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Extract the cohort method diagostic results

Source: R/EstimationQueries.R
getCmDiagnosticsData.Rd

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
)

Arguments

connectionHandler

A connection handler that connects to the database and extracts sql queries. Create a connection handler via `ResultModelManager::ConnectionHandler$new()`.

schema

The result database schema (e.g., 'main' for sqlite)

cmTablePrefix

The prefix used for the cohort method results tables

cgTablePrefix

The prefix used for the cohort generator results tables

databaseTable

The name of the table with the database details (default 'database_meta_data')

targetIds

A vector of integers corresponding to the target cohort IDs

outcomeIds

A vector of integers corresponding to the outcome cohort IDs

comparatorIds

A vector of integers corresponding to the comparator 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

  • 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.

  • unblind whether the results can be unblinded.

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

Details

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

See also

Other Estimation: getCMEstimation(), getCmMetaEstimation(), getSccsDiagnosticsData(), getSccsEstimation(), getSccsMetaEstimation(), plotCmEstimates(), plotSccsEstimates()

Examples

conDet <- getExampleConnectionDetails()

connectionHandler <- ResultModelManager::ConnectionHandler$new(conDet)
#> Connecting using SQLite driver

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