Help for package DrugUtilisation

Title:

Summarise Patient-Level Drug Utilisation in Data Mapped to the OMOP Common Data Model

Version:

1.0.4

Description:

Summarise patient-level drug utilisation cohorts using data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model. New users and prevalent users cohorts can be generated and their characteristics, indication and drug use summarised.

License:

Apache License (≥ 2)

Encoding:

UTF-8

RoxygenNote:

7.3.2

Suggests:

bit64, CohortSurvival, covr, DBI, duckdb, flextable, ggplot2, ggtext, gt, here, knitr, odbc, rmarkdown, RPostgres, scales, testthat (≥ 3.1.5), tibble, visOmopResults (≥ 1.0.0)

Config/testthat/edition:

Imports:

CDMConnector (≥ 1.4.0), cli, clock, CodelistGenerator (≥ 3.1.0), dplyr, glue, omopgenerics (≥ 1.0.0), PatientProfiles (≥ 1.0.0), purrr, rlang, stringr, tidyr

Depends:

R (≥ 4.1)

LazyData:

true

URL:

https://darwin-eu.github.io/DrugUtilisation/

BugReports:

https://github.com/darwin-eu/DrugUtilisation/issues

Config/testthat/parallel:

true

VignetteBuilder:

knitr

NeedsCompilation:

Packaged:

2025-07-02 19:22:51 UTC; martics

Author:

Martí Català

[aut, cre], Mike Du

[ctb], Yuchen Guo

[aut], Kim Lopez-Guell

[aut], Edward Burn

[aut], Xintong Li

[ctb], Marta Alcalde-Herraiz

[ctb], Nuria Mercade-Besora

[aut], Xihang Chen

[aut]

Maintainer:

Martí Català <marti.catalasabate@ndorms.ox.ac.uk>

Repository:

CRAN

Date/Publication:

2025-07-02 23:00:02 UTC

DrugUtilisation: Summarise Patient-Level Drug Utilisation in Data Mapped to the OMOP Common Data Model

Description

Author(s)

Maintainer: Martí Català marti.catalasabate@ndorms.ox.ac.uk (ORCID)

Authors:

Yuchen Guo yuchen.guo@ndorms.ox.ac.uk (ORCID)
Kim Lopez-Guell kim.lopez@spc.ox.ac.uk (ORCID)
Edward Burn edward.burn@ndorms.ox.ac.uk (ORCID)
Nuria Mercade-Besora nuria.mercadebesora@ndorms.ox.ac.uk (ORCID)
Xihang Chen xihang.chen@ndorms.ox.ac.uk (ORCID)

Other contributors:

Mike Du mike.du@ndorms.ox.ac.uk (ORCID) [contributor]
Xintong Li xintong.li@ndorms.ox.ac.uk (ORCID) [contributor]
Marta Alcalde-Herraiz marta.alcaldeherraiz@ndorms.ox.ac.uk (ORCID) [contributor]

To add a new column with the cumulative dose. To add multiple columns use `addDrugUtilisation()` for efficiency.

Description

To add a new column with the cumulative dose. To add multiple columns use addDrugUtilisation() for efficiency.

Usage

addCumulativeDose(
  cohort,
  ingredientConceptId,
  conceptSet = NULL,
  indexDate = "cohort_start_date",
  censorDate = "cohort_end_date",
  restrictIncident = TRUE,
  nameStyle = "cumulative_dose_{concept_name}_{ingredient}",
  name = NULL
)

Arguments

cohort

A cohort_table object.

ingredientConceptId

Ingredient OMOP concept that we are interested for the study.

conceptSet

List of concepts to be included.

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

restrictIncident

Whether to include only incident prescriptions in the analysis. If FALSE all prescriptions that overlap with the study period will be included.

nameStyle

Character string to specify the nameStyle of the new columns.

name

Name of the new computed cohort table, if NULL a temporary table will be created.

Value

The same cohort with the added column.

Examples


library(DrugUtilisation)
library(CodelistGenerator)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()
codelist <- getDrugIngredientCodes(cdm = cdm, name = "acetaminophen")
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "dus_cohort",
                                        conceptSet = codelist)

cdm$dus_cohort |>
  addCumulativeDose(ingredientConceptId = 1125315) |>
  glimpse()

To add a new column with the cumulative quantity. To add multiple columns use `addDrugUtilisation()` for efficiency.

Description

To add a new column with the cumulative quantity. To add multiple columns use addDrugUtilisation() for efficiency.

Usage

addCumulativeQuantity(
  cohort,
  conceptSet,
  indexDate = "cohort_start_date",
  censorDate = "cohort_end_date",
  restrictIncident = TRUE,
  nameStyle = "cumulative_quantity_{concept_name}",
  name = NULL
)

Arguments

cohort

A cohort_table object.

conceptSet

List of concepts to be included.

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

restrictIncident

Whether to include only incident prescriptions in the analysis. If FALSE all prescriptions that overlap with the study period will be included.

nameStyle

Character string to specify the nameStyle of the new columns.

name

Name of the new computed cohort table, if NULL a temporary table will be created.

Value

The same cohort with the added column.

Examples


library(DrugUtilisation)
library(CodelistGenerator)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()
codelist <- getDrugIngredientCodes(cdm = cdm, name = "acetaminophen")
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "dus_cohort",
                                        conceptSet = codelist)

cdm$dus_cohort |>
  addCumulativeQuantity(conceptSet = codelist) |>
  glimpse()

To add a new column with the days exposed. To add multiple columns use `addDrugUtilisation()` for efficiency.

