Package {SelfControlledCohort}

Type:	Package
Title:	Self-Controlled Cohort Population-Level Estimation
Version:	2.0.0
Date:	2026-05-27
Maintainer:	Jamie Gilbert <gilbert@ohdsi.org>
Description:	Estimates incidence rate ratios by comparing time exposed with time unexposed among an exposed cohort using self-controlled cohort methodology as described in Ryan et al. (2013) <doi:10.1002/pds.3457>. Functions used for empirical calibration of effect estimates, confidence intervals, and p-values are included to control for residual bias.
URL:	https://github.com/OHDSI/SelfControlledCohort, https://ohdsi.github.io/SelfControlledCohort/
BugReports:	https://github.com/OHDSI/SelfControlledCohort/issues
Language:	en-US
Depends:	DatabaseConnector (≥ 5.0.0), R (≥ 4.1.0)
Imports:	SqlRender (≥ 1.4.3), ParallelLogger, rateratio.test, checkmate, dplyr, EmpiricalCalibration, ResultModelManager, Andromeda, readr, rlang, cli, stats
Suggests:	withr, testthat, knitr, rmarkdown, Eunomia, duckdb, DiagrammeR, tidyr, R.utils, jsonlite, DT, ggplot2, shiny, shinycssloaders, plotly, reactable, shinyWidgets
License:	Apache License 2.0
RoxygenNote:	8.0.0
VignetteBuilder:	knitr
Encoding:	UTF-8
NeedsCompilation:	no
Packaged:	2026-06-09 23:53:00 UTC; jgilber2
Author:	Jamie Gilbert [cre, aut], Martijn Schuemie [aut], Patrick Ryan [aut], Observational Health Data Science and Informatics [cph]
Repository:	CRAN
Date/Publication:	2026-06-17 14:00:13 UTC

SelfControlledCohort: Self-Controlled Cohort Population-Level Estimation

Description

Estimates incidence rate ratios by comparing time exposed with time unexposed among an exposed cohort using self-controlled cohort methodology as described in Ryan et al. (2013) doi:10.1002/pds.3457. Functions used for empirical calibration of effect estimates, confidence intervals, and p-values are included to control for residual bias.

Author(s)

Maintainer: Jamie Gilbert gilbert@ohdsi.org (ORCID)

Authors:

• Jamie Gilbert gilbert@ohdsi.org (ORCID)

• Martijn Schuemie schuemie@ohdsi.org

• Patrick Ryan

Other contributors:

• Observational Health Data Science and Informatics [copyright holder]

See Also

Useful links:

• https://github.com/OHDSI/SelfControlledCohort

• https://ohdsi.github.io/SelfControlledCohort/

• Report bugs at https://github.com/OHDSI/SelfControlledCohort/issues

Check module version compatibility

Description

Check module version compatibility

Usage

checkModuleVersion(moduleVersion)

Arguments

Value

NULL (invisibly). Stops execution if incompatible, warns if older version.

Compute Expected Absolute Systematic Error (EASE)

Description

Computes the expected absolute systematic error from the null distribution fitted on negative control estimates. EASE summarizes both bias (mean of null) and imprecision (spread of null) into a single metric.

Usage

computeEase(negatives)

Arguments

Details

EASE is computed by fitting a null distribution to the negative control log rate ratios using EmpiricalCalibration::fitNull(), then calling EmpiricalCalibration::computeExpectedAbsoluteSystematicError().

Lower values indicate less systematic error. A value of 0 means no detectable bias. The default threshold of 0.25 is aligned with SCCS package conventions.

Value

Numeric EASE value, or NA if the null distribution could not be fitted.

References

Schuemie MJ, Hripcsak G, Ryan PB, Madigan D, Suchard MA. Empirical confidence interval calibration for population-level effect estimation studies in observational healthcare data. PNAS. 2018;115(11):2571-2577.

