Package 'SelfControlledCohort' reference manual

Title:	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.
Authors:	Jamie Gilbert [cre, aut] (ORCID: <https://orcid.org/0000-0003-2294-3459>), Martijn Schuemie [aut], Patrick Ryan [aut], Observational Health Data Science and Informatics [cph]
Maintainer:	Jamie Gilbert <[email protected]>
License:	Apache License 2.0
Version:	2.0.0
Built:	2026-06-17 18:01:34 UTC
Source:	https://github.com/cran/SelfControlledCohort

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)
computeEase(negatives)

Arguments

negatives

Data frame of negative control results with columns 'rr' and 'seLogRr'

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)
}

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

Arguments

exposedPersonTime

Total person-time in exposed window (in days)

unexposedPersonTime

Total person-time in unexposed window (in days)

exposedEvents

Number of outcome events in exposed window

unexposedEvents

Number of outcome events in unexposed window

alpha

Significance level (default: 0.05)

power

Desired power (default: 0.80)

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
)
}

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)
createExposureOutcome(exposureId, outcomeId, trueEffectSize = NA)

Arguments

exposureId

A concept ID indentifying the drug of interest in the exposure table. If multiple strategies for picking the exposure will be tested in the analysis, a named list of numbers can be provided instead. In the analysis, the name of the number to be used can be specified using the exposureType parameter in the createSccAnalysis function.

outcomeId

A concept ID indentifying the outcome of interest in the outcome table. If multiple strategies for picking the outcome will be tested in the analysis, a named list of numbers can be provided instead. In the analysis, the name of the number to be used can be specified using the #' outcomeType parameter in the createSccAnalysis function.

trueEffectSize

Should this be set to 1 this will be considererd a negative control

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)
}

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

Arguments

connectionDetails

DatabaseConnector connectionDetails instance @seealso[DatabaseConnector::createConnectionDetails]

databaseSchema

The schema on the server where the tables will be created.

tablePrefix

(Optional) string to insert before table names for database table names

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"
)
}

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
)
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

firstExposureOnly

If TRUE, only use first occurrence of each drug concept idfor each person

firstOutcomeOnly

If TRUE, only use first occurrence of each condition conceptid for each person.

minAge

Integer for minimum allowable age.

maxAge

Integer for maximum allowable age.

studyStartDate

Date for minimum allowable data for index exposure. Dateformat is 'yyyymmdd'.

studyEndDate

Date for maximum allowable data for index exposure. Dateformat is 'yyyymmdd'.

addLengthOfExposureExposed

If TRUE, use the duration from drugEraStart -> drugEraEnd as part of timeAtRisk.

riskWindowStartExposed

Integer of days to add to drugEraStart for start oftimeAtRisk (0 to include index date, 1 to start the dayafter).

riskWindowEndExposed

Additional window to add to end of exposure period (ifaddLengthOfExposureExposed = TRUE, then add to exposure enddate, else add to exposure start date).

addLengthOfExposureUnexposed

If TRUE, use the duration from exposure start -> exposureend as part of timeAtRisk looking back before exposurestart.

riskWindowEndUnexposed

Integer of days to add to exposure start for end oftimeAtRisk (0 to include index date, -1 to end the daybefore).

riskWindowStartUnexposed

Additional window to add to start of exposure period (ifaddLengthOfExposureUnexposed = TRUE, then add to exposureend date, else add to exposure start date).

hasFullTimeAtRisk

If TRUE, restrict to people who have full time-at-riskexposed and unexposed.

washoutPeriod

Integer to define required time observed before exposurestart.

followupPeriod

Integer to define required time observed after exposurestart. absolute time between treatment and outcome. Note, may add significant computation time on some database engines. If set true in one analysis will default to true for all others.

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
)
}

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

Arguments

analysisId

An integer that will be used later to refer to this specific set of analysis choices.

description

A short description of the analysis.

exposureType

If more than one exposure is provided for each exposureOutcome, this field should be used to select the specific exposure to use in this analysis.

outcomeType

If more than one outcome is provided for each exposureOutcome, this field should be used to select the specific outcome to use in this analysis.

runSelfControlledCohortArgs

An object representing the arguments to be used when calling the runSelfControlledCohort function.

controlType

Character string specifying the type of control. Options are "outcome" or "exposure". Default is "outcome".

runDiagnostics

Logical indicating whether to run diagnostic tests on the results. Default is TRUE.

diagnostics

Character vector specifying which diagnostics to run. Options: "all", "counts", "event_dependent", "pre_exposure", "window_balance", "cohort_stability". Default is "all".

diagnosticThresholds

Named list of diagnostic thresholds. See getDefaultDiagnosticThresholds() for defaults.