Description

To add a new column with the days exposed. To add multiple columns use addDrugUtilisation() for efficiency.

Usage

addDaysExposed(
  cohort,
  conceptSet,
  gapEra,
  indexDate = "cohort_start_date",
  censorDate = "cohort_end_date",
  restrictIncident = TRUE,
  nameStyle = "days_exposed_{concept_name}",
  name = NULL
)

Arguments

cohort

A cohort_table object.

conceptSet

List of concepts to be included.

gapEra

Number of days between two continuous exposures to be considered in the same era.

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

restrictIncident

Whether to include only incident prescriptions in the analysis. If FALSE all prescriptions that overlap with the study period will be included.

nameStyle

Character string to specify the nameStyle of the new columns.

name

Name of the new computed cohort table, if NULL a temporary table will be created.

Value

The same cohort with the added column.

Examples


library(DrugUtilisation)
library(CodelistGenerator)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()
codelist <- getDrugIngredientCodes(cdm = cdm, name = "acetaminophen")
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "dus_cohort",
                                        conceptSet = codelist)

cdm$dus_cohort |>
  addDaysExposed(conceptSet = codelist, gapEra = 1) |>
  glimpse()

To add a new column with the days prescribed. To add multiple columns use `addDrugUtilisation()` for efficiency.

Description

To add a new column with the days prescribed. To add multiple columns use addDrugUtilisation() for efficiency.

Usage

addDaysPrescribed(
  cohort,
  conceptSet,
  indexDate = "cohort_start_date",
  censorDate = "cohort_end_date",
  restrictIncident = TRUE,
  nameStyle = "days_prescribed_{concept_name}",
  name = NULL
)

Arguments

cohort

A cohort_table object.

conceptSet

List of concepts to be included.

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

restrictIncident

Whether to include only incident prescriptions in the analysis. If FALSE all prescriptions that overlap with the study period will be included.

nameStyle

Character string to specify the nameStyle of the new columns.

name

Name of the new computed cohort table, if NULL a temporary table will be created.

Value

The same cohort with the added columns.

Examples


library(DrugUtilisation)
library(CodelistGenerator)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()
codelist <- getDrugIngredientCodes(cdm = cdm, name = "acetaminophen")
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "dus_cohort",
                                        conceptSet = codelist)

cdm$dus_cohort |>
  addDaysPrescribed(conceptSet = codelist) |>
  glimpse()

Add drug restart information as a column per follow-up period of interest.

Description

Add drug restart information as a column per follow-up period of interest.

Usage

addDrugRestart(
  cohort,
  switchCohortTable,
  switchCohortId = NULL,
  followUpDays = Inf,
  censorDate = NULL,
  incident = TRUE,
  nameStyle = "drug_restart_{follow_up_days}"
)

Arguments

cohort

A cohort_table object.

switchCohortTable

A cohort table in the cdm that contains possible alternative treatments.

switchCohortId

The cohort ids to be used from switchCohortTable. If NULL all cohort definition ids are used.

followUpDays

A vector of number of days to follow up. It can be multiple values.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

incident

Whether the switch treatment has to be incident (start after discontinuation) or not (it can start before the discontinuation and last till after).

nameStyle

Character string to specify the nameStyle of the new columns.

Value

The cohort table given with additional columns with information on the restart, switch and not exposed per follow-up period of interest.

Examples


library(DrugUtilisation)

cdm <- mockDrugUtilisation()

conceptlist <- list(acetaminophen = 1125360, metformin = c(1503297, 1503327))
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "switch_cohort",
                                        conceptSet = conceptlist)

cdm$cohort1 |>
  addDrugRestart(switchCohortTable = "switch_cohort")

Add new columns with drug use related information

Description

Add new columns with drug use related information

Usage

addDrugUtilisation(
  cohort,
  gapEra,
  conceptSet = NULL,
  ingredientConceptId = NULL,
  indexDate = "cohort_start_date",
  censorDate = "cohort_end_date",
  restrictIncident = TRUE,
  numberExposures = TRUE,
  numberEras = TRUE,
  daysExposed = TRUE,
  daysPrescribed = TRUE,
  timeToExposure = TRUE,
  initialExposureDuration = TRUE,
  initialQuantity = TRUE,
  cumulativeQuantity = TRUE,
  initialDailyDose = TRUE,
  cumulativeDose = TRUE,
  nameStyle = "{value}_{concept_name}_{ingredient}",
  name = NULL
)

Arguments

cohort

A cohort_table object.

gapEra

Number of days between two continuous exposures to be considered in the same era.

conceptSet

List of concepts to be included.

ingredientConceptId

Ingredient OMOP concept that we are interested for the study.

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

restrictIncident

Whether to include only incident prescriptions in the analysis. If FALSE all prescriptions that overlap with the study period will be included.

numberExposures

Whether to include 'number_exposures' (number of drug exposure records between indexDate and censorDate).

numberEras

Whether to include 'number_eras' (number of continuous exposure episodes between indexDate and censorDate).

daysExposed

Whether to include 'days_exposed' (number of days that the individual is in a continuous exposure episode, including allowed treatment gaps, between indexDate and censorDate; sum of the length of the different drug eras).

daysPrescribed

Whether to include 'days_prescribed' (sum of the number of days for each prescription that contribute in the analysis).

timeToExposure

Whether to include 'time_to_exposure' (number of days between indexDate and the first episode).

initialExposureDuration

Whether to include 'initial_exposure_duration' (number of prescribed days of the first drug exposure record).

initialQuantity

Whether to include 'initial_quantity' (quantity of the first drug exposure record).

cumulativeQuantity

Whether to include 'cumulative_quantity' (sum of the quantity of the different exposures considered in the analysis).

initialDailyDose

Whether to include 'initial_daily_dose_{unit}' (daily dose of the first considered prescription).

cumulativeDose

Whether to include 'cumulative_dose_{unit}' (sum of the cumulative dose of the analysed drug exposure records).

nameStyle

Character string to specify the nameStyle of the new columns.

name

Name of the new computed cohort table, if NULL a temporary table will be created.

