runCharacterizationAnalyses: 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,
  outputDatabaseSchema = targetDatabaseSchema,
  outputTable = "characterization_cohort",
  tempEmulationSchema = getOption("sqlRenderTempEmulationSchema"),
  cdmDatabaseSchema,
  characterizationSettings,
  outputDirectory,
  executionPath = file.path(outputDirectory, "execution"),
  csvFilePrefix = "c_",
  databaseId = "1",
  showSubjectId = FALSE,
  minCellCount = 0,
  incremental = TRUE,
  threads = 1,
  cohortGenerationThreads = NULL,
  nTargetJobs = 1,
  minCharacterizationMean = 0.01,
  minCovariateCount = 0,
  mode = "CohortIncidence",
  minSMD = 0
)

Value

Multiple csv files in the outputDirectory.

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.
outputDatabaseSchema: The schema where the characterization cohort table will be saved into
outputTable: The table name where the characterization cohort table will be saved into
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 analyses jobs in parallel
cohortGenerationThreads: (optional) The number of threads to use when generating the cohorts in parallel (Note: some database management systems do not allow insert parallelization)
nTargetJobs: Partition the targets into this number of groups (e.g., if there are 20 targets and njobs is 5 then there will be 4 targets per job and 5 jobs)
minCharacterizationMean: The minimum mean threshold to extract when running aggregate covariates
minCovariateCount: The minimum number of patients who must have the covariate when running aggregate covariates
mode: Select from Efficient (no exclusions to target based on washout)/CohortIncidence (excludes targets with outcome in washout if they have no time at risk)/PatientLevelPrediction (excludes targets with outcome during washout prior to index)
minSMD: The minimum standardized mean difference for the risk factor analysis

Details

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

Examples

Run this code


conDet <- exampleOmopConnectionDetails()

tteSet <- createTimeToEventSettings(
  targetIds = c(1,2),
  outcomeIds = 3
)

cSet <- createCharacterizationSettings(
  timeToEventSettings = tteSet
)

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