Package 'OhdsiReportGenerator'

Description

An internal function to determine the version of CohortMethod used to store results

Usage

.getCmVersion(connectionHandler, schema, cmTablePrefix = "cm_")

Arguments

Details

Specify the connectionHandler, the schema and the prefixes. This query will attempt to identify if CohortMethod v6 was used by inspecing the migration table. When the migration_order is >= 3 then v6 of CohortMethod was used.

Value

A integer with the major version number of cohort method

See Also

Other Estimation: .getSccsVersion(), getCMEstimation(), getCmDiagnosticsData(), getCmMetaEstimation(), getCmNegativeControlEstimates(), getCmOutcomes(), getCmPropensityModel(), getCmTable(), getCmTargets(), getSccsDiagnosticsData(), getSccsEstimation(), getSccsMetaEstimation(), getSccsModel(), getSccsNegativeControlEstimates(), getSccsOutcomes(), getSccsTable(), getSccsTargets(), getSccsTimeToEvent(), plotCmEstimates(), plotSccsEstimates()

Description

An internal function to determine the version of SCCS used to store results

Usage

.getSccsVersion(connectionHandler, schema, sccsTablePrefix = "sccs_")

Arguments

Details

Specify the connectionHandler, the schema and the prefixes. This query will attempt to identify if SelfControlledCaseSeries v6.1.4 was used by inspecing the migration table. When the migration_order is >= 3 then v6.1.4 of SelfControlledCaseSeries was used.

Value

A integer with the major version number of cohort method

See Also

Other Estimation: .getCmVersion(), getCMEstimation(), getCmDiagnosticsData(), getCmMetaEstimation(), getCmNegativeControlEstimates(), getCmOutcomes(), getCmPropensityModel(), getCmTable(), getCmTargets(), getSccsDiagnosticsData(), getSccsEstimation(), getSccsMetaEstimation(), getSccsModel(), getSccsNegativeControlEstimates(), getSccsOutcomes(), getSccsTable(), getSccsTargets(), getSccsTimeToEvent(), plotCmEstimates(), plotSccsEstimates()

Description

Finds the four TAR columns and creates a new column called tar that pastes the columns into a nice string

Usage

addTarColumn(data)

Arguments

Details

Create a friendly single tar column

Value

The data data.frame object with the tar column added if seperate TAR columns are found

See Also

Other helper: formatBinaryCovariateName(), getExampleConnectionDetails(), getOutcomeTable(), getTargetTable(), kableDark(), removeSpaces()

Examples

addTarColumn(data.frame(
tarStartWith = 'cohort start',
tarStartOffset = 1,
tarEndWith = 'cohort start',
tarEndOffset = 0
))

Description

A function to add indexes to the Characterization results

Usage

createCharacterizationIndexes(connectionHandler, schema, cTablePrefix = "c_")

Arguments

Details

Specify the connectionHandler, the schema and the prefixes

See Also

Other Indexes: createCohortIndexes(), createIncidenceIndexes(), createSccsIndexes()

Description

A function to add indexes to the Cohort Generator results

Usage

createCohortIndexes(connectionHandler, schema, cgTablePrefix = "cg_")

Arguments

Details

Specify the connectionHandler, the schema and the prefixes

See Also

Other Indexes: createCharacterizationIndexes(), createIncidenceIndexes(), createSccsIndexes()

Description

A function to add indexes to the incidence results

Usage

createIncidenceIndexes(connectionHandler, schema, ciTablePrefix = "ci_")

Arguments

Details

Specify the connectionHandler, the schema and the prefixes

See Also

Other Indexes: createCharacterizationIndexes(), createCohortIndexes(), createSccsIndexes()

Description

Generates a report for a given prediction model design

Usage

createPredictionReport(
  connectionHandler,
  schema,
  plpTablePrefix,
  databaseTablePrefix = plpTablePrefix,
  cgTablePrefix = plpTablePrefix,
  modelDesignId,
  output,
  intermediatesDir = file.path(tempdir(), "plp-prot"),
  outputFormat = "html_document"
)

Arguments

Details

Specify the connection handler to the result database, the schema name and the modelDesignId of interest to generate a html report summarizing the performance of models developed across databases.

Value

An named R list with the elements 'standard' and 'source'

See Also

Other Reporting: generateFullReport(), generateSummaryPredictionReport()

Description

A function to add indexes to the SCCS results

Usage

createSccsIndexes(connectionHandler, schema, sccsTablePrefix = "sccs_")

Arguments

Details

Specify the connectionHandler, the schema and the prefixes

See Also

Other Indexes: createCharacterizationIndexes(), createCohortIndexes(), createIncidenceIndexes()

Description

Removes the long part of the covariate name to make it friendly

Usage

formatBinaryCovariateName(data)

Arguments

Details

Makes the covariateName more friendly and shorter

Value

The data data.frame object with the ovariateName column changed to be more friendly

See Also

Other helper: addTarColumn(), getExampleConnectionDetails(), getOutcomeTable(), getTargetTable(), kableDark(), removeSpaces()

Examples

formatBinaryCovariateName(data.frame(
covariateName = c("fdfgfgf: dgdgff","made up test")
))

Description

Generates a full report from a Strategus analysis

Usage

generateFullReport(
  server,
  username,
  password,
  dbms,
  resultsSchema = NULL,
  targetId = 1,
  outcomeIds = 3,
  comparatorIds = 2,
  indicationIds = NULL,
  restrictTargetToIndications = FALSE,
  cohortNames = c("target name", "outcome name", "comp name"),
  cohortIds = c(1, 3, 2),
  includeCI = TRUE,
  includeCharacterization = TRUE,
  includeCohortMethod = TRUE,
  includeSccs = TRUE,
  includePrediction = TRUE,
  webAPI = NULL,
  authMethod = NULL,
  webApiUsername = NULL,
  webApiPassword = NULL,
  outputLocation,
  outputName = paste0("full_report_", gsub(":", "_", gsub(" ", "_",
    as.character(date()))), ".html"),
  intermediateDir = tempdir(),
  pathToDriver = Sys.getenv("DATABASECONNECTOR_JAR_FOLDER")
)

Arguments

Details

Specify the connection details to the result database and the schema name to generate the full report.

Value

An html document containing the full results for the target, comparators, indications and outcomes specified.

See Also

Other Reporting: createPredictionReport(), generateSummaryPredictionReport()

Description

Generates a summary report for a given targets and outcomes

Usage

generateSummaryPredictionReport(
  connectionHandler,
  schema,
  targetIds = NULL,
  outcomeIds = NULL,
  plpTablePrefix = "plp_",
  databaseTablePrefix = "",
  cgTablePrefix = "cg_",
  outputFolder,
  outputFileName = "plp-summary.html",
  intermediatesDir = file.path(tempdir(), "plp-prot"),
  overwrite = FALSE
)

Arguments

Details

Specify the connection handler to the result database, the schema name and the cohortId of interest to generate a html report summarizing the performance of prediction models in the database.

