Characterization: Implement Descriptive Studies Using the Common Data Model
Description
An end-to-end framework that enables users to implement various descriptive studies for a given set of target and outcome cohorts for data mapped to the Observational Medical Outcomes Partnership Common Data Model.
Author(s)
Maintainer: Jenna Reps jreps@its.jnj.com
Authors:
Patrick Ryan ryan@ohdsi.org
Chris Knoll knoll@ohdsi.org
See Also
Useful links:
Report bugs at https://github.com/OHDSI/Characterization/issues
Removes csv files from folders that have not been marked as completed and removes the record of the execution file
Description
Removes csv files from folders that have not been marked as completed and removes the record of the execution file
Usage
cleanIncremental(executionFolder, ignoreWhenEmpty = FALSE)
Arguments
executionFolder
The folder that has the execution files
ignoreWhenEmpty
When TRUE, if there are no incremental logs then nothing is run
Value
A list with the settings
See Also
Other Incremental:
cleanNonIncremental()
Examples
cleanIncremental(
file.path(tempdir(), 'incremental'),
ignoreWhenEmpty = TRUE
)
Removes csv files from the execution folder as there should be no csv files when running in non-incremental model
Description
Removes csv files from the execution folder as there should be no csv files when running in non-incremental model
Usage
cleanNonIncremental(executionFolder)
Arguments
executionFolder
The folder that has the execution files
Value
A list with the settings
See Also
Other Incremental:
cleanIncremental()
Examples
# example code
cleanNonIncremental(file.path(tempdir(), 'incremental'))
Compute dechallenge rechallenge study
Description
Compute dechallenge rechallenge study
Usage
computeDechallengeRechallengeAnalyses(
connectionDetails = NULL,
targetDatabaseSchema,
targetTable,
outcomeDatabaseSchema = targetDatabaseSchema,
outcomeTable = targetTable,
tempEmulationSchema = getOption("sqlRenderTempEmulationSchema"),
settings,
databaseId = "database 1",
outputFolder,
minCellCount = 0,
progressBar = interactive(),
...
)
Arguments
connectionDetails
An object of type 'connectionDetails' as created using the [DatabaseConnector::createConnectionDetails()] function.
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
settings
The settings for the timeToEvent study
databaseId
An identifier for the database (string)
outputFolder
A directory to save the results as csv files
minCellCount
The minimum cell value to display, values less than this will be replaced by -1
progressBar
Whether to display a progress bar while the analysis is running
...
extra inputs
Value
An Andromeda::andromeda() object containing the dechallenge rechallenge results
See Also
Other DechallengeRechallenge:
computeRechallengeFailCaseSeriesAnalyses(),
createDechallengeRechallengeSettings()
Examples
conDet <- exampleOmopConnectionDetails()
drSet <- createDechallengeRechallengeSettings(
targetIds = c(1,2),
outcomeIds = 3
)
computeDechallengeRechallengeAnalyses(
connectionDetails = conDet,
targetDatabaseSchema = 'main',
targetTable = 'cohort',
settings = drSet,
outputFolder = tempdir()
)
Compute fine the subjects that fail the dechallenge rechallenge study
Description
Compute fine the subjects that fail the dechallenge rechallenge study
Usage
computeRechallengeFailCaseSeriesAnalyses(
connectionDetails = NULL,
targetDatabaseSchema,
targetTable,
outcomeDatabaseSchema = targetDatabaseSchema,
outcomeTable = targetTable,
tempEmulationSchema = getOption("sqlRenderTempEmulationSchema"),
settings,
databaseId = "database 1",
showSubjectId = FALSE,
outputFolder,
minCellCount = 0,
progressBar = interactive(),
...
)
Arguments
connectionDetails
An object of type 'connectionDetails' as created using the [DatabaseConnector::createConnectionDetails()] function.