Value

The same cohort with the added columns.

Examples


library(DrugUtilisation)
library(CodelistGenerator)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()
codelist <- getDrugIngredientCodes(cdm = cdm, name = "acetaminophen")

cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "dus_cohort",
                                        conceptSet = codelist)
cdm$dus_cohort |>
  addDrugUtilisation(ingredientConceptId = 1125315, gapEra = 30) |>
  glimpse()

Add a variable indicating individuals indications

Description

Add a variable to a drug cohort indicating their presence in an indication cohort in a specified time window. If an individual is not in one of the indication cohorts, they will be considered to have an unknown indication if they are present in one of the specified OMOP CDM clinical tables. If they are neither in an indication cohort or a clinical table they will be considered as having no observed indication.

Usage

addIndication(
  cohort,
  indicationCohortName,
  indicationCohortId = NULL,
  indicationWindow = list(c(0, 0)),
  unknownIndicationTable = NULL,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  mutuallyExclusive = TRUE,
  nameStyle = NULL,
  name = NULL
)

Arguments

cohort

A cohort_table object.

indicationCohortName

Name of indication cohort table

indicationCohortId

target cohort Id to add indication

indicationWindow

time window of interests

unknownIndicationTable

Tables to search unknown indications

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

mutuallyExclusive

Whether to consider mutually exclusive categories (one column per window) or not (one column per window and indication).

nameStyle

Name style for the indications. By default: 'indication_{window_name}' (mutuallyExclusive = TRUE), 'indication_{window_name}_{cohort_name}' (mutuallyExclusive = FALSE).

name

Name of the new computed cohort table, if NULL a temporary table will be created.

Value

The original table with a variable added that summarises the individual´s indications.

Examples


library(DrugUtilisation)
library(CDMConnector)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()

indications <- list(headache = 378253, asthma = 317009)
cdm <- generateConceptCohortSet(cdm = cdm,
                                conceptSet = indications,
                                name = "indication_cohorts")

cdm <- generateIngredientCohortSet(cdm = cdm,
                                   name = "drug_cohort",
                                   ingredient = "acetaminophen")

cdm$drug_cohort |>
  addIndication(
    indicationCohortName = "indication_cohorts",
    indicationWindow = list(c(0, 0)),
    unknownIndicationTable = "condition_occurrence"
  ) |>
  glimpse()

To add a new column with the initial daily dose. To add multiple columns use `addDrugUtilisation()` for efficiency.

Description

To add a new column with the initial daily dose. To add multiple columns use addDrugUtilisation() for efficiency.

Usage

addInitialDailyDose(
  cohort,
  ingredientConceptId,
  conceptSet = NULL,
  indexDate = "cohort_start_date",
  censorDate = "cohort_end_date",
  restrictIncident = TRUE,
  nameStyle = "initial_daily_dose_{concept_name}_{ingredient}",
  name = NULL
)

Arguments

cohort

A cohort_table object.

ingredientConceptId

Ingredient OMOP concept that we are interested for the study.

conceptSet

List of concepts to be included.

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

restrictIncident

Whether to include only incident prescriptions in the analysis. If FALSE all prescriptions that overlap with the study period will be included.

nameStyle

Character string to specify the nameStyle of the new columns.

name

Name of the new computed cohort table, if NULL a temporary table will be created.

Value

The same cohort with the added column.

Examples


library(DrugUtilisation)
library(CodelistGenerator)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()
codelist <- getDrugIngredientCodes(cdm = cdm, name = "acetaminophen")
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "dus_cohort",
                                        conceptSet = codelist)

cdm$dus_cohort |>
  addInitialDailyDose(ingredientConceptId = 1125315) |>
  glimpse()

To add a new column with the duration of the first exposure. To add multiple columns use `addDrugUtilisation()` for efficiency.

Description

To add a new column with the duration of the first exposure. To add multiple columns use addDrugUtilisation() for efficiency.

Usage

addInitialExposureDuration(
  cohort,
  conceptSet,
  indexDate = "cohort_start_date",
  censorDate = "cohort_end_date",
  restrictIncident = TRUE,
  nameStyle = "initial_exposure_duration_{concept_name}",
  name = NULL
)

Arguments

cohort

A cohort_table object.

conceptSet

List of concepts to be included.

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

restrictIncident

Whether to include only incident prescriptions in the analysis. If FALSE all prescriptions that overlap with the study period will be included.

nameStyle

Character string to specify the nameStyle of the new columns.

name

Name of the new computed cohort table, if NULL a temporary table will be created.

Value

The same cohort with the added column.

Examples


library(DrugUtilisation)
library(CodelistGenerator)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()
codelist <- getDrugIngredientCodes(cdm = cdm, name = "acetaminophen")
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "dus_cohort",
                                        conceptSet = codelist)

cdm$dus_cohort |>
  addInitialExposureDuration(conceptSet = codelist) |>
  glimpse()

To add a new column with the initial quantity. To add multiple columns use `addDrugUtilisation()` for efficiency.