Examples

if (interactive()) {
# Compute EASE from negative control results
negativeControls <- data.frame(
  rr = c(0.95, 1.02, 0.98, 1.05),
  seLogRr = c(0.2, 0.18, 0.22, 0.19)
)
ease <- computeEase(negativeControls)
}

Compute Minimum Detectable Relative Risk (MDRR) for rate ratio

Description

Calculates the minimum detectable relative risk for a two-sample Poisson rate comparison using the Signed Root Likelihood (SRL1) method described by Musonda et al. (2006). This diagnostic assesses whether the study has adequate statistical power to detect clinically meaningful effects in a self-controlled design.

Usage

computeMdrrForRateRatio(
  exposedPersonTime,
  unexposedPersonTime,
  exposedEvents,
  unexposedEvents,
  alpha = 0.05,
  power = 0.8
)

Arguments

Details

The MDRR is the minimum incidence rate ratio that can be detected with the given sample size, alpha, and power. This implementation uses the SRL1 method from Musonda (2006), which is more accurate for self-controlled studies than standard binomial approximations.

Value

Numeric value representing the MDRR. Values > 10.0 typically indicate low power.

References

Musonda P, Farrington CP, Whitaker HJ (2006) Samples sizes for self-controlled case series studies, Statistics in Medicine, 15;25(15):2618-31

Examples

if (interactive()) {
# Calculate MDRR for a study with 100 exposed person-years and 200 unexposed person-years
mdrr <- computeMdrrForRateRatio(
  exposedPersonTime = 36500,    # 100 person-years in days
  unexposedPersonTime = 73000,  # 200 person-years in days
  exposedEvents = 10,
  unexposedEvents = 15
)
}

Create exposure-outcome combinations.

Description

Create exposure-outcome combinations.

Usage

createExposureOutcome(exposureId, outcomeId, trueEffectSize = NA)

Arguments

Details

Create a hypothesis of interest, to be used with the runSccAnalyses function.

Value

An object of class exposureOutcome specifying an exposure-outcome pair for analysis.

Examples

if (interactive()) {
# Create a simple exposure-outcome pair
eo1 <- createExposureOutcome(exposureId = 1124300,
                             outcomeId = 444382)

# Create a negative control (true effect size = 1)
eo2 <- createExposureOutcome(exposureId = 1124300,
                             outcomeId = 444382,
                             trueEffectSize = 1)
}

Create the results data model tables on a database server.

Description

Create the results data model tables on a database server.

Usage

createResultsDataModel(
  connectionDetails = NULL,
  databaseSchema,
  tablePrefix = ""
)

Arguments

Details

Only PostgreSQL and SQLite servers are supported.

Value

Invisibly returns NULL. Creates database tables for results storage as a side effect.

Examples

if (interactive()) {
connectionDetails <- DatabaseConnector::createConnectionDetails(
  dbms = "sqlite",
  server = "myResults.db"
)

createResultsDataModel(
  connectionDetails = connectionDetails,
  databaseSchema = "main"
)
}

Create a parameter object for the function runSelfControlledCohort

Description

Create a parameter object for the function runSelfControlledCohort

Usage

createRunSelfControlledCohortArgs(
  firstExposureOnly = TRUE,
  firstOutcomeOnly = TRUE,
  minAge = "",
  maxAge = "",
  studyStartDate = "",
  studyEndDate = "",
  addLengthOfExposureExposed = TRUE,
  riskWindowStartExposed = 1,
  riskWindowEndExposed = 30,
  addLengthOfExposureUnexposed = TRUE,
  riskWindowEndUnexposed = -1,
  riskWindowStartUnexposed = -30,
  hasFullTimeAtRisk = FALSE,
  washoutPeriod = 0,
  followupPeriod = 0
)

Arguments

Details

Create an object defining the parameter values.

Value

A parameter object of class args for use with runSelfControlledCohort.