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)
}

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

Arguments

analysisSettings

A list of analysis settings containing the analysis configuration and parameters for self-controlled cohort analyses.

exposureOutcomeList

A list of objects of type exposureOutcome as created using the createExposureOutcome function. Each object defines an exposure-outcome pair with exposureId, outcomeId, and optionally trueEffectSize (set to 1 for negative controls).

computeThreads

Integer specifying the number of threads to use for parallel computation. Default is the number of available cores minus 1.

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)
)
}

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

Arguments

connectionDetails

An object containing the details required to connect to the database.

executionSettings

A list of settings for execution, including database schemas and cohort table names.

analysisSpecifications

A list containing analysis settings, exposure-outcome pairs, and compute thread count.

databaseId

A string identifier for the target database.

exportFolder

Path to the folder where results and exports will be written.

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()
)
}

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 = "")
getDataMigrator(connectionDetails, databaseSchema, tablePrefix = "")

Arguments

connectionDetails

DatabaseConnector connection details object

databaseSchema

String schema where database schema lives

tablePrefix

(Optional) Use if a table prefix is used before table names (e.g. "cd_")

Value

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

Examples


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

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()
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
}

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)
getDefaultExportManager(resultExportPath, databaseId)

Arguments

resultExportPath

Folder where result files are exported

databaseId

Unique identifier for database - required

Value

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

Examples


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

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)
getDiagnosticsSummary(diagnosticResults)

Arguments

diagnosticResults

Data frame of diagnostic results as returned by runSccDiagnostics

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)
}

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()
getModuleInfo()

Value

A list with module metadata

Examples


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

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

Get specifications for CohortMethod results data model

Description

Get specifications for CohortMethod results data model

Usage

getResultsDataModelSpecifications()
getResultsDataModelSpecifications()

Value

A data frame object with specifications

Examples


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

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

Get results folders for an analysis specification

Description

Get results folders for an analysis specification

Usage

getResultsFolders(analysisSpecification, exportFolder)
getResultsFolders(analysisSpecification, exportFolder)

Arguments

analysisSpecification

An analysis specification object containing analysis settings and exposure-outcome pairs.

exportFolder

The base folder where results are exported. Individual analysis results will be in subfolders named A_analysisId.

Value

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

Examples


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

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)
)
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

connection

DatabaseConnector connection instance

outcomeDatabaseSchema

The name of the database schema that is the location where the data used to define the outcome cohorts is available. If exposureTable = CONDITION_ERA, exposureDatabaseSchema is not used by assumed to be cdmSchema. Requires read permissions to this database.

databaseId

Unique identifier for database - required

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.

outcomeIds

The condition_concept_ids or cohort_definition_ids of the outcomes of interest. If empty, all the outcomes in the outcome table will be included.

outcomeTable

The tablename that contains the outcome cohorts. If outcomeTable <> CONDITION_OCCURRENCE, then expectation is outcomeTable has format of COHORT table: COHORT_DEFINITION_ID, SUBJECT_ID, COHORT_START_DATE, COHORT_END_DATE.

firstOutcomeOnly

If TRUE, only use first occurrence of each condition concept id for each person.

resultsDatabaseSchema

Schema to oputput results to. Ignored if resultsTable and riskWindowsTable are temporary.

riskWindowsTable

String: optionally store the risk windows in a (non-temporary) table.

resultExportPath

Folder where result files are exported

analysisId

An integer unique to this analysis

resultExportManager

ResultModelManager::ResultExportManager instance - customize this to implement an alternative mechanism for exporting results

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)
}

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)
loadExposureOutcomeList(file)

Arguments

file

The name of the file

Value

A list of objects of type exposureOutcome.

Examples


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

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)
loadSccAnalysisList(file)

Arguments

file

The name of the file

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)
}

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 = "")
migrateDataModel(connectionDetails, databaseSchema, tablePrefix = "")

Arguments

connectionDetails

DatabaseConnector connection details object

databaseSchema

String schema where database schema lives

tablePrefix

(Optional) Use if a table prefix is used before table names (e.g. "cd_")

Value

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

Examples


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

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
)
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

connectionDetails

An R object of type connectionDetails created using the function createConnectionDetails in the DatabaseConnector package.

cdmDatabaseSchema

Name of database schema that contains the OMOP CDM and vocabulary.

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.

exposureDatabaseSchema

The name of the database schema that is the location where the exposure data used to define the exposure cohorts is available. If exposureTable = DRUG_ERA, exposureDatabaseSchema is not used by assumed to be cdmSchema. Requires read permissions to this database.

exposureTable

The tablename that contains the exposure cohorts. If exposureTable <> DRUG_ERA, then expectation is exposureTable has format of COHORT table: cohort_concept_id, SUBJECT_ID, COHORT_START_DATE, COHORT_END_DATE.

outcomeDatabaseSchema

outcomeTable

resultsFolder

Name of the folder where all the outputs will written to.

sccAnalysisList

A list of objects of type sccAnalysis as created using the createSccAnalysis function.

exposureOutcomeList

A list of objects of type exposureOutcome as created using the createExposureOutcome function.

databaseId

Unique identifier for database - required

controlType

Calibrate effect estimates with outcome (default) or exposure controls

analysisThreads

The number of parallel threads to use to execute the analyses.

computeThreads

Number of parallel threads for computing IRRs with exact confidence intervals.