Description

To add a new column with the initial quantity. To add multiple columns use addDrugUtilisation() for efficiency.

Usage

addInitialQuantity(
  cohort,
  conceptSet,
  indexDate = "cohort_start_date",
  censorDate = "cohort_end_date",
  restrictIncident = TRUE,
  nameStyle = "initial_quantity_{concept_name}",
  name = NULL
)

Arguments

cohort

A cohort_table object.

conceptSet

List of concepts to be included.

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

restrictIncident

Whether to include only incident prescriptions in the analysis. If FALSE all prescriptions that overlap with the study period will be included.

nameStyle

Character string to specify the nameStyle of the new columns.

name

Name of the new computed cohort table, if NULL a temporary table will be created.

Value

The same cohort with the added column.

Examples


library(DrugUtilisation)
library(CodelistGenerator)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()
codelist <- getDrugIngredientCodes(cdm = cdm, name = "acetaminophen")
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "dus_cohort",
                                        conceptSet = codelist)

cdm$dus_cohort |>
  addInitialQuantity(conceptSet = codelist) |>
  glimpse()

To add a new column with the number of eras. To add multiple columns use `addDrugUtilisation()` for efficiency.

Description

To add a new column with the number of eras. To add multiple columns use addDrugUtilisation() for efficiency.

Usage

addNumberEras(
  cohort,
  conceptSet,
  gapEra,
  indexDate = "cohort_start_date",
  censorDate = "cohort_end_date",
  restrictIncident = TRUE,
  nameStyle = "number_eras_{concept_name}",
  name = NULL
)

Arguments

cohort

A cohort_table object.

conceptSet

List of concepts to be included.

gapEra

Number of days between two continuous exposures to be considered in the same era.

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

restrictIncident

Whether to include only incident prescriptions in the analysis. If FALSE all prescriptions that overlap with the study period will be included.

nameStyle

Character string to specify the nameStyle of the new columns.

name

Name of the new computed cohort table, if NULL a temporary table will be created.

Value

The same cohort with the added column.

Examples


library(DrugUtilisation)
library(CodelistGenerator)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()
codelist <- getDrugIngredientCodes(cdm = cdm, name = "acetaminophen")
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "dus_cohort",
                                        conceptSet = codelist)

cdm$dus_cohort |>
  addNumberEras(conceptSet = codelist, gapEra = 1) |>
  glimpse()

To add a new column with the number of exposures. To add multiple columns use `addDrugUtilisation()` for efficiency.

Description

To add a new column with the number of exposures. To add multiple columns use addDrugUtilisation() for efficiency.

Usage

addNumberExposures(
  cohort,
  conceptSet,
  indexDate = "cohort_start_date",
  censorDate = "cohort_end_date",
  restrictIncident = TRUE,
  nameStyle = "number_exposures_{concept_name}",
  name = NULL
)

Arguments

cohort

A cohort_table object.

conceptSet

List of concepts to be included.

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

restrictIncident

Whether to include only incident prescriptions in the analysis. If FALSE all prescriptions that overlap with the study period will be included.

nameStyle

Character string to specify the nameStyle of the new columns.

name

Name of the new computed cohort table, if NULL a temporary table will be created.

Value

The same cohort with the added columns.

Examples


library(DrugUtilisation)
library(CodelistGenerator)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()
codelist <- getDrugIngredientCodes(cdm = cdm, name = "acetaminophen")
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "dus_cohort",
                                        conceptSet = codelist)

cdm$dus_cohort |>
  addNumberExposures(conceptSet = codelist) |>
  glimpse()

To add a new column with the time to exposure. To add multiple columns use `addDrugUtilisation()` for efficiency.

Description

To add a new column with the time to exposure. To add multiple columns use addDrugUtilisation() for efficiency.

Usage

addTimeToExposure(
  cohort,
  conceptSet,
  indexDate = "cohort_start_date",
  censorDate = "cohort_end_date",
  restrictIncident = TRUE,
  nameStyle = "time_to_exposure_{concept_name}",
  name = NULL
)

Arguments

cohort

A cohort_table object.

conceptSet

List of concepts to be included.

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

restrictIncident

Whether to include only incident prescriptions in the analysis. If FALSE all prescriptions that overlap with the study period will be included.

nameStyle

Character string to specify the nameStyle of the new columns.

name

Name of the new computed cohort table, if NULL a temporary table will be created.

Value

The same cohort with the added column.

Examples


library(DrugUtilisation)
library(CodelistGenerator)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()
codelist <- getDrugIngredientCodes(cdm = cdm, name = "acetaminophen")
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "dus_cohort",
                                        conceptSet = codelist)

cdm$dus_cohort |>
  addTimeToExposure(conceptSet = codelist) |>
  glimpse()

Add a variable indicating individuals medications

Description

Add a variable to a drug cohort indicating their presence of a medication cohort in a specified time window.

Usage

addTreatment(
  cohort,
  treatmentCohortName,
  treatmentCohortId = NULL,
  window = list(c(0, 0)),
  indexDate = "cohort_start_date",
  censorDate = NULL,
  mutuallyExclusive = TRUE,
  nameStyle = NULL,
  name = NULL
)

Arguments

cohort

A cohort_table object.

treatmentCohortName

Name of treatment cohort table

treatmentCohortId

target cohort Id to add treatment

window

time window of interests.