Examples

if (interactive()) {
# Create default parameters
args1 <- createRunSelfControlledCohortArgs()

# Custom risk window settings
args2 <- createRunSelfControlledCohortArgs(
  riskWindowStartExposed = 1,
  riskWindowEndExposed = 60,
  hasFullTimeAtRisk = TRUE
)
}

Create a SelfControlledCohort analysis specification

Description

Create a SelfControlledCohort analysis specification

Usage

createSccAnalysis(
  analysisId = 1,
  description = "",
  exposureType = NULL,
  outcomeType = NULL,
  runSelfControlledCohortArgs,
  controlType = "outcome",
  runDiagnostics = TRUE,
  diagnostics = c("all"),
  diagnosticThresholds = getDefaultDiagnosticThresholds()
)

Arguments

Details

Create a set of analysis choices, to be used with the runSccAnalyses function.

Value

An object of class sccAnalysis containing the analysis specifications.

Examples

if (interactive()) {
# Create SCC analysis with default risk window settings
sccArgs <- createRunSelfControlledCohortArgs(riskWindowStartExposed = 1,
                                              riskWindowEndExposed = 30)

analysis1 <- createSccAnalysis(analysisId = 1,
                              description = "30-day exposed risk window",
                              runSelfControlledCohortArgs = sccArgs)
}

Create Self-Controlled Cohort Module Specifications

Description

Creates a specifications object for the Self-Controlled Cohort module to be used within the OHDSI Strategus framework.

Usage

createSelfControlledCohortModuleSpecifications(
  analysisSettings,
  exposureOutcomeList,
  computeThreads = parallel::detectCores() - 1
)

Arguments

Value

A list object of class 'SelfControlledCohortModuleSpecifications' and 'ModuleSpecifications' containing the module name, version, repository information, and analysis settings.

Examples

if (interactive()) {
eo1 <- createExposureOutcome(exposureId = 1124300, outcomeId = 444382)
analysis1 <- createSccAnalysis(analysisId = 1,
                              description = "Main",
                              runSelfControlledCohortArgs = createRunSelfControlledCohortArgs())

moduleSpec <- createSelfControlledCohortModuleSpecifications(
  analysisSettings = list(analysis1),
  exposureOutcomeList = list(eo1)
)
}

Execute Self-Controlled Cohort Analyses for Strategus

Description

Executes Self-Controlled Cohort analyses within the OHDSI Strategus framework using the provided analysis specifications and settings.

Usage

execute(
  connectionDetails,
  executionSettings,
  analysisSpecifications,
  databaseId,
  exportFolder
)

Arguments

Details

This function validates the input specifications, extracts unique exposure and outcome cohort IDs, identifies negative control pairs, and iterates over each analysis setting to run the Self-Controlled Cohort analysis. Results are exported to the specified folder. Diagnostic settings are handled per-analysis, and warnings are issued if no negative controls are found.

Value

No return value. Results are written to the specified export folder as a side effect.

Examples

if (interactive()) {
eo1 <- createExposureOutcome(exposureId = 1124300, outcomeId = 444382)
analysis1 <- createSccAnalysis(analysisId = 1,
                              description = "Main",
                              runSelfControlledCohortArgs = createRunSelfControlledCohortArgs())

moduleSpec <- createSelfControlledCohortModuleSpecifications(
  analysisSettings = list(analysis1),
  exposureOutcomeList = list(eo1)
)
execute(
  connectionDetails = connectionDetails,
  executionSettings = list(
    databaseSchema = "main",
    cohortTable = "cohort",
    cdmDatabaseSchema = "main"
  ),
  analysisSpecifications = moduleSpec,
  databaseId = "MyDatabase",
  exportFolder = tempdir()
)
}

Get database migrations instance

Description

Returns ResultModelManager DataMigrationsManager instance.

Usage

getDataMigrator(connectionDetails, databaseSchema, tablePrefix = "")

Arguments

Value

Instance of ResultModelManager::DataMigrationManager that has interface for converting existing data models

See Also

[ResultModelManager::DataMigrationManager] which this function is a utility for.

Examples

if (interactive()) {
migrator <- getDataMigrator(
  connectionDetails = connectionDetails,
  databaseSchema = "results"
)
}

Get default diagnostic thresholds

Description

Returns default thresholds for diagnostic tests following SCCS standards. Version 2.1.0+ uses revised diagnostics that align with SelfControlledCaseSeries package.

