this should include both the database and schema name, for example 'scratch.dbo'.
targetTable	Name of the target cohort table.
outcomeDatabaseSchema	Schema name where your outcome cohort table resides. Note that for SQL Server, this should include both the database and schema name, for example 'scratch.dbo'.
outcomeTable	Name of the outcome cohort table.
tempEmulationSchema	Some database platforms like Oracle and Impala do not truly support temp tables. To emulate temp tables, provide a schema with write privileges where temp tables can be created
cdmDatabaseSchema	The schema with the OMOP CDM data
characterizationSettings	The study settings created using createCharacterizationSettings
outputDirectory	The location to save the final csv files to
executionPath	The location where intermediate results are saved to
csvFilePrefix	A string to append the csv files in the outputDirectory
databaseId	The unique identifier for the cdm database
showSubjectId	Whether to include subjectId of failed rechallenge case series or hide
minCellCount	The minimum count value that is calculated
incremental	If TRUE then skip previously executed analyses that completed
threads	The number of threads to use when running aggregate covariates
minCharacterizationMean	The minimum mean threshold to extract when running aggregate covariates

Details

The results of the characterization will be saved into an sqlite database inside the specified saveDirectory

Value

Multiple csv files in the outputDirectory.

See Also

Other LargeScale: createCharacterizationSettings(), loadCharacterizationSettings(), saveCharacterizationSettings()

Examples

conDet <- exampleOmopConnectionDetails()

drSet <- createDechallengeRechallengeSettings(
  targetIds = c(1,2),
  outcomeIds = 3
)

cSet <- createCharacterizationSettings(
  dechallengeRechallengeSettings = drSet
)

runCharacterizationAnalyses(
  connectionDetails = conDet,
  targetDatabaseSchema = 'main',
  targetTable = 'cohort',
  outcomeDatabaseSchema = 'main',
  outcomeTable = 'cohort',
  cdmDatabaseSchema = 'main',
  characterizationSettings = cSet,
  outputDirectory = tempdir()
)

Characterization documentation built on April 4, 2025, 2:02 a.m.