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
settings
The settings for the timeToEvent study
databaseId
An identifier for the database (string)
showSubjectId
if F then subject_ids are hidden (recommended if sharing results)
outputFolder
A directory to save the results as csv files
minCellCount
The minimum cell value to display, values less than this will be replaced by -1
progressBar
Whether to display a progress bar while the analysis is running
...
extra inputs
Value
An Andromeda::andromeda() object with the case series details of the failed rechallenge
See Also
Other DechallengeRechallenge:
computeDechallengeRechallengeAnalyses(),
createDechallengeRechallengeSettings()
Examples
conDet <- exampleOmopConnectionDetails()
drSet <- createDechallengeRechallengeSettings(
targetIds = c(1,2),
outcomeIds = 3
)
computeRechallengeFailCaseSeriesAnalyses(
connectionDetails = conDet,
targetDatabaseSchema = 'main',
targetTable = 'cohort',
settings = drSet,
outputFolder = tempdir()
)
Compute time to event study
Description
Compute time to event study
Usage
computeTimeToEventAnalyses(
connectionDetails = NULL,
targetDatabaseSchema,
targetTable,
outcomeDatabaseSchema = targetDatabaseSchema,
outcomeTable = targetTable,
tempEmulationSchema = getOption("sqlRenderTempEmulationSchema"),
cdmDatabaseSchema,
settings,
databaseId = "database 1",
outputFolder,
minCellCount = 0,
progressBar = interactive(),
...
)
Arguments
connectionDetails
An object of type 'connectionDetails' as created using the [DatabaseConnector::createConnectionDetails()] function.
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 database schema containing the OMOP CDM data
settings
The settings for the timeToEvent study
databaseId
An identifier for the database (string)
outputFolder
A directory to save the results as csv files
minCellCount
The minimum cell value to display, values less than this will be replaced by -1
progressBar
Whether to display a progress bar while the analysis is running
...
extra inputs
Value
An Andromeda::andromeda() object containing the time to event results.
See Also
Other TimeToEvent:
createTimeToEventSettings()
Examples
# example code
conDet <- exampleOmopConnectionDetails()
tteSet <- createTimeToEventSettings(
targetIds = c(1,2),
outcomeIds = 3
)
result <- computeTimeToEventAnalyses(
connectionDetails = conDet,
targetDatabaseSchema = 'main',
targetTable = 'cohort',
cdmDatabaseSchema = 'main',
settings = tteSet,
outputFolder = file.path(tempdir(), 'tte')
)
Create aggregate covariate study settings
Description
Create aggregate covariate study settings
Usage
createAggregateCovariateSettings(
targetIds,
outcomeIds,
minPriorObservation = 0,
outcomeWashoutDays = 0,
riskWindowStart = 1,
startAnchor = "cohort start",
riskWindowEnd = 365,
endAnchor = "cohort start",
covariateSettings = FeatureExtraction::createCovariateSettings(useDemographicsGender =
TRUE, useDemographicsAge = TRUE, useDemographicsAgeGroup = TRUE, useDemographicsRace
= TRUE, useDemographicsEthnicity = TRUE, useDemographicsIndexYear = TRUE,
useDemographicsIndexMonth = TRUE, useDemographicsTimeInCohort = TRUE,
useDemographicsPriorObservationTime = TRUE, useDemographicsPostObservationTime =
TRUE, useConditionGroupEraLongTerm = TRUE, useDrugGroupEraOverlapping = TRUE,
useDrugGroupEraLongTerm = TRUE, useProcedureOccurrenceLongTerm = TRUE,
useMeasurementLongTerm = TRUE, useObservationLongTerm = TRUE,
useDeviceExposureLongTerm = TRUE, useVisitConceptCountLongTerm = TRUE,
useConditionGroupEraShortTerm = TRUE, useDrugGroupEraShortTerm = TRUE,
useProcedureOccurrenceShortTerm = TRUE, useMeasurementShortTerm = TRUE,
useObservationShortTerm = TRUE, useDeviceExposureShortTerm = TRUE,
useVisitConceptCountShortTerm = TRUE, endDays = 0, longTermStartDays = -365,
shortTermStartDays = -30),
caseCovariateSettings = createDuringCovariateSettings(useConditionGroupEraDuring =
TRUE, useDrugGroupEraDuring = TRUE, useProcedureOccurrenceDuring = TRUE,
useDeviceExposureDuring = TRUE, useMeasurementDuring = TRUE, useObservationDuring =
TRUE, useVisitConceptCountDuring = TRUE),
casePreTargetDuration = 365,
casePostOutcomeDuration = 365,
extractNonCaseCovariates = TRUE
)
Arguments
targetIds
A list of cohortIds for the target cohorts
outcomeIds
A list of cohortIds for the outcome cohorts
minPriorObservation
The minimum time (in days) in the database a patient in the target cohorts must be observed prior to index
outcomeWashoutDays
Patients with the outcome within outcomeWashout days prior to index are excluded from the risk factor analysis
riskWindowStart
The start of the risk window (in days) relative to the 'startAnchor'.