Usage

getDefaultDiagnosticThresholds()

Details

Thresholds:

• mdrrMaxAcceptable - Maximum acceptable MDRR (default: 10.0). Higher values indicate low power.

• maxPreExposureProportion - Maximum proportion of persons with pre-exposure outcomes (default: 0.05)

• preExposurePThreshold - Significance threshold for pre-exposure gain test (default: 0.05)

• maxEventDependentCensoring - Maximum proportion censored within 30 days of outcome (default: 0.25)

• minEventsPerWindow - Minimum events required in each window (default: 3)

• easeMaxAcceptable - Maximum acceptable EASE (default: 0.25). Requires negative controls.

Value

A list of diagnostic thresholds

Examples

if (interactive()) {
# Get default thresholds
   thresholds <- getDefaultDiagnosticThresholds()

   # Modify specific thresholds
   customThresholds <- getDefaultDiagnosticThresholds()
   customThresholds$mdrrMaxAcceptable <- 5.0
}

Get Default export manager

Description

Returns the default export manager class for writing csv file results

Usage

getDefaultExportManager(resultExportPath, databaseId)

Arguments

Value

An instance of ResultModelManager::ResultExportManager configured for SelfControlledCohort results.

Examples

if (interactive()) {
exportManager <- getDefaultExportManager(
  resultExportPath = tempdir(),
  databaseId = "CCAE"
)
}

Get diagnostics summary

Description

Returns a summary of diagnostic results for each target-outcome pair, including blinding status.

Usage

getDiagnosticsSummary(diagnosticResults)

Arguments

Value

A data frame with blinding status per target-outcome pair

Examples

if (interactive()) {
connectionDetails <- Eunomia::getEunomiaConnectionDetails()
connection <- DatabaseConnector::connect(connectionDetails)

diagnostics <- runSccDiagnostics(
  connection = connection,
  cdmDatabaseSchema = "main",
  resultsTable = "#scc_results",
  riskWindowsTable = "#risk_windows",
  analysisId = 1,
  databaseId = "Eunomia",
  estimates = resultsData,
  diagnostics = "all",
  thresholds = getDefaultDiagnosticThresholds()
)

DatabaseConnector::disconnect(connection)
summary <- getDiagnosticsSummary(diagnostics)
}

Get module information

Description

Get module information

Usage

getModuleInfo()

Value

A list with module metadata

Examples

if (interactive())
moduleInfo <- getModuleInfo()

Get specifications for CohortMethod results data model

Description

Get specifications for CohortMethod results data model

Usage

getResultsDataModelSpecifications()

Value

A data frame object with specifications

Examples

if (interactive()) {
specs <- getResultsDataModelSpecifications()
}

Get results folders for an analysis specification

Description

Get results folders for an analysis specification

Usage

getResultsFolders(analysisSpecification, exportFolder)

Arguments

Value

A character vector of paths to results folders for each analysis setting.

Examples

if (interactive())
resultsFolders <- getResultsFolders(analysisSpec, tempdir())

Get Self-Controlled Cohort Risk Window Statistics

Description

Compute statistics from risk windows.

Usage

getSccRiskWindowStats(
  connection,
  outcomeDatabaseSchema,
  databaseId,
  tempEmulationSchema = getOption("sqlRenderTempEmulationSchema"),
  outcomeIds = NULL,
  outcomeTable = "condition_era",
  firstOutcomeOnly = TRUE,
  resultsDatabaseSchema = NULL,
  riskWindowsTable = "#risk_windows",
  resultExportPath = "scc_result",
  analysisId = 1,
  resultExportManager = ResultModelManager::createResultExportManager(tableSpecification
    = getResultsDataModelSpecifications(), exportDir = resultExportPath, databaseId =
    databaseId)
)

Arguments

Details

Requires a risk window table to be created first with 'runSccRiskWindows'

Value

list containing data frames: treatmentTimeDistribution, timeToOutcomeDistribution, timeToOutcomeDistributionExposed, timeToOutcomeDistributionUnexposed