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

mutuallyExclusive

Whether to consider mutually exclusive categories (one column per window) or not (one column per window and treatment).

nameStyle

Name style for the treatment columns. By default: 'treatment_{window_name}' (mutuallyExclusive = TRUE), 'treatment_{window_name}_{cohort_name}' (mutuallyExclusive = FALSE).

name

Name of the new computed cohort table, if NULL a temporary table will be created.

Value

The original table with a variable added that summarises the individual´s indications.

Examples


library(DrugUtilisation)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation(numberIndividuals = 50)

cdm <- generateIngredientCohortSet(cdm = cdm,
                                   name = "drug_cohort",
                                   ingredient = "acetaminophen")

cdm <- generateIngredientCohortSet(cdm = cdm,
                                   name = "treatments",
                                   ingredient = c("metformin", "simvastatin"))

cdm$drug_cohort |>
  addTreatment("treatments", window = list(c(0, 0), c(1, 30), c(31, 60))) |>
  glimpse()

Run benchmark of drug utilisation cohort generation

Description

Run benchmark of drug utilisation cohort generation

Usage

benchmarkDrugUtilisation(
  cdm,
  ingredient = "acetaminophen",
  alternativeIngredient = c("ibuprofen", "aspirin", "diclofenac"),
  indicationCohort = NULL
)

Arguments

cdm

A cdm_reference object.

ingredient

Name of ingredient to benchmark.

alternativeIngredient

Name of ingredients to use as alternative treatments.

indicationCohort

Name of a cohort in the cdm_reference object to use as indicatiomn.

Value

A summarise_result object.

Examples


library(DrugUtilisation)
library(CDMConnector)
library(duckdb)

requireEunomia()
con <- dbConnect(drv = duckdb(dbdir = eunomiaDir()))
cdm <- cdmFromCon(con = con, cdmSchema = "main", writeSchema = "main")

timings <- benchmarkDrugUtilisation(cdm)

timings

Get the gapEra used to create a cohort

Description

Get the gapEra used to create a cohort

Usage

cohortGapEra(cohort, cohortId = NULL)

Arguments

cohort

A cohort_table object.

cohortId

Integer vector refering to cohortIds from cohort. If NULL all cohort definition ids in settings will be used.

Value

gapEra values for the specific cohortIds

Examples


library(DrugUtilisation)
library(CodelistGenerator)

cdm <- mockDrugUtilisation()

druglist <- getDrugIngredientCodes(cdm = cdm,
                                   name = c("acetaminophen", "metformin"))

cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "drug_cohorts",
                                        conceptSet = druglist,
                                        gapEra = 100)

cohortGapEra(cdm$drug_cohorts)

numberEras

Whether to include 'number_eras' (number of continuous exposure episodes between indexDate and censorDate).

daysExposed

daysPrescribed

Whether to include 'days_prescribed' (sum of the number of days for each prescription that contribute in the analysis).

timeToExposure

Whether to include 'time_to_exposure' (number of days between indexDate and the first episode).

initialExposureDuration

Whether to include 'initial_exposure_duration' (number of prescribed days of the first drug exposure record).

initialQuantity

Whether to include 'initial_quantity' (quantity of the first drug exposure record).

cumulativeQuantity

Whether to include 'cumulative_quantity' (sum of the quantity of the different exposures considered in the analysis).

initialDailyDose

Whether to include 'initial_daily_dose_{unit}' (daily dose of the first considered prescription).

cumulativeDose

Whether to include 'cumulative_dose_{unit}' (sum of the cumulative dose of the analysed drug exposure records).

exposedTime

deprecated.

Erafy a cohort_table collapsing records separated gapEra days or less.

Description

Erafy a cohort_table collapsing records separated gapEra days or less.

Usage

erafyCohort(
  cohort,
  gapEra,
  cohortId = NULL,
  nameStyle = "{cohort_name}_{gap_era}",
  name = omopgenerics::tableName(cohort)
)

Arguments

cohort

A cohort_table object.

gapEra

Number of days between two continuous exposures to be considered in the same era.

cohortId

A cohort definition id to restrict by. If NULL, all cohorts will be included.

nameStyle

String to create the new names of cohorts. Must contain '{cohort_name}' if more than one cohort is present and '{gap_era}' if more than one gapEra is provided.

name

Name of the new cohort table, it must be a length 1 character vector.

Value

A cohort_table object.

Examples


library(DrugUtilisation)

cdm <- mockDrugUtilisation()

cdm$cohort2 <- cdm$cohort1 |>
  erafyCohort(gapEra = 30, name = "cohort2")

cdm$cohort2

settings(cdm$cohort2)

mockDisconnect(cdm)

Helper for consistent documentation of `gapEra`.

Description

Helper for consistent documentation of gapEra.

Arguments

gapEra

Number of days between two continuous exposures to be considered in the same era.

Generate a set of drug cohorts based on ATC classification

Description

Adds a new cohort table to the cdm reference with individuals who have drug exposure records that belong to the specified Anatomical Therapeutic Chemical (ATC) classification. Cohort start and end dates will be based on drug record start and end dates, respectively. Records that overlap or have fewer days between them than the specified gap era will be concatenated into a single cohort entry.