Value

A html file is created with the summary report

See Also

Other Reporting: createPredictionReport(), generateFullReport()

Description

This function extracts analysis ids, events cohorts, and databases per target from treatment patterns

Usage

getAnalysisCohorts(connectionHandler, schema, tpTablePrefix = "tp_")

Arguments

Details

Specify the connectionHandler and the schema

Value

Returns a data.frame with the columns:

• databaseName a concatinated string of all the database names ran for that analysis

• databaseId a concatinated string of all the database ids ran for that analysis

• analysisId the analysis ids for the treament patterns run

• targetCohortName the target cohort name

• targetCohortId the target cohort unique identifier

• eventCohortList a concatinated string of all the event cohort names ran for that target

• exitCohortList a concatinated string of all the exit cohort names ran for that target

See Also

Other TreatmentPatterns: getEventDuration(), getTreatmentPathways()

Examples

conDet <- getExampleConnectionDetails()

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

cohortAnalysis <- getAnalysisCohorts(
  connectionHandler = connectionHandler,
  schema = "main"
)

Description

A function to extract case series characterization results

Usage

getBinaryCaseSeries(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetId = NULL,
  outcomeId = NULL,
  databaseIds = NULL,
  riskWindowStart = NULL,
  riskWindowEnd = NULL,
  startAnchor = NULL,
  endAnchor = NULL,
  conceptIds = NULL,
  minVal = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the target/outcome cohort IDs

Value

A data.frame with the characterization case series results

See Also

Other Characterization: getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

cs <- getBinaryCaseSeries(
  connectionHandler = connectionHandler, 
  schema = 'main',
  targetId = 1, 
  outcomeId = 3
)

Description

A function to extract non-case and case binary characterization results

Usage

getBinaryRiskFactors(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetId = NULL,
  outcomeId = NULL,
  databaseId = NULL,
  analysisIds = c(3),
  riskWindowStart = NULL,
  riskWindowEnd = NULL,
  startAnchor = NULL,
  endAnchor = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the target/outcome cohort IDs

Value

A data.frame with the characterization results for the cases and non-cases

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

rf <- getBinaryRiskFactors(
  connectionHandler = connectionHandler, 
  schema = 'main',
  targetId = 1, 
  outcomeId = 3
)

Description

This function extracts the specified covariates for the specified targets

Usage

getBinaryTargetBaseline(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetIds = NULL,
  analysisIds = NULL,
  covariateIds = NULL,
  conceptIds = NULL,
  databaseIds = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the target cohort IDs

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

• limitToFirstINDays the

• covariateName the

• covariateId the

• analysisId the

• sumValue the

• averageValue the

See Also

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

Examples

conDet <- getExampleConnectionDetails()

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

btb <- getBinaryTargetBaseline(
connectionHandler = connectionHandler, 
schema = 'main', 
targetIds = 1
)

Description

This function extracts outcome cohort counts across databases in the results for specified target and outcome cohorts.

Usage

getCaseCounts(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetIds = NULL,
  outcomeIds = NULL,
  databaseIds = NULL,
  riskWindowStart = NULL,
  riskWindowEnd = NULL,
  startAnchor = NULL,
  endAnchor = NULL
)

Arguments

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

• databaseId the unique identifier of the database

• targetName the target cohort name

• targetId the target cohort unique identifier

• outcomeName the outcome name

• outcomeId the outcome unique identifier

• rowCount the number of entries in the cohort

• personCount the number of people in the cohort

• 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)

• riskWindowStart the number of days ofset the start anchor that is the start of the time-at-risk

• startAnchor the start anchor is either the target cohort start or cohort end date

• riskWindowEnd the number of days ofset the end anchor that is the end of the time-at-risk

• endAnchor the end anchor is either the target cohort start or cohort end date

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

cc <- getCaseCounts(
connectionHandler = connectionHandler, 
schema = 'main'
)

Description

This function extracts target cohort counts across databases in the results for specified target and outcome cohorts.

Usage

getCaseTargetCounts(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetIds = NULL,
  outcomeIds = NULL,
  databaseIds = NULL
)

Arguments

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

• databaseId the unique identifier of the database

• targetName the target cohort name

• targetId the target cohort unique identifier

• outcomeName the outcome name

• outcomeId the outcome unique identifier

• rowCount the number of entries in the cohort

• personCount the number of people in the cohort

• withoutExcludedPersonCount the number of people in the target ignoring exclusions

• 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)

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

tc <- getCaseTargetCounts(
connectionHandler = connectionHandler, 
schema = 'main'
)

Description

A function to extract cohort aggregate binary feature characterization results

Usage

getCharacterizationCohortBinary(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetIds = NULL,
  databaseIds = NULL,
  minThreshold = 0
)

Arguments

Details

Specify the connectionHandler, the schema and the target cohort ID and database id

Value

A data.frame with the characterization aggregate binary features for a specific cohort and database

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

binCohort <- getCharacterizationCohortBinary(
  connectionHandler = connectionHandler, 
  schema = 'main',
  targetIds = 1, 
  databaseIds = 'eunomia'
)

Description

A function to extract cohort aggregate continuous feature characterization results

Usage

getCharacterizationCohortContinuous(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetIds = NULL,
  databaseIds = NULL,
  minThreshold = 0
)

Arguments

Details

Specify the connectionHandler, the schema and the target cohort ID and database id

Value

A data.frame with the characterization aggregate continuous features for a specific cohort and database

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

conCohort <- getCharacterizationCohortContinuous(
  connectionHandler = connectionHandler, 
  schema = 'main',
  targetIds = 1, 
  databaseIds = 'eunomia'
)

Description

This function extracts the age group feature extraction results for cases and targets corresponding to specified target and outcome cohorts.

Usage

getCharacterizationDemographics(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetId = NULL,
  outcomeId = NULL,
  type = "age"
)

Arguments

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

• databaseId the unique identifier 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)

• riskWindowStart the number of days ofset the start anchor that is the start of the time-at-risk

• startAnchor the start anchor is either the target cohort start or cohort end date

• riskWindowEnd the number of days ofset the end anchor that is the end of the time-at-risk

• endAnchor the end anchor is either the target cohort start or cohort end date

• covariateName the name of the feature

• sumValue the number of cases who have the feature value of 1

• averageValue the mean feature value

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

# example code

conDet <- getExampleConnectionDetails()

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

ageData <- getCharacterizationDemographics(
connectionHandler = connectionHandler, 
schema = 'main'
)

Description

A function to extract the outcomes found in characterization

Usage

getCharacterizationOutcomes(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  cgTablePrefix = "cg_",
  targetId = NULL,
  printTimes = FALSE,
  useDcrc = TRUE,
  useTte = TRUE,
  useRf = TRUE,
  useCs = TRUE
)

Arguments

Details

Specify the connectionHandler, the schema and the prefixes

Value

A data.frame with the characterization outcome cohort ids, names and which characterization analyses the cohorts are used in.

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

cohorts <- getCharacterizationOutcomes(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

A function to extarct the targets found in characterization

Usage

getCharacterizationTargets(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  cgTablePrefix = "cg_",
  printTimes = FALSE,
  useTte = TRUE,
  useDcrc = TRUE,
  useRf = TRUE,
  useTb = TRUE,
  useCs = TRUE
)

Arguments

Details

Specify the connectionHandler, the schema and the prefixes

Value

A data.frame with the characterization target cohort ids, names and which characterization analyses the cohorts are used in.

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

cohorts <- getCharacterizationTargets(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts the cohort method diagnostics that examine whether the analyses were sufficiently powered and checks for different types of bias.

Usage

getCmDiagnosticsData(
  connectionHandler,
  schema,
  cmTablePrefix = "cm_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetIds = NULL,
  outcomeIds = NULL,
  comparatorIds = NULL,
  indicationIds = NULL,
  analysisIds = NULL,
  databaseIds = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the target/comparator/outcome cohort IDs

Value

Returns a data.frame with the columns:

• databaseName the name of the database

• databaseId the unqiue identifier of the database

• analysisId the analysis unique identifier

• description a description of the analysis

• targetName the target cohort name

• targetId the target cohort unique identifier

• comparatorName the comparator cohort name

• comparatorId the comparator cohort unique identifier

• outcomeName the outcome name

• outcomeId the outcome cohort unique identifier

• maxSdm max allowed standardized difference of means when comparing the target to the comparator after PS adjustment for the ballance diagnostic diagnostic to pass.