Examples

if (interactive()) {
# First, create the risk windows table
connectionDetails <- Eunomia::getEunomiaConnectionDetails()
connection <- DatabaseConnector::connect(connectionDetails)
riskWindowsTable <- "computed_risk_windows"
runSccRiskWindows(connection,
                  cdmDatabaseSchema = "main",
                  exposureIds = c(1102527, 1125315),
                  resultsDatabaseSchema = "main",
                  riskWindowsTable = riskWindowsTable,
                  exposureTable = "drug_era")
# Get stats based on outcomes of interest
tarStats <- getSccRiskWindowStats(connection,
                                  outcomeDatabaseSchema = "main",
                                  databaseId = "Eunomia",
                                  resultsDatabaseSchema = "main",
                                  riskWindowsTable = riskWindowsTable,
                                  outcomeTable = "condition_era",
                                  outcomeIds = 192671)
DatabaseConnector::disconnect(connection)
}

Load a list of exposureOutcome from file

Description

Load a list of objects of type exposureOutcome from file. The file is in JSON format.

Usage

loadExposureOutcomeList(file)

Arguments

Value

A list of objects of type exposureOutcome.

Examples

tempFile <- file.path(tempdir(), "exposureOutcomes.json")
saveExposureOutcomeList(list(createExposureOutcome(1124300, 444382)), tempFile)
eoList <- loadExposureOutcomeList(tempFile)

Load a list of sccAnalysis from file

Description

Load a list of objects of type sccAnalysis from file. The file is in JSON format.

Usage

loadSccAnalysisList(file)

Arguments

Value

A list of objects of type sccAnalysis.

Examples

if (interactive()) {
tempFile <- file.path(tempdir(), "analyses.json")
saveSccAnalysisList(list(createSccAnalysis(analysisId = 1,
  runSelfControlledCohortArgs = createRunSelfControlledCohortArgs())), tempFile)
analysisList <- loadSccAnalysisList(tempFile)
}

Migrate Data model

Description

Migrate data from current state to next state

It is strongly advised that you have a backup of all data (either sqlite files, a backup database (in the case you are using a postgres backend) or have kept the csv/zip files from your data generation.

Usage

migrateDataModel(connectionDetails, databaseSchema, tablePrefix = "")

Arguments

Value

Invisibly returns NULL. Migrates the database schema as a side effect.

Examples

if (interactive()) {
migrateDataModel(
  connectionDetails = connectionDetails,
  databaseSchema = "results"
)
}

Run a list of analyses

Description

Run a list of analyses

Usage

runSccAnalyses(
  connectionDetails,
  cdmDatabaseSchema,
  tempEmulationSchema = getOption("sqlRenderTempEmulationSchema"),
  exposureDatabaseSchema = cdmDatabaseSchema,
  exposureTable = "drug_era",
  outcomeDatabaseSchema = cdmDatabaseSchema,
  outcomeTable = "condition_occurrence",
  resultsFolder = "./SelfControlledCohortOutput",
  sccAnalysisList,
  exposureOutcomeList,
  databaseId,
  controlType = "outcome",
  analysisThreads = 1,
  computeThreads = 1
)

Arguments

Details

Run a list of analyses for the drug-comparator-outcomes of interest. This function will run all specified analyses against all hypotheses of interest, meaning that the total number of outcome models is 'length(cmAnalysisList) * length(drugComparatorOutcomesList)'.

Value

Invisibly returns a data frame containing a reference table for all exposure-outcome-analysis combinations executed.