Usage

generateAtcCohortSet(
  cdm,
  name,
  atcName = NULL,
  gapEra = 1,
  subsetCohort = NULL,
  subsetCohortId = NULL,
  numberExposures = FALSE,
  daysPrescribed = FALSE,
  ...
)

Arguments

cdm

A cdm_reference object.

name

Name of the new cohort table, it must be a length 1 character vector.

atcName

Names of ATC classification of interest.

gapEra

Number of days between two continuous exposures to be considered in the same era. Records that have fewer days between them than this gap will be concatenated into the same cohort record.

subsetCohort

Cohort table to subset.

subsetCohortId

Cohort id to subset.

numberExposures

Whether to include 'number_exposures' (number of drug exposure records between indexDate and censorDate).

daysPrescribed

Whether to include 'days_prescribed' (number of days prescribed used to create each era).

...

Arguments to be passed to CodelistGenerator::getATCCodes().

Value

The function returns the cdm reference provided with the addition of the new cohort table.

Examples


library(DrugUtilisation)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()

cdm <- generateAtcCohortSet(cdm = cdm,
                            atcName = "alimentary tract and metabolism",
                            name = "drugs")

cdm$drugs |>
  glimpse()

Generate a set of drug cohorts based on given concepts

Description

Adds a new cohort table to the cdm reference with individuals who have drug exposure records with the specified concepts. Cohort start and end dates will be based on drug record start and end dates, respectively. Records that overlap or have fewer days between them than the specified gap era will be concatenated into a single cohort entry.

Usage

generateDrugUtilisationCohortSet(
  cdm,
  name,
  conceptSet,
  gapEra = 1,
  subsetCohort = NULL,
  subsetCohortId = NULL,
  numberExposures = FALSE,
  daysPrescribed = FALSE
)

Arguments

cdm

A cdm_reference object.

name

Name of the new cohort table, it must be a length 1 character vector.

conceptSet

List of concepts to be included.

gapEra

Number of days between two continuous exposures to be considered in the same era.

subsetCohort

Cohort table to subset.

subsetCohortId

Cohort id to subset.

numberExposures

Whether to include 'number_exposures' (number of drug exposure records between indexDate and censorDate).

daysPrescribed

Whether to include 'days_prescribed' (number of days prescribed used to create each era).

Value

The function returns the cdm reference provided with the addition of the new cohort table.

Examples


library(DrugUtilisation)
library(CodelistGenerator)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()

druglist <- getDrugIngredientCodes(cdm = cdm,
                                   name = c("acetaminophen", "metformin"),
                                   nameStyle = "{concept_name}")

cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "drug_cohorts",
                                        conceptSet = druglist,
                                        gapEra = 30,
                                        numberExposures = TRUE,
                                        daysPrescribed = TRUE)

cdm$drug_cohorts |>
  glimpse()

Generate a set of drug cohorts based on drug ingredients

Description

Adds a new cohort table to the cdm reference with individuals who have drug exposure records with the specified drug ingredient. Cohort start and end dates will be based on drug record start and end dates, respectively. Records that overlap or have fewer days between them than the specified gap era will be concatenated into a single cohort entry.

Usage

generateIngredientCohortSet(
  cdm,
  name,
  ingredient = NULL,
  gapEra = 1,
  subsetCohort = NULL,
  subsetCohortId = NULL,
  numberExposures = FALSE,
  daysPrescribed = FALSE,
  ...
)

Arguments

cdm

A cdm_reference object.

name

Name of the new cohort table, it must be a length 1 character vector.

ingredient

Accepts both vectors and named lists of ingredient names. For a vector input, e.g., c("acetaminophen", "codeine"), it generates a cohort table with descendant concept codes for each ingredient, assigning unique cohort_definition_id. For a named list input, e.g., list( "test_1" = c("simvastatin", "acetaminophen"), "test_2" = "metformin"), it produces a cohort table based on the structure of the input, where each name leads to a combined set of descendant concept codes for the specified ingredients, creating distinct cohort_definition_id for each named group.

gapEra

Number of days between two continuous exposures to be considered in the same era.

subsetCohort

Cohort table to subset.

subsetCohortId

Cohort id to subset.

numberExposures

Whether to include 'number_exposures' (number of drug exposure records between indexDate and censorDate).

daysPrescribed

Whether to include 'days_prescribed' (number of days prescribed used to create each era).

...

Arguments to be passed to CodelistGenerator::getDrugIngredientCodes().

Value

The function returns the cdm reference provided with the addition of the new cohort table.

Examples


library(DrugUtilisation)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()

cdm <- generateIngredientCohortSet(cdm = cdm,
                                   ingredient = "acetaminophen",
                                   name = "acetaminophen")

cdm$acetaminophen |>
  glimpse()

Helper for consistent documentation of `indexDate`.

Description

Helper for consistent documentation of indexDate.

Arguments

indexDate

Name of a column that indicates the date to start the analysis.

Helper for consistent documentation of `ingredientConceptId`.

Description

Helper for consistent documentation of ingredientConceptId.

Arguments

ingredientConceptId

Ingredient OMOP concept that we are interested for the study.

It creates a mock database for testing DrugUtilisation package

Description

It creates a mock database for testing DrugUtilisation package

Usage

mockDrugUtilisation(
  con = NULL,
  writeSchema = NULL,
  numberIndividuals = 10,
  seed = NULL,
  ...
)

Arguments

con

A DBIConnection object to a database. If NULL a new duckdb connection will be used.

writeSchema

A schema with writing permissions to copy there the cdm tables.

numberIndividuals

Number of individuals in the mock cdm.

seed

Seed for the random numbers. If NULL no seed is used.

...

Tables to use as basis to create the mock. If some tables are provided they will be used to construct the cdm object.

Value

A cdm reference with the mock tables

Examples


library(DrugUtilisation)

cdm <- mockDrugUtilisation()

cdm

Function to create a tibble with the patterns from current drug strength table

Description

Function to create a tibble with the patterns from current drug strength table

Usage

patternTable(cdm)

Arguments

cdm

A cdm_reference object.