• sharedMaxSdm max allowed standardized difference of means when comparing the target to the comparator after PS adjustment for the ballance diagnostic diagnostic to pass.

• equipoise the bounds on the preference score to determine whether a subject is in equipoise.

• mdrr the maximum passable minimum detectable relative risk (mdrr) value. If the mdrr is greater than this the diagnostics will fail.

• attritionFraction (depreciated) the minmum attrition before the diagnostics fails.

• ease The expected absolute systematic error (ease) measures residual bias.

• balanceDiagnostic whether the balance diagnostic passed or failed.

• sharedBalanceDiagnostic whether the shared balance diagnostic passed or failed.

• equipoiseDiagnostic whether the equipose diagnostic passed or failed.

• mdrrDiagnostic whether the mdrr (power) diagnostic passed or failed.

• attritionDiagnostic (depreciated) whether the attrition diagnostic passed or failed.

• easeDiagnostic whether the ease diagnostic passed or failed.

• unblindForEvidenceSynthesis whether the results can be unblinded for the meta analysis.

• unblind whether the results can be unblinded.

• summaryValue summary of diagnostics results. FAIL, PASS or number of warnings.

See Also

Other Estimation: .getCmVersion(), .getSccsVersion(), getCMEstimation(), getCmMetaEstimation(), getCmNegativeControlEstimates(), getCmOutcomes(), getCmPropensityModel(), getCmTable(), getCmTargets(), getSccsDiagnosticsData(), getSccsEstimation(), getSccsMetaEstimation(), getSccsModel(), getSccsNegativeControlEstimates(), getSccsOutcomes(), getSccsTable(), getSccsTargets(), getSccsTimeToEvent(), plotCmEstimates(), plotSccsEstimates()

Examples

conDet <- getExampleConnectionDetails()

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

cmDiag <- getCmDiagnosticsData(
  connectionHandler = connectionHandler, 
  schema = 'main',
  targetIds = 1,
  outcomeIds = 3
)

Description

This function extracts the single database cohort method estimates for results that can be unblinded and have a calibrated RR

Usage

getCMEstimation(
  connectionHandler,
  schema,
  cmTablePrefix = "cm_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetIds = NULL,
  outcomeIds = NULL,
  indicationIds = NULL,
  comparatorIds = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the target/comparator/outcome cohort IDs

Value

Returns a data.frame with the columns:

• databaseName the name of the database

• databaseId the unqiue identifier of the database

• analysisId the analysis design unique identifier

• description the analysis design description

• targetName the target cohort name

• targetId the target cohort unique identifier

• comparatorName the comparator cohort name

• comparatorId the comparator cohort unique identifier

• outcomeName the outcome name

• outcomeId the outcome unique identifier

• calibratedRr the calibrated relative risk

• calibratedRrCi95Lb the calibrated relative risk 95 percent confidence interval lower bound

• calibratedRrCi95Ub the calibrated relative risk 95 percent confidence interval upper bound

• calibratedP the two sided calibrated p value

• calibratedOneSidedP the one sided calibrated p value

• calibratedLogRr the calibrated relative risk logged

• calibratedSeLogRr the standard error of the calibrated relative risk logged

• targetSubjects the number of people in the target cohort

• comparatorSubjects the number of people in the comparator cohort

• targetDays the total number of days at risk across the target cohort people

• comparatorDays the total number of days at risk across the comparator cohort people

• targetOutcomes the total number of outcomes occuring during the time at risk for the target cohort people

• comparatorOutcomes the total number of outcomes occuring during the time at risk for the comparator cohort people

• Unblind Whether the results passed diagnostics and were unblinded

• unblindForEvidenceSynthesis whether the results can be unblinded for the meta analysis.

• targetEstimator ...

See Also

Other Estimation: .getCmVersion(), .getSccsVersion(), getCmDiagnosticsData(), getCmMetaEstimation(), getCmNegativeControlEstimates(), getCmOutcomes(), getCmPropensityModel(), getCmTable(), getCmTargets(), getSccsDiagnosticsData(), getSccsEstimation(), getSccsMetaEstimation(), getSccsModel(), getSccsNegativeControlEstimates(), getSccsOutcomes(), getSccsTable(), getSccsTargets(), getSccsTimeToEvent(), plotCmEstimates(), plotSccsEstimates()

Examples

conDet <- getExampleConnectionDetails()

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

cmEst <- getCMEstimation(
  connectionHandler = connectionHandler, 
  schema = 'main',
  targetIds = 1,
  outcomeIds = 3
)

Description

This function extracts any meta analysis estimation results for cohort method.

Usage

getCmMetaEstimation(
  connectionHandler,
  schema,
  cmTablePrefix = "cm_",
  cgTablePrefix = "cg_",
  esTablePrefix = "es_",
  targetIds = NULL,
  outcomeIds = NULL,
  indicationIds = NULL,
  comparatorIds = NULL,
  includeOneSidedP = TRUE
)

Arguments

Details

Specify the connectionHandler, the schema and the target/comparator/outcome cohort IDs

Value

Returns a data.frame with the columns:

• databaseName the name of the database

• analysisId the analysis unique identifier

• description a description of the analysis

• targetName the target cohort name

• targetId the target cohort unique identifier

• comparatorName the comparator cohort name

• comparatorId the comparator cohort unique identifier

• outcomeName the outcome name

• outcomeId the outcome cohort unique identifier

• calibratedRr the calibrated relative risk

• calibratedRrCi95Lb the calibrated relative risk 95 percent confidence interval lower bound

• calibratedRrCi95Ub the calibrated relative risk 95 percent confidence interval upper bound

• calibratedP the two sided calibrated p value

• calibratedOneSidedP the one sided calibrated p value

• calibratedLogRr the calibrated relative risk logged

• calibratedSeLogRr the standard error of the calibrated relative risk logged

• targetSubjects the number of people in the target cohort across included database

• comparatorSubjects the number of people in the comparator cohort across included database

• targetDays the total number of days at risk across the target cohort people across included database

• comparatorDays the total number of days at risk across the comparator cohort people across included database

• targetOutcomes the total number of outcomes occuring during the time at risk for the target cohort people across included database

• comparatorOutcomes the total number of outcomes occuring during the time at risk for the comparator cohort people across included database

• unblind whether the results can be unblinded.

• nDatabases the number of databases included

See Also

Other Estimation: .getCmVersion(), .getSccsVersion(), getCMEstimation(), getCmDiagnosticsData(), getCmNegativeControlEstimates(), getCmOutcomes(), getCmPropensityModel(), getCmTable(), getCmTargets(), getSccsDiagnosticsData(), getSccsEstimation(), getSccsMetaEstimation(), getSccsModel(), getSccsNegativeControlEstimates(), getSccsOutcomes(), getSccsTable(), getSccsTargets(), getSccsTimeToEvent(), plotCmEstimates(), plotSccsEstimates()

Examples

conDet <- getExampleConnectionDetails()

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

cmMeta <- getCmMetaEstimation(
  connectionHandler = connectionHandler, 
  schema = 'main',
  targetIds = 1,
  outcomeIds = 3
)

Description

This function extracts the cohort method negative control table.