Examples

if (interactive()) {
connectionDetails <- Eunomia::getEunomiaConnectionDetails()

eo1 <- createExposureOutcome(exposureId = 1124300, outcomeId = 444382)
analysis1 <- createSccAnalysis(analysisId = 1,
                              description = "Main analysis",
                              runSelfControlledCohortArgs = createRunSelfControlledCohortArgs())

results <- runSccAnalyses(
  connectionDetails = connectionDetails,
  cdmDatabaseSchema = "main",
  exposureOutcomeList = list(eo1),
  sccAnalysisList = list(analysis1),
  databaseId = "Eunomia",
  resultsFolder = tempdir()
)
}

Run Self-Controlled Cohort Diagnostics

Description

Runs a suite of diagnostic tests to assess the validity of SCC analysis assumptions. Version 1.6.0+ implements diagnostics aligned with SelfControlledCaseSeries package standards.

Usage

runSccDiagnostics(
  connection,
  cdmDatabaseSchema,
  tempEmulationSchema = getOption("sqlRenderTempEmulationSchema"),
  resultsTable,
  riskWindowsTable,
  outcomeTable = "condition_era",
  outcomeDatabaseSchema = cdmDatabaseSchema,
  analysisId,
  databaseId,
  estimates = NULL,
  diagnostics = c("all"),
  thresholds = getDefaultDiagnosticThresholds(),
  resultExportManager
)

Arguments

Value

Invisible data frame of diagnostic results

Examples

if (interactive()) {
  connectionDetails <- Eunomia::getEunomiaConnectionDetails()
  connection <- DatabaseConnector::connect(connectionDetails)

  diagnostics <- runSccDiagnostics(
    connection = connection,
    cdmDatabaseSchema = "main",
    resultsTable = "#scc_results",
    riskWindowsTable = "#risk_windows",
    analysisId = 1,
    databaseId = "Eunomia",
    estimates = resultsData,
    diagnostics = "all",
    thresholds = getDefaultDiagnosticThresholds()
  )

  DatabaseConnector::disconnect(connection)
}

Run Self-Controlled Cohort Risk Windows

Description

Compute time at risk exposed and time at risk unexposed for risk window parameters. See 'getSccRiskWindowStats' for example usage.

Usage

runSccRiskWindows(
  connection,
  cdmDatabaseSchema,
  tempEmulationSchema = getOption("sqlRenderTempEmulationSchema"),
  exposureIds = NULL,
  exposureDatabaseSchema = cdmDatabaseSchema,
  exposureTable = "drug_era",
  firstExposureOnly = TRUE,
  minAge = "",
  maxAge = "",
  studyStartDate = "",
  studyEndDate = "",
  addLengthOfExposureExposed = TRUE,
  riskWindowStartExposed = 1,
  riskWindowEndExposed = 30,
  addLengthOfExposureUnexposed = TRUE,
  riskWindowEndUnexposed = -1,
  riskWindowStartUnexposed = -30,
  hasFullTimeAtRisk = FALSE,
  washoutPeriod = 0,
  followupPeriod = 0,
  riskWindowsTable = "#risk_windows",
  keepResultsTables = TRUE,
  analysisId = 1,
  resultsDatabaseSchema = NULL
)

Arguments

Value

Invisibly returns NULL. Creates the risk windows table in the database as a side effect.

Examples

if (interactive()) {
connectionDetails <- Eunomia::getEunomiaConnectionDetails()
connection <- DatabaseConnector::connect(connectionDetails)

runSccRiskWindows(
  connection = connection,
  cdmDatabaseSchema = "main",
  exposureIds = c(1124300),
  riskWindowStartExposed = 1,
  riskWindowEndExposed = 30,
  riskWindowsTable = "#risk_windows"
)

DatabaseConnector::disconnect(connection)
}

Run self-controlled cohort

Description

runSelfControlledCohort generates population-level estimation by comparing exposed and unexposed time among exposed cohort.

