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execute a large-scale characterization study

Source: R/RunCharacterization.R
runCharacterizationAnalyses.Rd

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
)

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

Value

Multiple csv files in the outputDirectory.

Details

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

See also

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

Examples


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')
)
#> Creating directory /tmp/Rtmpgkb1Yb/runChar
#> Creating directory /tmp/Rtmpgkb1Yb/runChar/execution
#> Connecting using SQLite driver
#> Extracting cohort jobs
#> Connecting using SQLite driver
#> Creating new cluster
#> Connecting using SQLite driver
#> Uploading #cohort_settings
#> Inserting data took 0.00712 secs
#> Computing time to event results
#> Executing SQL took 0.0213 secs
#> Computing time-to-event for 2 T-O pairs took 0.206 secs
#> exporting to andromeda
#> Disconnected Andromeda. This data object can no longer be used
#> Removing any existing results in outputFolder directory
#> Loading andromeda result at /tmp/Rtmpgkb1Yb/runChar/execution/tte_1