Usage

getCmNegativeControlEstimates(
  connectionHandler,
  schema,
  cmTablePrefix = "cm_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetIds = NULL,
  comparatorIds = NULL,
  indicationIds = NULL,
  analysisIds = NULL,
  databaseIds = NULL,
  excludePositiveControls = TRUE
)

Arguments

Details

Specify the connectionHandler, the schema and optionally the target/comparator/outcome/analysis/database IDs

Value

Returns a data.frame with the cohort method negative controls

See Also

Other Estimation: .getCmVersion(), .getSccsVersion(), getCMEstimation(), getCmDiagnosticsData(), getCmMetaEstimation(), getCmOutcomes(), getCmPropensityModel(), getCmTable(), getCmTargets(), getSccsDiagnosticsData(), getSccsEstimation(), getSccsMetaEstimation(), getSccsModel(), getSccsNegativeControlEstimates(), getSccsOutcomes(), getSccsTable(), getSccsTargets(), getSccsTimeToEvent(), plotCmEstimates(), plotSccsEstimates()

Examples

conDet <- getExampleConnectionDetails()

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

cmNc <- getCmNegativeControlEstimates(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

A function to extract the outcomes found in cohort method

Usage

getCmOutcomes(
  connectionHandler,
  schema,
  cmTablePrefix = "cm_",
  cgTablePrefix = "cg_",
  targetId = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the prefixes

Value

A data.frame with the cohort method outcome ids and names.

See Also

Other Estimation: .getCmVersion(), .getSccsVersion(), getCMEstimation(), getCmDiagnosticsData(), getCmMetaEstimation(), getCmNegativeControlEstimates(), getCmPropensityModel(), getCmTable(), getCmTargets(), getSccsDiagnosticsData(), getSccsEstimation(), getSccsMetaEstimation(), getSccsModel(), getSccsNegativeControlEstimates(), getSccsOutcomes(), getSccsTable(), getSccsTargets(), getSccsTimeToEvent(), plotCmEstimates(), plotSccsEstimates()

Examples

conDet <- getExampleConnectionDetails()

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

outcomes <- getCmOutcomes(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts the cohort method model.

Usage

getCmPropensityModel(
  connectionHandler,
  schema,
  cmTablePrefix = "cm_",
  targetId = NULL,
  comparatorId = NULL,
  indicationId = NULL,
  analysisId = NULL,
  databaseId = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and optionally the target/comparator/analysis/database IDs

Value

Returns a data.frame with the cohort method model

See Also

Other Estimation: .getCmVersion(), .getSccsVersion(), getCMEstimation(), getCmDiagnosticsData(), getCmMetaEstimation(), getCmNegativeControlEstimates(), getCmOutcomes(), getCmTable(), getCmTargets(), getSccsDiagnosticsData(), getSccsEstimation(), getSccsMetaEstimation(), getSccsModel(), getSccsNegativeControlEstimates(), getSccsOutcomes(), getSccsTable(), getSccsTargets(), getSccsTimeToEvent(), plotCmEstimates(), plotSccsEstimates()

Examples

conDet <- getExampleConnectionDetails()

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

cmModel <- getCmPropensityModel(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts the specific cohort method table.

Usage

getCmTable(
  connectionHandler,
  schema,
  table = c("attrition", "follow_up_dist", "interaction_result", "covariate_balance",
    "kaplan_meier_dist", "likelihood_profile", "preference_score_dist",
    "propensity_model", "shared_covariate_balance")[1],
  cmTablePrefix = "cm_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetIds = NULL,
  indicationIds = NULL,
  outcomeIds = NULL,
  comparatorIds = NULL,
  analysisIds = NULL,
  databaseIds = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and optionally the target/comparator/outcome/analysis/database IDs

Value

Returns a data.frame with the cohort method requested table

See Also

Other Estimation: .getCmVersion(), .getSccsVersion(), getCMEstimation(), getCmDiagnosticsData(), getCmMetaEstimation(), getCmNegativeControlEstimates(), getCmOutcomes(), getCmPropensityModel(), getCmTargets(), getSccsDiagnosticsData(), getSccsEstimation(), getSccsMetaEstimation(), getSccsModel(), getSccsNegativeControlEstimates(), getSccsOutcomes(), getSccsTable(), getSccsTargets(), getSccsTimeToEvent(), plotCmEstimates(), plotSccsEstimates()

Examples

conDet <- getExampleConnectionDetails()

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

cmTable <- getCmTable(
  connectionHandler = connectionHandler, 
  schema = 'main',
  table = 'attrition'
)

Description

A function to extract the targets found in cohort method

Usage

getCmTargets(
  connectionHandler,
  schema,
  cmTablePrefix = "cm_",
  cgTablePrefix = "cg_"
)

Arguments

Details

Specify the connectionHandler, the schema and the prefixes

Value

A data.frame with the cohort method target cohort ids and names.

See Also

Other Estimation: .getCmVersion(), .getSccsVersion(), getCMEstimation(), getCmDiagnosticsData(), getCmMetaEstimation(), getCmNegativeControlEstimates(), getCmOutcomes(), getCmPropensityModel(), getCmTable(), getSccsDiagnosticsData(), getSccsEstimation(), getSccsMetaEstimation(), getSccsModel(), getSccsNegativeControlEstimates(), getSccsOutcomes(), getSccsTable(), getSccsTargets(), getSccsTimeToEvent(), plotCmEstimates(), plotSccsEstimates()

Examples

conDet <- getExampleConnectionDetails()

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

cohorts <- getCmTargets(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

Retrieves attrition information for specified cohorts from the database.

Usage

getCohortAttrition(
  connectionHandler,
  schema,
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  cohortIds = NULL
)

Arguments

Value

A data.frame with attrition details for each cohort.

See Also

Other Cohorts: getCohortCounts(), getCohortDefinitions(), getCohortInclusionRules(), getCohortInclusionStats(), getCohortInclusionSummary(), getCohortMeta(), getCohortSubsetAttrition(), getCohortSubsetDefinitions(), getSubsetText(), processCohortDefinitionsForQuarto(), processCohorts(), restrictCohortDefinitionsForQuarto()

Description

This function extracts all cohort counts for the cohorts of interest.

Usage

getCohortCounts(
  connectionHandler,
  schema,
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  cohortIds = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the cohort IDs

Value

Returns a data.frame with the cohort inclusion rules

See Also

Other Cohorts: getCohortAttrition(), getCohortDefinitions(), getCohortInclusionRules(), getCohortInclusionStats(), getCohortInclusionSummary(), getCohortMeta(), getCohortSubsetAttrition(), getCohortSubsetDefinitions(), getSubsetText(), processCohortDefinitionsForQuarto(), processCohorts(), restrictCohortDefinitionsForQuarto()

Examples

conDet <- getExampleConnectionDetails()

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

cohortMeta <- getCohortCounts(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts all cohort definitions for the targets of interest.

Usage

getCohortDefinitions(
  connectionHandler,
  schema,
  cgTablePrefix = "cg_",
  targetIds = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the target cohort IDs

Value

Returns a data.frame with the cohort details

See Also

Other Cohorts: getCohortAttrition(), getCohortCounts(), getCohortInclusionRules(), getCohortInclusionStats(), getCohortInclusionSummary(), getCohortMeta(), getCohortSubsetAttrition(), getCohortSubsetDefinitions(), getSubsetText(), processCohortDefinitionsForQuarto(), processCohorts(), restrictCohortDefinitionsForQuarto()

Examples

conDet <- getExampleConnectionDetails()

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

cohortDef <- getCohortDefinitions(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts all cohort inclusion rules for the cohorts of interest.