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()
)
}

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

Arguments

connection

DatabaseConnector connection instance

cdmDatabaseSchema

Name of database schema that contains OMOP CDM

tempEmulationSchema

Schema for temp table emulation (Oracle, Impala)

resultsTable

Name of the results table (contains counts)

riskWindowsTable

Name of the risk windows table

outcomeTable

Name of outcome table (e.g., "condition_era", "cohort")

outcomeDatabaseSchema

Schema containing outcome table

analysisId

Analysis identifier

databaseId

Database identifier for results export

estimates

Data frame of raw SCC results (including rr and se_log_rr)

diagnostics

Character vector of diagnostics to run. Options: "all", "mdrr", "pre_exposure_gain", "event_dependent", "ease"

thresholds

Named list of diagnostic thresholds (see getDefaultDiagnosticThresholds)

resultExportManager

ResultModelManager::ResultExportManager instance

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)
}

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
)
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

connection

DatabaseConnector connection instance

cdmDatabaseSchema

Name of database schema that contains the OMOP CDM and vocabulary.

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.

exposureIds

A vector containing the drug_concept_ids or cohort_definition_ids of the exposures of interest. If empty, all exposures in the exposure table will be included.

exposureDatabaseSchema

exposureTable

firstExposureOnly

If TRUE, only use first occurrence of each drug concept id for each person

minAge

Integer for minimum allowable age.

maxAge

Integer for maximum allowable age.

studyStartDate

Date for minimum allowable data for index exposure. Date format is 'yyyymmdd'.

studyEndDate

Date for maximum allowable data for index exposure. Date format is 'yyyymmdd'.

addLengthOfExposureExposed

If TRUE, use the duration from drugEraStart -> drugEraEnd as part of timeAtRisk.

riskWindowStartExposed

Integer of days to add to drugEraStart for start of timeAtRisk (0 to include index date, 1 to start the day after).

riskWindowEndExposed

Additional window to add to end of exposure period (if addLengthOfExposureExposed = TRUE, then add to exposure end date, else add to exposure start date).

addLengthOfExposureUnexposed

If TRUE, use the duration from exposure start -> exposure end as part of timeAtRisk looking back before exposure start.

riskWindowEndUnexposed

Integer of days to add to exposure start for end of timeAtRisk (0 to include index date, -1 to end the day before).

riskWindowStartUnexposed

Additional window to add to start of exposure period (if addLengthOfExposureUnexposed = TRUE, then add to exposure end date, else add to exposure start date).

hasFullTimeAtRisk

If TRUE, restrict to people who have full time-at-risk exposed and unexposed.

washoutPeriod

Integer to define required time observed before exposure start.

followupPeriod

Integer to define required time observed after exposure start.

riskWindowsTable

String: optionally store the risk windows in a (non-temporary) table.

keepResultsTables

Keep the results tables in place if they exist. This allows the data set to be added to with aditional targets and outcomes. (ignored if temporary tables are used, default)

analysisId

An integer unique to this analysis

resultsDatabaseSchema

Schema to oputput results to. Ignored if resultsTable and riskWindowsTable are temporary.

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)
}

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()
)
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

connectionDetails

An R object of type connectionDetails created using the function createConnectionDetails in the DatabaseConnector package.

cdmDatabaseSchema

Name of database schema that contains the OMOP CDM and vocabulary.

connection

DatabaseConnector connection instance

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.

exposureIds

A vector containing the drug_concept_ids or cohort_definition_ids of the exposures of interest. If empty, all exposures in the exposure table will be included.

outcomeIds

The condition_concept_ids or cohort_definition_ids of the outcomes of interest. If empty, all the outcomes in the outcome table will be included.

negativeControlPairs

A list of vectors for pairs of negative control

controlType

Calibrate effect estimates with outcome (default) or exposure controls

exposureDatabaseSchema

exposureTable

outcomeDatabaseSchema

outcomeTable

firstExposureOnly

If TRUE, only use first occurrence of each drug concept id for each person

firstOutcomeOnly

If TRUE, only use first occurrence of each condition concept id for each person.

minAge

Integer for minimum allowable age.

maxAge

Integer for maximum allowable age.

studyStartDate

Date for minimum allowable data for index exposure. Date format is 'yyyymmdd'.

studyEndDate

Date for maximum allowable data for index exposure. Date format is 'yyyymmdd'.

addLengthOfExposureExposed

If TRUE, use the duration from drugEraStart -> drugEraEnd as part of timeAtRisk.

