Package index • Characterization

Aggregate Covariate Analysis

This analysis calculates the aggregate characteristics for a Target cohort (T), an Outcome cohort (O) and combiations of T with O during time at risk and T without O during time at risk.

createAggregateCovariateSettings(): Create aggregate covariate study settings

Dechallenge Rechallenge Analysis

For a given Target cohort (T) and Outcome cohort (O) find any occurrances of a dechallenge (when the T cohort stops close to when O started) and a rechallenge (when T restarts and O starts again) This is useful for investigating causality between drugs and events.

computeDechallengeRechallengeAnalyses(): Compute dechallenge rechallenge study

computeRechallengeFailCaseSeriesAnalyses(): Compute fine the subjects that fail the dechallenge rechallenge study

createDechallengeRechallengeSettings(): Create dechallenge rechallenge study settings

Time to Event Analysis

This analysis calculates the timing between the Target cohort (T) and an Outcome cohort (O).

computeTimeToEventAnalyses(): Compute time to event study

createTimeToEventSettings(): Create time to event study settings

Run Large Scale Characterization Study

Run multipe aggregate covariate analysis, time to event and dechallenge/rechallenge studies.

createCharacterizationSettings(): Create the settings for a large scale characterization study

loadCharacterizationSettings(): Load the characterization settings previously saved as a json file

runCharacterizationAnalyses(): execute a large-scale characterization study

saveCharacterizationSettings(): Save the characterization settings as a json

Save Load

Functions to save the analysis settings and the results (as sqlite or csv files).

Insert into Database

Functions to insert the results into a database.

createCharacterizationTables(): Create the results tables to store characterization results into a database

createSqliteDatabase(): Create an sqlite database connection

insertResultsToDatabase(): Upload the results into a result database

Shiny App

Functions to interactively exlore the results from runCharacterizationAnalyses().

viewCharacterization(): viewCharacterization - Interactively view the characterization results

Custom covariates

Code to create covariates during cohort start and end

createDuringCovariateSettings(): Create during covariate settings

getDbDuringCovariateData(): Extracts covariates that occur during a cohort

Incremental

Code to run incremetal model

cleanIncremental(): Removes csv files from folders that have not been marked as completed and removes the record of the execution file

cleanNonIncremental(): Removes csv files from the execution folder as there should be no csv files when running in non-incremental model

Helpers

Helper functions such as example data for users

exampleOmopConnectionDetails(): create a connection detail for an example GI Bleed dataset from Eunomia