Usage

getCohortInclusionRules(
  connectionHandler,
  schema,
  cgTablePrefix = "cg_",
  cohortIds = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the cohort IDs

Value

Returns a data.frame with the cohort inclusion rules

See Also

Other Cohorts: getCohortAttrition(), getCohortCounts(), getCohortDefinitions(), getCohortInclusionStats(), getCohortInclusionSummary(), getCohortMeta(), getCohortSubsetAttrition(), getCohortSubsetDefinitions(), getSubsetText(), processCohortDefinitionsForQuarto(), processCohorts(), restrictCohortDefinitionsForQuarto()

Examples

conDet <- getExampleConnectionDetails()

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

cohortInclsuionsRules <- getCohortInclusionRules(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts all cohort inclusion stats for the cohorts of interest.

Usage

getCohortInclusionStats(
  connectionHandler,
  schema,
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  cohortIds = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the cohort IDs

Value

Returns a data.frame with the cohort inclusion stats

See Also

Other Cohorts: getCohortAttrition(), getCohortCounts(), getCohortDefinitions(), getCohortInclusionRules(), getCohortInclusionSummary(), getCohortMeta(), getCohortSubsetAttrition(), getCohortSubsetDefinitions(), getSubsetText(), processCohortDefinitionsForQuarto(), processCohorts(), restrictCohortDefinitionsForQuarto()

Examples

conDet <- getExampleConnectionDetails()

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

cohortInclsuionsStats <- getCohortInclusionStats(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts all cohort inclusion summary for the cohorts of interest.

Usage

getCohortInclusionSummary(
  connectionHandler,
  schema,
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  cohortIds = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the cohort IDs

Value

Returns a data.frame with the cohort inclusion rules

See Also

Other Cohorts: getCohortAttrition(), getCohortCounts(), getCohortDefinitions(), getCohortInclusionRules(), getCohortInclusionStats(), getCohortMeta(), getCohortSubsetAttrition(), getCohortSubsetDefinitions(), getSubsetText(), processCohortDefinitionsForQuarto(), processCohorts(), restrictCohortDefinitionsForQuarto()

Examples

conDet <- getExampleConnectionDetails()

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

cohortInclsuionsSummary <- getCohortInclusionSummary(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts all cohort meta for the cohorts of interest.

Usage

getCohortMeta(
  connectionHandler,
  schema,
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  cohortIds = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the cohort IDs

Value

Returns a data.frame with the cohort inclusion rules

See Also

Other Cohorts: getCohortAttrition(), getCohortCounts(), getCohortDefinitions(), getCohortInclusionRules(), getCohortInclusionStats(), getCohortInclusionSummary(), getCohortSubsetAttrition(), getCohortSubsetDefinitions(), getSubsetText(), processCohortDefinitionsForQuarto(), processCohorts(), restrictCohortDefinitionsForQuarto()

Examples

conDet <- getExampleConnectionDetails()

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

cohortMeta <- getCohortMeta(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

Retrieves attrition information for specified cohort subsets from the database.

Usage

getCohortSubsetAttrition(
  connectionHandler,
  schema,
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  cohortIds = NULL
)

Arguments

Value

A data.frame with attrition details for each cohort subset.

See Also

Other Cohorts: getCohortAttrition(), getCohortCounts(), getCohortDefinitions(), getCohortInclusionRules(), getCohortInclusionStats(), getCohortInclusionSummary(), getCohortMeta(), getCohortSubsetDefinitions(), getSubsetText(), processCohortDefinitionsForQuarto(), processCohorts(), restrictCohortDefinitionsForQuarto()

Description

This function extracts all cohort subset definitions for the subsets of interest.

Usage

getCohortSubsetDefinitions(
  connectionHandler,
  schema,
  cgTablePrefix = "cg_",
  subsetIds = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the subset IDs

Value

Returns a data.frame with the cohort subset details

See Also

Other Cohorts: getCohortAttrition(), getCohortCounts(), getCohortDefinitions(), getCohortInclusionRules(), getCohortInclusionStats(), getCohortInclusionSummary(), getCohortMeta(), getCohortSubsetAttrition(), getSubsetText(), processCohortDefinitionsForQuarto(), processCohorts(), restrictCohortDefinitionsForQuarto()

Examples

conDet <- getExampleConnectionDetails()

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

subsetDef <- getCohortSubsetDefinitions(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

A function to extract case series continuous feature characterization results

Usage

getContinuousCaseSeries(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetId = NULL,
  outcomeId = NULL,
  databaseIds = NULL,
  riskWindowStart = NULL,
  riskWindowEnd = NULL,
  startAnchor = NULL,
  endAnchor = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the target/outcome cohort IDs

Value

A data.frame with the characterization case series results

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

cs <- getContinuousCaseSeries(
  connectionHandler = connectionHandler, 
  schema = 'main',
  targetId = 1, 
  outcomeId = 3
)

Description

A function to extract non-case and case continuous characterization results

Usage

getContinuousRiskFactors(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetId = NULL,
  outcomeId = NULL,
  analysisIds = NULL,
  databaseIds = NULL,
  riskWindowStart = NULL,
  riskWindowEnd = NULL,
  startAnchor = NULL,
  endAnchor = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the target/outcome cohort IDs

Value

A data.frame with the characterization results for the cases and non-cases

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

rf <- getContinuousRiskFactors(
  connectionHandler = connectionHandler, 
  schema = 'main',
  targetId = 1, 
  outcomeId = 3
)

Description

This function extracts the databases and their information.

Usage

getDatabaseDetails(
  connectionHandler,
  schema,
  databaseTable = "database_meta_data"
)

Arguments

Details

Specify the connectionHandler, the schema and the database table name

Value

Returns a data.frame with the columns:

• databaseFullName the full name of the database

• databaseName the friendly name of the database

• cdmHolder the license holder of the database

• sourceDescription a description of the database

• sourceDocumentationReference a link to the database information document

• cdmEtlReference a link to the ETL document

• sourceReleaseDatethe release date for the source database

• (cdmReleaseDate the release date for the database mapped to the OMOP CDM)

• cdmVersion the OMOP CDM version of the database

• cdmVersionConceptId the CDM version concept ID

• vocabularyVersion the database's vocabulary version

• databaseId a unique identifier for the database

• maxObsPeriodEndDate the last observational period end date in the database

Examples

conDet <- getExampleConnectionDetails()

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

ir <- getIncidenceRates(
connectionHandler = connectionHandler, 
schema = 'main'
)

Description

This function extracts all dechallenge rechallenge results across databases for specified target and outcome cohorts.