riskWindowStartExposed

Integer of days to add to drugEraStart for start of timeAtRisk (0 to include index date, 1 to start the day after).

riskWindowEndExposed

Additional window to add to end of exposure period (if addLengthOfExposureExposed = TRUE, then add to exposure end date, else add to exposure start date).

addLengthOfExposureUnexposed

If TRUE, use the duration from exposure start -> exposure end as part of timeAtRisk looking back before exposure start.

riskWindowEndUnexposed

Integer of days to add to exposure start for end of timeAtRisk (0 to include index date, -1 to end the day before).

riskWindowStartUnexposed

Additional window to add to start of exposure period (if addLengthOfExposureUnexposed = TRUE, then add to exposure end date, else add to exposure start date).

hasFullTimeAtRisk

If TRUE, restrict to people who have full time-at-risk exposed and unexposed.

washoutPeriod

Integer to define required time observed before exposure start.

followupPeriod

Integer to define required time observed after exposure start.

computeThreads

Number of parallel threads for computing IRRs with exact confidence intervals.

riskWindowsTable

String: optionally store the risk windows in a (non-temporary) table.

resultsTable

String: optionally store the summary results (number exposed/ unexposed patients per outcome-exposure pair) in a (non-temporary) table. Note that this table does not store the rate ratios, only the values required to calculate rate ratios.

keepResultsTables

Keep the results tables in place if they exist. This allows the data set to be added to with aditional targets and outcomes. (ignored if temporary tables are used, default)

extractResults

Export results to disk. In the case of very large exposure/outcome set pairs it is often more ideal to create a permanent results table with the @resultsTable parameter and extract and calibrate small subsets on demand. Performing calibration across the full set of outcome/exposure pairs can take a significant amount of time. If set to false, no relative risk ratios will be produced.

resultsDatabaseSchema

Schema to oputput results to. Ignored if resultsTable and riskWindowsTable are temporary.

resultExportPath

Folder where result files are exported

databaseId

Unique identifier for database - required

analysisId

An integer unique to this analysis

analysisDescription

A string description of the analysis (optional)

resultExportManager

ResultModelManager::ResultExportManager instance - customize this to implement an alternative mechanism for exporting results

runDiagnostics

If TRUE, run diagnostic tests on the results

diagnostics

Character vector specifying which diagnostics to run. Options: "all", "counts", "event_dependent", "pre_exposure", "window_balance", "cohort_stability". Default is "all".

diagnosticThresholds

Named list of diagnostic thresholds. See getDefaultDiagnosticThresholds()

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"
)
}

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)
saveExposureOutcomeList(exposureOutcomeList, file)

Arguments

exposureOutcomeList

The exposureOutcome list to be written to file

file

The name of the file where the results will be written

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"))
}

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)
saveSccAnalysisList(sccAnalysisList, file)

Arguments

sccAnalysisList

The sccAnalysis list to be written to file

file

The name of the file where the results will be written

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"))
}

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

Arguments

connectionDetails

An object of type connectionDetails as created using the createConnectionDetails function in the DatabaseConnector package.

schema

The schema on the server where the tables have been created.

resultsFolder

Path to result files

forceOverWriteOfSpecifications

If TRUE, specifications of the phenotypes, cohort definitions, and analysis will be overwritten if they already exist on the database. Only use this if these specifications have changed since the last upload.

purgeSiteDataBeforeUploading

If TRUE, before inserting data for a specific databaseId all the data for that site will be dropped. This assumes the input zip file contains the full data for that data site.

tablePrefix

(Optional) string to insert before table names for database table names

...

See ResultModelManager::uploadResults

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()
)
}

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

Package 'SelfControlledCohort'

Help Index

Compute Expected Absolute Systematic Error (EASE)

Description

Usage

Arguments

Details

Value

References

Examples

Compute Minimum Detectable Relative Risk (MDRR) for rate ratio

Description

Usage

Arguments

Details

Value

References

Examples

Create exposure-outcome combinations.

Description

Usage

Arguments

Details

Value

Examples

Create the results data model tables on a database server.

Description

Usage

Arguments

Details

Value

Examples

Create a parameter object for the function runSelfControlledCohort

Description

Usage

Arguments

Details

Value

Examples

Create a SelfControlledCohort analysis specification

Description

Usage

Arguments

Details

Value

Examples

Create Self-Controlled Cohort Module Specifications

Description

Usage

Arguments

Value

Examples

Execute Self-Controlled Cohort Analyses for Strategus

Description

Usage

Arguments

Details

Value

Examples

Get database migrations instance

Description

Usage

Arguments

Value

See Also

Examples

Get default diagnostic thresholds

Description

Usage

Details

Value

Examples

Get Default export manager

Description

Usage

Arguments

Value

Examples

Get diagnostics summary

Description