Value

The function creates a tibble with the different patterns found in the table, plus a column of potentially valid and invalid combinations.

Examples


library(DrugUtilisation)

cdm <- mockDrugUtilisation()

patternTable(cdm)

Patterns valid to compute daily dose with the associated formula.

Description

Patterns valid to compute daily dose with the associated formula.

Usage

patternsWithFormula

Format

A data frame with eight variables: pattern_id, amount, amount_unit, numerator, numerator_unit, denominator, denominator_unit, formula_name and formula.

Helper for consistent documentation of `plot`.

Description

Helper for consistent documentation of plot.

Arguments

facet

Columns to facet by. See options with availablePlotColumns(result). Formula is also allowed to specify rows and columns.

colour

Columns to color by. See options with availablePlotColumns(result).

Generate a custom ggplot2 from a summarised_result object generated with summariseDrugRestart() function.

Description

Generate a custom ggplot2 from a summarised_result object generated with summariseDrugRestart() function.

Usage

plotDrugRestart(
  result,
  facet = cdm_name + cohort_name ~ follow_up_days,
  colour = "variable_level"
)

Arguments

result

A summarised_result object.

facet

Columns to facet by. See options with availablePlotColumns(result). Formula is also allowed to specify rows and columns.

colour

Columns to color by. See options with availablePlotColumns(result).

Value

A ggplot2 object.

Examples

## Not run: 
library(DrugUtilisation)

cdm <- mockDrugUtilisation()

conceptlist <- list("a" = 1125360, "b" = c(1503297, 1503327))
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "switch_cohort",
                                        conceptSet = conceptlist)

result <- cdm$cohort1 |>
  summariseDrugRestart(switchCohortTable = "switch_cohort")

plotDrugRestart(result)

## End(Not run)

Plot the results of `summariseDrugUtilisation`

Description

Plot the results of summariseDrugUtilisation

Usage

plotDrugUtilisation(
  result,
  variable = "number exposures",
  plotType = "barplot",
  facet = strataColumns(result),
  colour = "cohort_name"
)

Arguments

result

A summarised_result object.

variable

Variable to plot. See unique(result$variable_name) for options.

plotType

Must be a choice between: 'scatterplot', 'barplot', 'densityplot', and 'boxplot'.

facet

Columns to facet by. See options with availablePlotColumns(result). Formula is also allowed to specify rows and columns.

colour

Columns to color by. See options with availablePlotColumns(result).

Value

A ggplot2 object.

Examples


library(DrugUtilisation)
library(PatientProfiles)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation(numberIndividuals = 100)
codes <- list(aceta = c(1125315, 1125360, 2905077, 43135274))
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "cohort",
                                        conceptSet = codes)

result <- cdm$cohort |>
  addSex() |>
  summariseDrugUtilisation(
    strata = "sex",
    ingredientConceptId = 1125315,
    estimates = c("min", "q25", "median", "q75", "max", "density")
  )

result |>
  filter(estimate_name == "median") |>
  plotDrugUtilisation(
    variable = "days prescribed",
    plotType = "barplot"
  )

result |>
  plotDrugUtilisation(
    variable = "days exposed",
    facet = cohort_name ~ cdm_name,
    colour = "sex",
    plotType = "boxplot"
  )

result |>
  plotDrugUtilisation(
    variable = "cumulative dose milligram",
    plotType = "densityplot",
    facet = "cohort_name",
    colour = "sex"
  )

mockDisconnect(cdm)

Generate a plot visualisation (ggplot2) from the output of summariseIndication

Description

Generate a plot visualisation (ggplot2) from the output of summariseIndication

Usage

plotIndication(
  result,
  facet = cdm_name + cohort_name ~ window_name,
  colour = "variable_level"
)

Arguments

result

A summarised_result object.

facet

Columns to facet by. See options with availablePlotColumns(result). Formula is also allowed to specify rows and columns.

colour

Columns to color by. See options with availablePlotColumns(result).

Value

A ggplot2 object

Examples


library(DrugUtilisation)
library(CDMConnector)

cdm <- mockDrugUtilisation()

indications <- list(headache = 378253, asthma = 317009)
cdm <- generateConceptCohortSet(cdm = cdm,
                                conceptSet = indications,
                                name = "indication_cohorts")

cdm <- generateIngredientCohortSet(cdm = cdm,
                                   name = "drug_cohort",
                                   ingredient = "acetaminophen")

result <- cdm$drug_cohort |>
  summariseIndication(
    indicationCohortName = "indication_cohorts",
    unknownIndicationTable = "condition_occurrence",
    indicationWindow = list(c(-Inf, 0), c(-365, 0))
  )

plotIndication(result)

Plot proportion of patients covered

Description

Plot proportion of patients covered

Usage

plotProportionOfPatientsCovered(
  result,
  facet = "cohort_name",
  colour = strataColumns(result),
  ribbon = TRUE
)

Arguments

result

A summarised_result object.

facet

Columns to facet by. See options with availablePlotColumns(result). Formula is also allowed to specify rows and columns.

colour

Columns to color by. See options with availablePlotColumns(result).

ribbon

Whether to plot a ribbon with the confidence intervals.