Usage

getDechallengeRechallenge(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetIds = NULL,
  outcomeIds = NULL
)

Arguments

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

• databaseId the unique identifier of the database

• targetName the target cohort name

• targetId the target cohort unique identifier

• outcomeName the outcome name

• outcomeId the outcome unique identifier

• 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 A period of time evaluated for outcome recurrence after discontinuation of exposure, among patients with challenge outcomes

• numExposureEras Distinct number of exposure events (i.e. drug eras) in a given target cohort

• numPersonsExposed Distinct number of people exposed in target cohort. A person must have at least 1 day exposure to be included

• numCases Distinct number of persons in outcome cohort. A person must have at least 1 day of observation time to be included

• dechallengeAttempt Distinct count of people with observable time after discontinuation of the exposure era during which the challenge outcome occurred

• dechallengeFail Among people with challenge outcomes, the distinct number of people with outcomes during dechallengeEvaluationWindow

• dechallengeSuccess Among people with challenge outcomes, the distinct number of people without outcomes during the dechallengeEvaluationWindow

• rechallengeAttempt Number of people with a new exposure era after the occurrence of an outcome during a prior exposure era

• rechallengeFail Number of people with a new exposure era during which an outcome occurred, after the occurrence of an outcome during a prior exposure era

• rechallengeSuccess Number of people with a new exposure era during which an outcome did not occur, after the occurrence of an outcome during a prior exposure era

• pctDechallengeAttempt Percent of people with observable time after discontinuation of the exposure era during which the challenge outcome occurred

• pctDechallengeFail Among people with challenge outcomes, the percent of people without outcomes during the dechallengeEvaluationWindow

• pctDechallengeSuccess Among people with challenge outcomes, the percent of people with outcomes during dechallengeEvaluationWindow

• pctRechallengeAttempt Percent of people with a new exposure era after the occurrence of an outcome during a prior exposure era

• pctRechallengeFail Percent of people with a new exposure era during which an outcome did not occur, after the occurrence of an outcome during a prior exposure era

• pctRechallengeSuccess Percent of people with a new exposure era during which an outcome occurred, after the occurrence of an outcome during a prior exposure era

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

dcrc <- getDechallengeRechallenge(
connectionHandler = connectionHandler, 
schema = 'main'
)

Description

A function to extract the failed dechallenge-rechallenge cases

Usage

getDechallengeRechallengeFails(
  connectionHandler,
  schema,
  cTablePrefix = "c_",
  targetId = NULL,
  outcomeId = NULL,
  databaseId = NULL,
  dechallengeStopInterval = NULL,
  dechallengeEvaluationWindow = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the target/outcome cohort IDs and database id

Value

A data.frame each failed dechallenge rechallenge exposures and outcomes

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getIncidenceOutcomes(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

conCohort <- getDechallengeRechallengeFails(
  connectionHandler = connectionHandler, 
  schema = 'main',
  targetId = 1, 
  outcomeId = 3,
  databaseId = 'eunomia'
)

Description

This function extracts results summary stats of event duration for specified analysis ids and target cohorts

Usage

getEventDuration(
  connectionHandler,
  schema,
  analysisIds,
  tpTablePrefix = "tp_",
  databaseTable = "database_meta_data",
  databaseIds = NULL,
  databaseNames = NULL,
  targetIds = NULL
)

Arguments

Details

Specify the connectionHandler, the schema, and the analysisIds

Value

Returns a data.frame with the columns:

• databaseName the name of the database

• databaseId the unique identifier of the database

• analysisId the unique identifier of a treament patterns run

• targetCohortId the target cohort unique identifier

• targetCohortName the target cohort name

• eventName a string representing an events for a target. Uses '+' for combination of event cohorts

• rank the step number of event occurance

• eventCount the count of event occurance at rank

• durationAverage the average duration of event

• durationMax the maximum duration of event

• durationMin the minimum duration of event

• durationMedian the median duration of event

• p25Value the 25th percentile for duration of event

• p75Value the 75th percentile for duration of event

• standardDeviation the standard deviation for duration of event

See Also

Other TreatmentPatterns: getAnalysisCohorts(), getTreatmentPathways()

Examples

conDet <- getExampleConnectionDetails()

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

ed <- getEventDuration(
  connectionHandler = connectionHandler,
  schema = "main",
  analysisIds = c(1)
)

Description

This returns an object of class 'ConnectionDetails' that lets you connect via 'DatabaseConnector::connect()' to the example result database.

Usage

getExampleConnectionDetails(exdir = tempdir())

Arguments

Details

Finds the location of the example result 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 result database

See Also

Other helper: addTarColumn(), formatBinaryCovariateName(), getOutcomeTable(), getTargetTable(), kableDark(), removeSpaces()

Examples

conDet <- getExampleConnectionDetails()

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

Description

This function extracts the model performances per evaluation

Usage

getFullPredictionPerformances(
  connectionHandler,
  schema,
  plpTablePrefix = "plp_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  databaseTablePrefix = "",
  modelDesignId = NULL,
  developmentDatabaseId = NULL
)

Arguments

Details

Specify the connectionHandler, the resultDatabaseSettings and (optionally) a modelDesignId and/or developmentDatabaseId to restrict models to

Value

Returns a data.frame with the columns:

• timeStamp the date/time when the analysis occurred

• performanceId the unique identifier for the performance

• modelDesignId the unique identifier for the model design

• modelType the type of classifier

• algorithmName the model algorithm name

• covariateName a summary name for the candidate covariates

• developmentDatabaseId the unique identifier for the database used to develop the model

• validationDatabaseId the unique identifier for the database used to validate the model

• developmentTargetId the unique cohort id for the development target population

• developmentTargetName the name for the development target population

• validationTargetId the id for the validation target population

• validationTargetName the name for the validation target population if different from development

• developmentOutcomeId the unique cohort id for the development outcome

• developmentOutcomeName the name for the development outcome

• validationOutcomeId the id for the validation outcome

• validationOutcomeName the name for the validation outcome if different from development

• developmentDatabase the name for the database used to develop the model

• validationDatabase the name for the database used to validate the model if different from development

• validationTarId the validation time at risk id

• validationTimeAtRisk the time at risk used when evaluating the model if different from development

• developmentTarId the development time at risk id

• developmentTimeAtRisk the time at risk used when developing the model

• evaluation The type of evaluation: Test/Train/CV/Validation

• populationSize the test/validation population size used to develop the model

• outcomeCount the test/validation outcome count used to develop the model

• AUROC the AUROC value for the model

• 95 lower AUROC: the lower bound of the 95 percent CI AUROC value for the model

• 95 upper AUROC: the upper bound of the 95 percent CI AUROC value for the model

• AUPRC the discrimination AUPRC value for the model

• brier score: the brier value for the model

• brier score scaled: the scaled brier value for the model

• Average Precision: the average precision value for the model

• Eavg the calibration average error e-statistic value for the model

• E90 the calibration 90 percent upper bound e-statistic value for the model

• Emax the calibration max error e-statistic value for the model

• calibrationInLarge mean prediction: the calibration in the large mean predicted risk value for the model

• calibrationInLarge observed risk: the calibration in the large mean observed risk value for the model

• calibrationInLarge intercept: the calibration in the large value intercept for the model

• weak calibration intercept: the weak calibration intercept for the model

• weak calibration gradient: the weak calibration gradient for the model

• Hosmer Lemeshow calibration intercept: the Hosmer Lemeshow calibration intercept for the model

• Hosmer Lemeshow calibration gradient: the Hosmer Lemeshow calibration gradient for the model

• ... Additional metrics that are added to PLP

See Also

Other Prediction: getPredictionAggregateTopPredictors(), getPredictionCohorts(), getPredictionCovariates(), getPredictionDiagnosticTable(), getPredictionDiagnostics(), getPredictionHyperParamSearch(), getPredictionIntercept(), getPredictionLift(), getPredictionModelDesigns(), getPredictionOutcomes(), getPredictionPerformanceTable(), getPredictionPerformances(), getPredictionTargets(), getPredictionTopPredictors()

Examples

conDet <- getExampleConnectionDetails()

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

perf <- getFullPredictionPerformances(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

A function to extract the outcomes found in incidence

Usage

getIncidenceOutcomes(
  connectionHandler,
  schema,
  ciTablePrefix = "ci_",
  cgTablePrefix = "cg_",
  targetId = NULL
)

Arguments

Details

Specify the connectionHandler, the schema and the prefixes

Value

A data.frame with the incidence outcome cohort ids and names

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceRates(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

outcomes <- getIncidenceOutcomes(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts all incidence rates across databases in the results for specified target and outcome cohorts.