startAnchor
The anchor point for the start of the risk window. Can be '"cohort start"' or '"cohort end"'.
riskWindowEnd
The end of the risk window (in days) relative to the 'endAnchor'.
endAnchor
The anchor point for the end of the risk window. Can be '"cohort start"' or '"cohort end"'.
covariateSettings
An object created using FeatureExtraction::createCovariateSettings
caseCovariateSettings
An object created using createDuringCovariateSettings
casePreTargetDuration
The number of days prior to case index we use for FeatureExtraction
casePostOutcomeDuration
The number of days prior to case index we use for FeatureExtraction
extractNonCaseCovariates
Whether to extract aggregate covariates and counts for patients in the targets and outcomes in addition to the cases
Value
A list with the settings
Examples
aggregateSetting <- createAggregateCovariateSettings(
targetIds = c(1,2),
outcomeIds = c(3),
minPriorObservation = 365,
outcomeWashoutDays = 90,
riskWindowStart = 1,
startAnchor = "cohort start",
riskWindowEnd = 365,
endAnchor = "cohort start",
casePreTargetDuration = 365,
casePostOutcomeDuration = 365
)
Create the settings for a large scale characterization study
Description
This function creates a list of settings for different characterization studies
Usage
createCharacterizationSettings(
timeToEventSettings = NULL,
dechallengeRechallengeSettings = NULL,
aggregateCovariateSettings = NULL
)
Arguments
timeToEventSettings
A list of timeToEvent settings
dechallengeRechallengeSettings
A list of dechallengeRechallenge settings
aggregateCovariateSettings
A list of aggregateCovariate settings
Details
Specify one or more timeToEvent, dechallengeRechallenge and aggregateCovariate settings
Value
Returns the connection to the sqlite database
See Also
Other LargeScale:
loadCharacterizationSettings(),
runCharacterizationAnalyses(),
saveCharacterizationSettings()
Examples
# example code
drSet <- createDechallengeRechallengeSettings(
targetIds = c(1,2),
outcomeIds = 3
)
cSet <- createCharacterizationSettings(
dechallengeRechallengeSettings = drSet
)
Create the results tables to store characterization results into a database
Description
This function executes a large set of SQL statements to create tables that can store results
Usage
createCharacterizationTables(
connectionDetails,
resultSchema,
targetDialect = "postgresql",
deleteExistingTables = TRUE,
createTables = TRUE,
tablePrefix = "c_",
tempEmulationSchema = getOption("sqlRenderTempEmulationSchema")
)
Arguments
connectionDetails
The connectionDetails to a database created by using the
function createConnectDetails in the
DatabaseConnector package.
resultSchema
The name of the database schema that the result tables will be created.
targetDialect
The database management system being used
deleteExistingTables
If true any existing tables matching the Characterization result tables names will be deleted
createTables
If true the Characterization result tables will be created
tablePrefix
A string appended to the Characterization result tables
tempEmulationSchema
The temp schema used when the database management system is oracle
Details
This function can be used to create (or delete) Characterization result tables
Value
Returns NULL but creates the required tables into the specified database schema.
