getTargetBinaryFeatures: Extract aggregate statistics of binary feature analysis IDs of interest for targets (ignoring excluding people with prior outcome)

Description

This function extracts the feature extraction results for targets corresponding to specified target but does not exclude any patients with the outcome during the outcome washout (so it agnostic to the outcome of interest).

Usage

getTargetBinaryFeatures(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetId = NULL,
  databaseIds = NULL,
  analysisIds = NULL,
  conceptIds = NULL
)

Value

Returns a data.frame with the columns:

databaseName the name of the database
databaseId the unique identifier of the database
targetName the target cohort name
targetId the target cohort unique identifier
minPriorObservation the minimum required observation days prior to index for an entry
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 (target patients are restricted to first occurrence and require min prior obervation days)
averageAvalue the fraction of target patients who have the feature value of 1 (target patients are restricted to first occurrence and require min prior obervation days)

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')
targetId: An integer corresponding to the target cohort ID
databaseIds: (optional) A vector of database ids to restrict to
analysisIds: (optional) The feature extraction analysis ID of interest (e.g., 201 is condition)
conceptIds: (optional) The feature extraction concept ID of interest to restrict to

Details

Specify the connectionHandler, the schema and the target cohort IDs

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getCaseBinaryFeatures(), getCaseContinuousFeatures(), getCaseCounts(), getCaseTargetBinaryFeatures(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions()

Examples

Run this code

conDet <- getExampleConnectionDetails()

connectionHandler <- ResultModelManager::ConnectionHandler$new(conDet)

tbf <- getTargetBinaryFeatures (
connectionHandler = connectionHandler, 
schema = 'main',
targetId = 1
)