Usage

getIncidenceRates(
  connectionHandler,
  schema,
  ciTablePrefix = "ci_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  targetIds = NULL,
  outcomeIds = NULL
)

Arguments

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

• databaseId the unique id of the database

• targetName the target cohort name

• targetId the target cohort unique identifier

• outcomeName the outcome name

• outcomeId the outcome unique identifier

• (tar the friendly time-at-risk string)

• cleanWindow clean windown around outcome

• subgroupName name for the result subgroup

• ageGroupName name for the result age group

• genderName name for the result gender group

• startYear name for the result start year

• tarStartWith time at risk start reference

• tarStartOffset time at risk start offset from reference

• tarEndWith time at risk end reference

• tarEndOffset time at risk end offset from reference

• personsAtRiskPe persons at risk per event

• personsAtRisk persons at risk

• personDaysPe person days per event

• personDays person days

• personOutcomesPe person outcome per event

• personOutcomes persons outcome

• outcomesPe number of outcome per event

• outcomes number of outcome

• incidenceProportionP100p incidence proportion per 100 persons

• incidenceRateP100py incidence rate per 100 person years

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceTargets(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

ir <- getIncidenceRates(
connectionHandler = connectionHandler, 
schema = 'main'
)

Description

A function to extract the targets found in incidence

Usage

getIncidenceTargets(
  connectionHandler,
  schema,
  ciTablePrefix = "ci_",
  cgTablePrefix = "cg_"
)

Arguments

Details

Specify the connectionHandler, the schema and the prefixes

Value

A data.frame with the incidence target cohort ids and names

See Also

Other Characterization: getBinaryCaseSeries(), getBinaryRiskFactors(), getBinaryTargetBaseline(), getCaseCounts(), getCaseTargetCounts(), getCharacterizationCohortBinary(), getCharacterizationCohortContinuous(), getCharacterizationDemographics(), getCharacterizationOutcomes(), getCharacterizationTargets(), getContinuousCaseSeries(), getContinuousRiskFactors(), getDechallengeRechallenge(), getDechallengeRechallengeFails(), getIncidenceOutcomes(), getIncidenceRates(), getTargetBinaryFeatures(), getTargetContinuousFeatures(), getTimeToEvent(), plotAgeDistributions(), plotSexDistributions(), viewIncidenceRate()

Examples

conDet <- getExampleConnectionDetails()

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

cohorts <- getIncidenceTargets(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts the outcome cohorts, the number of subjects/entries and where the cohort was used.

Usage

getOutcomeTable(
  connectionHandler,
  schema,
  cgTablePrefix = "cg_",
  cTablePrefix = "c_",
  ciTablePrefix = "ci_",
  cmTablePrefix = "cm_",
  sccsTablePrefix = "sccs_",
  plpTablePrefix = "plp_",
  databaseTable = "database_meta_data",
  targetId = NULL,
  getIncidenceInclusion = TRUE,
  getCharacterizationInclusion = TRUE,
  getPredictionInclusion = TRUE,
  getCohortMethodInclusion = TRUE,
  getSccsInclusion = TRUE,
  printTimes = FALSE
)

Arguments

Details

Specify the connectionHandler, the schema and the table prefixes

Value

Returns a data.frame with the columns:

• cohortId the number id for the target cohort

• cohortName the name of the cohort

• subsetParent the number id of the parent cohort

• subsetDefinitionId the number id of the subset

• numDatabase number of databases with the cohort

• databaseString all the names of the databases with the cohort

• databaseIdString all the ids of the databases with the cohort

• databaseStringCount all the names of the databases with the cohort plus their counts

• databaseCount all the names of the databases with the cohort and their sizes

• minSubjectCount number of subjects in databases with lowest count

• maxSubjectCount number of subjects in databases with highest count

• minEntryCount number of entries in databases with lowest count

• maxEntryCount number of entries in databases with highest count

• cohortIncidence whether the cohort was used in cohort incidence

• dechalRechal whether the cohort was used in dechallenge rechallenge

• riskFactors whether the cohort was used in risk factors

• caseSeries whether the cohort was used in case series analysis

• timeToEvent whether the cohort was used in time to event

• prediction whether the cohort was used in prediction

• cohortMethod whether the cohort was used in cohort method

• selfControlledCaseSeries whether the cohort was used in self controlled case series

See Also

Other helper: addTarColumn(), formatBinaryCovariateName(), getExampleConnectionDetails(), getTargetTable(), kableDark(), removeSpaces()

Examples

conDet <- getExampleConnectionDetails()

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

outcomeTable <- getOutcomeTable(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts the top N predictors across models by finding the sum of the absolute coefficient value across models.

Usage

getPredictionAggregateTopPredictors(
  connectionHandler,
  schema,
  plpTablePrefix = "plp_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  modelDesignIds = NULL
)

Arguments

Details

Specify the connectionHandler, the resultDatabaseSettings and (optionally) any modelDesignIds to restrict to

Value

Returns a data.frame with the columns:

• databaseName the name of the database the model was developed on

• tarStartDay the time-at-risk start day

• tarStartAnchor whether the time-at-risk start is relative to cohort start or end

• tarEndDay the time-at-risk end day

• tarEndAnchor whether the time-at-risk end is relative to cohort start or end

• covariateId the FeatureExtraction covariate identifier

• covariateName the name of the covariate

• conceptId the covariates corresponding concept or 0

• sumCovariateValue the total absolute feature importance or coefficient value

• numberOfTimesPredictive number of models that contained the covariate

See Also

Other Prediction: getFullPredictionPerformances(), getPredictionCohorts(), getPredictionCovariates(), getPredictionDiagnosticTable(), getPredictionDiagnostics(), getPredictionHyperParamSearch(), getPredictionIntercept(), getPredictionLift(), getPredictionModelDesigns(), getPredictionOutcomes(), getPredictionPerformanceTable(), getPredictionPerformances(), getPredictionTargets(), getPredictionTopPredictors()

Examples

conDet <- getExampleConnectionDetails()

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

topPreds <- getPredictionAggregateTopPredictors(
  connectionHandler = connectionHandler, 
  schema = 'main',
  modelDesignIds = c(1,2,5)
)

Description

This function extracts the target and outcome cohorts used to develop any model in the results

Usage

getPredictionCohorts(
  connectionHandler,
  schema,
  plpTablePrefix = "plp_",
  cgTablePrefix = "cg_"
)

Arguments

Details

Specify the connectionHandler, the resultDatabaseSettings and any targetIds or outcomeIds to restrict models to

Value

Returns a data.frame with the columns:

• cohortId the cohort definition ID

• cohortName the name of the cohort

• type whether the cohort was used as a target or outcome cohort

See Also

Other Prediction: getFullPredictionPerformances(), getPredictionAggregateTopPredictors(), getPredictionCovariates(), getPredictionDiagnosticTable(), getPredictionDiagnostics(), getPredictionHyperParamSearch(), getPredictionIntercept(), getPredictionLift(), getPredictionModelDesigns(), getPredictionOutcomes(), getPredictionPerformanceTable(), getPredictionPerformances(), getPredictionTargets(), getPredictionTopPredictors()

Examples

conDet <- getExampleConnectionDetails()

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

predCohorts <- getPredictionCohorts(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts the covariate summary details

Usage

getPredictionCovariates(
  connectionHandler,
  schema,
  plpTablePrefix = "plp_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  performanceIds = NULL,
  modelDesignIds = NULL
)