See Also
Other Database:
createSqliteDatabase(),
insertResultsToDatabase()
Examples
# create sqlite database
charResultDbCD <- createSqliteDatabase()
# create database results tables
createCharacterizationTables(
connectionDetails = charResultDbCD,
resultSchema = 'main'
)
Create dechallenge rechallenge study settings
Description
Create dechallenge rechallenge study settings
Usage
createDechallengeRechallengeSettings(
targetIds,
outcomeIds,
dechallengeStopInterval = 30,
dechallengeEvaluationWindow = 30
)
Arguments
targetIds
A list of cohortIds for the target cohorts
outcomeIds
A list of cohortIds for the outcome cohorts
dechallengeStopInterval
An integer specifying the how much time to add to the cohort_end when determining whether the event starts during cohort and ends after
dechallengeEvaluationWindow
An integer specifying the period of time after the cohort_end when you cannot see an outcome for a dechallenge success
Value
A list with the settings
See Also
Other DechallengeRechallenge:
computeDechallengeRechallengeAnalyses(),
computeRechallengeFailCaseSeriesAnalyses()
Examples
drSet <- createDechallengeRechallengeSettings(
targetIds = c(1,2),
outcomeIds = 3
)
Create during covariate settings
Description
Create during covariate settings
Usage
createDuringCovariateSettings(
useConditionOccurrenceDuring = FALSE,
useConditionOccurrencePrimaryInpatientDuring = FALSE,
useConditionEraDuring = FALSE,
useConditionGroupEraDuring = FALSE,
useDrugExposureDuring = FALSE,
useDrugEraDuring = FALSE,
useDrugGroupEraDuring = FALSE,
useProcedureOccurrenceDuring = FALSE,
useDeviceExposureDuring = FALSE,
useMeasurementDuring = FALSE,
useObservationDuring = FALSE,
useVisitCountDuring = FALSE,
useVisitConceptCountDuring = FALSE,
includedCovariateConceptIds = c(),
addDescendantsToInclude = FALSE,
excludedCovariateConceptIds = c(),
addDescendantsToExclude = FALSE,
includedCovariateIds = c()
)
Arguments
useConditionOccurrenceDuring
One covariate per condition in the condition_occurrence table starting between cohort start and cohort end. (analysis ID 109)
useConditionOccurrencePrimaryInpatientDuring
One covariate per condition observed as a primary diagnosis in an inpatient setting in the condition_occurrence table starting between cohort start and cohort end. (analysis ID 110)
useConditionEraDuring
One covariate per condition in the condition_era table starting between cohort start and cohort end. (analysis ID 217)
useConditionGroupEraDuring
One covariate per condition era rolled up to groups in the condition_era table starting between cohort start and cohort end. (analysis ID 218)
useDrugExposureDuring
One covariate per drug in the drug_exposure table between cohort start and end. (analysisId 305)
useDrugEraDuring
One covariate per drug in the drug_era table between cohort start and end. (analysis ID 417)
useDrugGroupEraDuring
One covariate per drug rolled up to ATC groups in the drug_era table between cohort start and end. (analysis ID 418)
useProcedureOccurrenceDuring
One covariate per procedure in the procedure_occurrence table between cohort start and end. (analysis ID 505)
useDeviceExposureDuring
One covariate per device in the device exposure table starting between cohort start and end. (analysis ID 605)
useMeasurementDuring
One covariate per measurement in the measurement table between cohort start and end. (analysis ID 713)
useObservationDuring
One covariate per observation in the observation table between cohort start and end. (analysis ID 805)
useVisitCountDuring
The number of visits observed between cohort start and end. (analysis ID 926)
useVisitConceptCountDuring
The number of visits observed between cohort start and end, stratified by visit concept ID. (analysis ID 927)
includedCovariateConceptIds
A list of concept IDs that should be used to construct covariates.
addDescendantsToInclude
Should descendant concept IDs be added to the list of concepts to include?
excludedCovariateConceptIds
A list of concept IDs that should NOT be used to construct covariates.
addDescendantsToExclude
Should descendant concept IDs be added to the list of concepts to exclude?
includedCovariateIds
A list of covariate IDs that should be restricted to.
