getTargetBinaryFeatures {OhdsiReportGenerator} | R Documentation |
Extract aggregate statistics of binary feature analysis IDs of interest for targets
Description
This function extracts the feature extraction results for targets corresponding to specified target and outcome cohorts.
Usage
getTargetBinaryFeatures(
connectionHandler,
schema,
cTablePrefix = "c_",
cgTablePrefix = "cg_",
databaseTable = "database_meta_data",
targetIds = NULL,
outcomeIds = NULL,
analysisIds = c(3)
)
Arguments
connectionHandler |
A connection handler that connects to the database and extracts sql queries. Create a connection handler via 'ResultModelManager::ConnectionHandler$new()'. |
schema |
The result database schema (e.g., 'main' for sqlite) |
cTablePrefix |
The prefix used for the characterization results tables |
cgTablePrefix |
The prefix used for the cohort generator results tables |
databaseTable |
The name of the table with the database details (default 'database_meta_data') |
targetIds |
A vector of integers corresponding to the target cohort IDs |
outcomeIds |
A vector of integers corresponding to the outcome cohort IDs |
analysisIds |
The feature extraction analysis ID of interest (e.g., 201 is condition) |
Details
Specify the connectionHandler, the schema and the target/outcome cohort IDs
Value
Returns a data.frame with the columns:
databaseName the name of the database
targetName the target cohort name
targetId the target cohort unique identifier
outcomeName the outcome name
outcomeId the outcome unique identifier
minPriorObservation the minimum required observation days prior to index for an entry
outcomeWashoutDays patients with the outcome occurring within this number of days prior to index are excluded (NA means no exclusion)
covariateId the id of the feature
covariateName the name of the feature
sumValue the number of target patients who have the feature value of 1 (minus those excluded due to having the outcome prior)
rawSum the number of target patients who have the feature value of 1 (ignoring exclusions)
rawAverage the fraction of target patients who have the feature value of 1 (ignoring exclusions)
See Also
Other Characterization:
getBinaryCaseSeries()
,
getBinaryRiskFactors()
,
getCaseBinaryFeatures()
,
getCaseContinuousFeatures()
,
getCaseCounts()
,
getCharacterizationDemographics()
,
getContinuousCaseSeries()
,
getContinuousRiskFactors()
,
getDechallengeRechallenge()
,
getIncidenceRates()
,
getTargetContinuousFeatures()
,
getTargetCounts()
,
getTimeToEvent()
,
plotAgeDistributions()
,
plotSexDistributions()
Examples
conDet <- getExampleConnectionDetails()
connectionHandler <- ResultModelManager::ConnectionHandler$new(conDet)
tbf <- getTargetBinaryFeatures (
connectionHandler = connectionHandler,
schema = 'main'
)