Arguments

Details

Specify the connectionHandler, the resultDatabaseSettings, the table of interest and (optionally) modelDesignIds/performanceIds to filter to

Value

Returns a data.frame with the specified table

See Also

Other Prediction: getFullPredictionPerformances(), getPredictionAggregateTopPredictors(), getPredictionCohorts(), getPredictionDiagnosticTable(), getPredictionDiagnostics(), getPredictionHyperParamSearch(), getPredictionIntercept(), getPredictionLift(), getPredictionModelDesigns(), getPredictionOutcomes(), getPredictionPerformanceTable(), getPredictionPerformances(), getPredictionTargets(), getPredictionTopPredictors()

Examples

conDet <- getExampleConnectionDetails()

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

covs <- getPredictionCovariates(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts the PROBAST diagnostics

Usage

getPredictionDiagnostics(
  connectionHandler,
  schema,
  plpTablePrefix = "plp_",
  cgTablePrefix = "cg_",
  databaseTable = "database_meta_data",
  modelDesignIds = NULL,
  threshold1_2 = 0.9
)

Arguments

Details

Specify the connectionHandler, the resultDatabaseSettings and (optionally) a modelDesignId and threshold1_2 a threshold value to use for the PROBAST 1.2

Value

Returns a data.frame with the columns:

• modelDesignId the unique identifier for the model design

• diagnosticId the unique identifier for diagnostic result

• developmentDatabaseName the name for the database used to develop the model

• developmentTargetName the name for the development target population

• developmentOutcomeName the name for the development outcome

• probast1_1 Were appropriate data sources used, e.g., cohort, RCT, or nested case-control study data?

• probast1_2 Were all inclusions and exclusions of paticipants appropriate?

• probast2_1 Were predictors defined and assessed in a similar way for all participants?

• probast2_2 Were predictors assessments made without knowledge of outcome data?

• probast2_3 All all predictors available at the time the model is intended to be used?

• probast3_4 Was the outcome defined and determined in a similar way for all participants?

• probast3_6 Was the time interval between predictor assessment and outcome determination appropriate?

• probast4_1 Were there a reasonable number of participants with the outcome?

See Also

Other Prediction: getFullPredictionPerformances(), getPredictionAggregateTopPredictors(), getPredictionCohorts(), getPredictionCovariates(), getPredictionDiagnosticTable(), getPredictionHyperParamSearch(), getPredictionIntercept(), getPredictionLift(), getPredictionModelDesigns(), getPredictionOutcomes(), getPredictionPerformanceTable(), getPredictionPerformances(), getPredictionTargets(), getPredictionTopPredictors()

Examples

conDet <- getExampleConnectionDetails()

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

diag <- getPredictionDiagnostics(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts the specified diagnostic table

Usage

getPredictionDiagnosticTable(
  connectionHandler,
  schema,
  plpTablePrefix = "plp_",
  table = "diagnostic_participants",
  diagnosticId = NULL
)

Arguments

Details

Specify the connectionHandler, the resultDatabaseSettings, the table of interest and (optionally) a diagnosticId to filter to

Value

Returns a data.frame with the specified table

See Also

Other Prediction: getFullPredictionPerformances(), getPredictionAggregateTopPredictors(), getPredictionCohorts(), getPredictionCovariates(), getPredictionDiagnostics(), getPredictionHyperParamSearch(), getPredictionIntercept(), getPredictionLift(), getPredictionModelDesigns(), getPredictionOutcomes(), getPredictionPerformanceTable(), getPredictionPerformances(), getPredictionTargets(), getPredictionTopPredictors()

Examples

conDet <- getExampleConnectionDetails()

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

diagPred <- getPredictionDiagnosticTable(
  connectionHandler = connectionHandler, 
  schema = 'main',
  table = 'diagnostic_predictors'
)

Description

This function extracts the hyper parameters details

Usage

getPredictionHyperParamSearch(
  connectionHandler,
  schema,
  plpTablePrefix = "plp_",
  modelDesignId = NULL,
  databaseId = NULL
)

Arguments

Details

Specify the connectionHandler, the resultDatabaseSettings, the modelDesignId and the databaseId

Value

Returns a data.frame with the columns:

• metric the hyperparameter optimization metric

• fold the fold in cross validation

• value the metric value for the fold with the specified hyperparameter combination

plus columns for all the hyperparameters and their values

See Also

Other Prediction: getFullPredictionPerformances(), getPredictionAggregateTopPredictors(), getPredictionCohorts(), getPredictionCovariates(), getPredictionDiagnosticTable(), getPredictionDiagnostics(), getPredictionIntercept(), getPredictionLift(), getPredictionModelDesigns(), getPredictionOutcomes(), getPredictionPerformanceTable(), getPredictionPerformances(), getPredictionTargets(), getPredictionTopPredictors()

Examples

conDet <- getExampleConnectionDetails()

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

hyperParams <- getPredictionHyperParamSearch(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts the interception value

Usage

getPredictionIntercept(
  connectionHandler,
  schema,
  plpTablePrefix = "plp_",
  modelDesignId = NULL,
  databaseId = NULL
)

Arguments

Details

Specify the connectionHandler, the resultDatabaseSettings, the modelDesignId and the databaseId

Value

Returns a single value corresponding to the model intercept or NULL if not a logistic regression model

See Also

Other Prediction: getFullPredictionPerformances(), getPredictionAggregateTopPredictors(), getPredictionCohorts(), getPredictionCovariates(), getPredictionDiagnosticTable(), getPredictionDiagnostics(), getPredictionHyperParamSearch(), getPredictionLift(), getPredictionModelDesigns(), getPredictionOutcomes(), getPredictionPerformanceTable(), getPredictionPerformances(), getPredictionTargets(), getPredictionTopPredictors()

Examples

conDet <- getExampleConnectionDetails()

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

intercepts <- getPredictionIntercept(
  connectionHandler = connectionHandler, 
  schema = 'main'
)

Description

This function extracts the model lift (PPV/outcomeRate)

Usage

getPredictionLift(
  connectionHandler,
  schema,
  plpTablePrefix = "plp_",
  modelDesignIds = NULL,
  performanceIds = NULL,
  sensitivity = 0.1
)

Arguments

Details

Specify the connectionHandler, the resultDatabaseSettings and (optionally) modelDesignIds or performanceIds to filter to

Value

Returns a data.frame with the columns: modelDesignId, performanceId, evaluation, sensitivity, outcomeCount, positivePredictiveValue, outcomeRate and lift.

See Also

Other Prediction: getFullPredictionPerformances(), getPredictionAggregateTopPredictors(), getPredictionCohorts(), getPredictionCovariates(), getPredictionDiagnosticTable(), getPredictionDiagnostics(), getPredictionHyperParamSearch(), getPredictionIntercept(), getPredictionModelDesigns(), getPredictionOutcomes(), getPredictionPerformanceTable(), getPredictionPerformances(), getPredictionTargets(), getPredictionTopPredictors()

Examples

conDet <- getExampleConnectionDetails()

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

liftsAt0p15 <- getPredictionLift(
  connectionHandler = connectionHandler, 
  schema = 'main', 
  sensitivity = 0.15
)

Description

This function extracts the model design settings

Usage

getPredictionModelDesigns(
  connectionHandler,
  schema,
  plpTablePrefix = "plp_",
  cgTablePrefix = "cg_",
  targetIds = NULL,
  outcomeIds = NULL,
  modelDesignIds = NULL
)

Arguments

Details