Details
creates an object specifying how during covariates should be constructed from data in the CDM model.
Value
An object of type covariateSettings, to be used in other functions.
See Also
Other CovariateSetting:
getDbDuringCovariateData()
Examples
settings <- createDuringCovariateSettings(
useConditionOccurrenceDuring = TRUE,
useConditionOccurrencePrimaryInpatientDuring = FALSE,
useConditionEraDuring = FALSE,
useConditionGroupEraDuring = FALSE
)
Create an sqlite database connection
Description
This function creates a connection to an sqlite database
Usage
createSqliteDatabase(sqliteLocation = tempdir())
Arguments
sqliteLocation
The location of the sqlite database
Details
This function creates a sqlite database and connection
Value
Returns the connection detail object to the sqlite database
See Also
Other Database:
createCharacterizationTables(),
insertResultsToDatabase()
Examples
charResultDbCD <- createSqliteDatabase()
Create time to event study settings
Description
Create time to event study settings
Usage
createTimeToEventSettings(targetIds, outcomeIds)
Arguments
targetIds
A list of cohortIds for the target cohorts
outcomeIds
A list of cohortIds for the outcome cohorts
Value
An list with the time to event settings
See Also
Other TimeToEvent:
computeTimeToEventAnalyses()
Examples
# example code
tteSet <- createTimeToEventSettings(
targetIds = c(1,2),
outcomeIds = 3
)
create a connection detail for an example GI Bleed dataset from Eunomia
Description
This returns an object of class 'ConnectionDetails' that lets you connect via 'DatabaseConnector::connect()' to the example database.
Usage
exampleOmopConnectionDetails(exdir = tempdir())
Arguments
exdir
a directory to unzip the example OMOP database into. Default is tempdir().
Details
Finds the location of the example database in the package and calls 'DatabaseConnector::createConnectionDetails' to create a 'ConnectionDetails' object for connecting to the database.
Value
An object of class 'ConnectionDetails' with the details to connect to the example OHDSI OMOP CDM database
Examples
conDet <- exampleOmopConnectionDetails()
connectionHandler <- ResultModelManager::ConnectionHandler$new(conDet)
Extracts covariates that occur during a cohort
Description
Extracts covariates that occur during a cohort
Usage
getDbDuringCovariateData(
connection,
oracleTempSchema = NULL,
cdmDatabaseSchema,
cdmVersion = "5",
cohortTable = "#cohort_person",
rowIdField = "subject_id",
aggregated = TRUE,
cohortIds = c(-1),
covariateSettings,
minCharacterizationMean = 0,
tempEmulationSchema = getOption("sqlRenderTempEmulationSchema"),
...
)
Arguments
connection
The database connection
oracleTempSchema
The temp schema if using oracle
cdmDatabaseSchema
The schema of the OMOP CDM data
cdmVersion
version of the OMOP CDM data
cohortTable
the table name that contains the target population cohort
rowIdField
string representing the unique identifier in the target population cohort
aggregated
whether the covariate should be aggregated
cohortIds
cohort id for the target cohort
covariateSettings
settings for the covariate cohorts and time periods
minCharacterizationMean
the minimum value for a covariate to be extracted
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
...