Usage

runSelfControlledCohort(
  connectionDetails = NULL,
  cdmDatabaseSchema,
  connection = NULL,
  tempEmulationSchema = getOption("sqlRenderTempEmulationSchema"),
  exposureIds = NULL,
  outcomeIds = NULL,
  negativeControlPairs = NULL,
  controlType = "outcome",
  exposureDatabaseSchema = cdmDatabaseSchema,
  exposureTable = "drug_era",
  outcomeDatabaseSchema = cdmDatabaseSchema,
  outcomeTable = "condition_era",
  firstExposureOnly = TRUE,
  firstOutcomeOnly = TRUE,
  minAge = "",
  maxAge = "",
  studyStartDate = "",
  studyEndDate = "",
  addLengthOfExposureExposed = TRUE,
  riskWindowStartExposed = 1,
  riskWindowEndExposed = 30,
  addLengthOfExposureUnexposed = TRUE,
  riskWindowEndUnexposed = -1,
  riskWindowStartUnexposed = -30,
  hasFullTimeAtRisk = FALSE,
  washoutPeriod = 0,
  followupPeriod = 0,
  computeThreads = getOption("strategus.SelfControlledCohort.computeThreads", default =
    parallel::detectCores() - 1),
  riskWindowsTable = "#risk_windows",
  resultsTable = "#results",
  keepResultsTables = TRUE,
  extractResults = TRUE,
  resultsDatabaseSchema = NULL,
  resultExportPath = "scc_result",
  databaseId,
  analysisId = 1,
  analysisDescription = paste("SCC analysis", analysisId),
  resultExportManager = getDefaultExportManager(resultExportPath, databaseId),
  runDiagnostics = TRUE,
  diagnostics = c("all"),
  diagnosticThresholds = getDefaultDiagnosticThresholds()
)

Arguments

Details

Population-level estimation method that estimates incidence rate comparison of exposed/unexposed time within an exposed cohort. If multiple exposureIds and outcomeIds are provided, estimates will be generated for every combination of exposure and outcome.

Value

An object of type sccResults containing the results of the analysis.

References

Ryan PB, Schuemie MJ, Madigan D.Empirical performance of a self-controlled cohort method: lessons for developing a risk identification and analysis system. Drug Safety 36 Suppl1:S95-106, 2013

Examples

if (interactive()) {
# Use Eunomia synthetic database for demonstration
connectionDetails <- Eunomia::getEunomiaConnectionDetails()

result <- runSelfControlledCohort(
  connectionDetails = connectionDetails,
  cdmDatabaseSchema = "main",
  exposureIds = c(1124300),
  outcomeIds = 444382,
  outcomeTable = "condition_era",
  databaseId = "Eunomia"
)
}

Save a list of exposureOutcome to file

Description

Write a list of objects of type exposureOutcome to file. The file is in JSON format.

Usage

saveExposureOutcomeList(exposureOutcomeList, file)

Arguments

Value

Invisibly returns NULL. Saves the exposure-outcome list to file as a side effect.

Examples

if (interactive()) {
eo1 <- createExposureOutcome(exposureId = 1124300, outcomeId = 444382)
saveExposureOutcomeList(list(eo1), file.path(tempdir(), "exposureOutcomes.json"))
}

Save a list of sccAnalysis to file

Description

Write a list of objects of type sccAnalysis to file. The file is in JSON format.

Usage

saveSccAnalysisList(sccAnalysisList, file)

Arguments

Value

Invisibly returns NULL. Saves the analysis list to file as a side effect.

Examples

if (interactive()) {
analysis1 <- createSccAnalysis(analysisId = 1,
                              description = "30-day risk window",
                              runSelfControlledCohortArgs = createRunSelfControlledCohortArgs())
saveSccAnalysisList(list(analysis1), file.path(tempdir(), "analyses.json"))
}

Upload results to the database server.

Description

Requires the results data model tables have been created using the createResultsDataModel function.

Usage

uploadResults(
  connectionDetails,
  schema,
  resultsFolder,
  forceOverWriteOfSpecifications = FALSE,
  purgeSiteDataBeforeUploading = FALSE,
  tablePrefix = "",
  ...
)

Arguments

Value

Invisibly returns NULL. Uploads results to the database as a side effect.

Examples

if (interactive()) {
connectionDetails <- DatabaseConnector::createDonnectionDetails(
  dbms = "sqlite",
  server = "myResults.db"
)
uploadResults(
  connectionDetails = connectionDetails,
  schema = "main",
  resultsFolder = tempdir()
)
}