additional arguments from FeatureExtraction
Details
The user specifies a what during covariates they want and this executes them using FE
Value
A 'FeatureExtraction' covariateData object containing the during covariates based on user settings
See Also
Other CovariateSetting:
createDuringCovariateSettings()
Examples
conDet <- exampleOmopConnectionDetails()
connection <- DatabaseConnector::connect(conDet)
settings <- createDuringCovariateSettings(
useConditionOccurrenceDuring = TRUE,
useConditionOccurrencePrimaryInpatientDuring = FALSE,
useConditionEraDuring = FALSE,
useConditionGroupEraDuring = FALSE
)
duringData <- getDbDuringCovariateData(
connection <- connection,
cdmDatabaseSchema = 'main',
cohortIds = 1,
covariateSettings = settings,
cohortTable = 'cohort'
)
DatabaseConnector::disconnect(connection)
Upload the results into a result database
Description
This function uploads results in csv format into a result database
Usage
insertResultsToDatabase(
connectionDetails,
schema,
resultsFolder,
tablePrefix = "",
csvTablePrefix = "c_",
includedFiles = NULL
)
Arguments
connectionDetails
The connection details to the result database
schema
The schema for the result database
resultsFolder
The folder containing the csv results
tablePrefix
A prefix to append to the result tables for the characterization results
csvTablePrefix
The prefix added to the csv results - default is 'c_'
includedFiles
Specify the csv files to upload or NULL to upload all in directory
Details
Calls ResultModelManager uploadResults function to upload the csv files
Value
Returns the connection to the sqlite database
See Also
Other Database:
createCharacterizationTables(),
createSqliteDatabase()
Examples
# generate results into resultsFolder
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(),'database')
)
# create sqlite database
charResultDbCD <- createSqliteDatabase()
# create database results tables
createCharacterizationTables(
connectionDetails = charResultDbCD,
resultSchema = 'main'
)
# insert results
insertResultsToDatabase(
connectionDetails = charResultDbCD,
schema = 'main',
resultsFolder = file.path(tempdir(),'database'),
includedFiles = c('time_to_event')
)
Load the characterization settings previously saved as a json file
Description
This function converts the json file back into an R object
Usage
loadCharacterizationSettings(fileName)
Arguments
fileName
The location of the the json settings
Details
Input the directory containing the 'characterizationSettings.json' file and load the settings into R
Value
Returns the json settings as an R object
See Also
Other LargeScale:
createCharacterizationSettings(),
runCharacterizationAnalyses(),
saveCharacterizationSettings()
Examples
# example code
setPath <- file.path(tempdir(), 'charSet.json')
drSet <- createDechallengeRechallengeSettings(
targetIds = c(1,2),
outcomeIds = 3
)
cSet <- createCharacterizationSettings(
dechallengeRechallengeSettings = drSet
)
saveCharacterizationSettings(
settings = cSet,
fileName = setPath
)
setting <- loadCharacterizationSettings(setPath)
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()
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')
)
Save the characterization settings as a json
Description
This function converts the settings into a json object and saves it
Usage
saveCharacterizationSettings(settings, fileName)
Arguments
settings
An object of class characterizationSettings created using createCharacterizationSettings
fileName
The location to save the json settings
Details
Input the characterization settings and output a json file to a file named 'characterizationSettings.json' inside the saveDirectory
Value
Returns the location of the directory containing the json settings
See Also
Other LargeScale:
createCharacterizationSettings(),
loadCharacterizationSettings(),
runCharacterizationAnalyses()
Examples
drSet <- createDechallengeRechallengeSettings(
targetIds = c(1,2),
outcomeIds = 3
)
cSet <- createCharacterizationSettings(
dechallengeRechallengeSettings = drSet
)
saveCharacterizationSettings(
settings = cSet,
fileName = file.path(tempdir(), 'cSet.json')
)
viewCharacterization - Interactively view the characterization results
Description
This is a shiny app for viewing interactive plots and tables
Usage
viewCharacterization(resultFolder, cohortDefinitionSet = NULL)
Arguments
resultFolder
The location of the csv results
cohortDefinitionSet
The cohortDefinitionSet extracted using webAPI
Details
Input is the output of ...
Value
Opens a shiny app for interactively viewing the results
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(),'view')
)
# interactive shiny app
## Not run:
viewCharacterization(
resultFolder = file.path(tempdir(),'view')
)
## End(Not run)