Value

Plot of proportion Of patients covered over time

Examples


library(DrugUtilisation)

cdm <- mockDrugUtilisation()

cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "my_cohort",
                                        conceptSet = list(drug_of_interest = c(1503297, 1503327)))

result <- cdm$my_cohort |>
  summariseProportionOfPatientsCovered(followUpDays = 365)

plotProportionOfPatientsCovered(result)

Generate a custom ggplot2 from a summarised_result object generated with summariseTreatment function.

Description

Generate a custom ggplot2 from a summarised_result object generated with summariseTreatment function.

Usage

plotTreatment(
  result,
  facet = cdm_name + cohort_name ~ window_name,
  colour = "variable_level"
)

Arguments

result

A summarised_result object.

facet

Columns to facet by. See options with availablePlotColumns(result). Formula is also allowed to specify rows and columns.

colour

Columns to color by. See options with availablePlotColumns(result).

Value

A ggplot2 object.

Examples

## Not run: 
library(DrugUtilisation)

cdm <- mockDrugUtilisation()
result <- cdm$cohort1 |>
  summariseTreatment(
    treatmentCohortName = "cohort2",
    window = list(c(0, 30), c(31, 365))
  )

plotTreatment(result)

## End(Not run)

Objects exported from other packages

Description

These objects are imported from other packages. Follow the links below to see their documentation.

omopgenerics: additionalColumns, attrition, bind, cohortCodelist, cohortCount, exportSummarisedResult, groupColumns, importSummarisedResult, settings, settingsColumns, strataColumns, suppress, tidy
PatientProfiles: mockDisconnect

Restrict cohort to only cohort records within a certain date range

Description

Filter the cohort table keeping only the cohort records for which the specified index date is within a specified date range.

Usage

requireDrugInDateRange(
  cohort,
  dateRange,
  indexDate = "cohort_start_date",
  cohortId = NULL,
  name = omopgenerics::tableName(cohort)
)

Arguments

cohort

A cohort_table object.

dateRange

Date interval to consider. Any records with the index date outside of this range will be dropped.

indexDate

The column containing the date that will be checked against the date range.

cohortId

A cohort definition id to restrict by. If NULL, all cohorts will be included.

name

Name of the new cohort table, it must be a length 1 character vector.

Value

The cohort table having applied the date requirement.

Examples


library(DrugUtilisation)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()

cdm$cohort1 <- cdm$cohort1 |>
  requireDrugInDateRange(dateRange = as.Date(c("2020-01-01", NA)))

attrition(cdm$cohort1) |>
  glimpse()

Restrict cohort to only the first cohort record per subject

Description

Filter the cohort table keeping only the first cohort record per subject.

Usage

requireIsFirstDrugEntry(
  cohort,
  cohortId = NULL,
  name = omopgenerics::tableName(cohort)
)

Arguments

cohort

A cohort_table object.

cohortId

A cohort definition id to restrict by. If NULL, all cohorts will be included.

name

Name of the new cohort table, it must be a length 1 character vector.

Value

The cohort table having applied the first entry requirement.

Examples


library(DrugUtilisation)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()

cdm$cohort1 <- cdm$cohort1 |>
  requireIsFirstDrugEntry()

attrition(cdm$cohort1) |>
  glimpse()

Restrict cohort to only cohort records with the given amount of prior observation time in the database

Description

Filter the cohort table keeping only the cohort records for which the individual has the required observation time in the database prior to their cohort start date.

Usage

requireObservationBeforeDrug(
  cohort,
  days,
  cohortId = NULL,
  name = omopgenerics::tableName(cohort)
)

Arguments

cohort

A cohort_table object.

days

Number of days of prior observation required before cohort start date. Any records with fewer days will be dropped.

cohortId

A cohort definition id to restrict by. If NULL, all cohorts will be included.

name

Name of the new cohort table, it must be a length 1 character vector.

Value

The cohort table having applied the prior observation requirement.

Examples


library(DrugUtilisation)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()

cdm$cohort1 <- cdm$cohort1 |>
  requireObservationBeforeDrug(days = 365)

attrition(cdm$cohort1) |>
  glimpse()

Restrict cohort to only cohort records with a given amount of time since the last cohort record ended

Description

Filter the cohort table keeping only the cohort records for which the required amount of time has passed since the last cohort entry ended for that individual.

Usage

requirePriorDrugWashout(
  cohort,
  days,
  cohortId = NULL,
  name = omopgenerics::tableName(cohort)
)

Arguments

cohort

A cohort_table object.

days

The number of days required to have passed since the last cohort record finished. Any records with fewer days than this will be dropped. Note that setting days to Inf will lead to the same result as that from using the requireIsFirstDrugEntry function (with only an individual´s first cohort record kept).

cohortId

A cohort definition id to restrict by. If NULL, all cohorts will be included.

name

Name of the new cohort table, it must be a length 1 character vector.

Value

The cohort table having applied the washout requirement.

Examples


library(DrugUtilisation)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()

cdm$cohort1 <- cdm$cohort1 |>
  requirePriorDrugWashout(days = 90)

attrition(cdm$cohort1) |>
  glimpse()

Check coverage of daily dose computation in a sample of the cdm for selected concept sets and ingredient

Description

Check coverage of daily dose computation in a sample of the cdm for selected concept sets and ingredient

Usage

summariseDoseCoverage(
  cdm,
  ingredientConceptId,
  estimates = c("count_missing", "percentage_missing", "mean", "sd", "q25", "median",
    "q75"),
  sampleSize = NULL
)

Arguments

cdm

A cdm_reference object.

ingredientConceptId

Ingredient OMOP concept that we are interested for the study.

estimates

Estimates to obtain.

sampleSize

Maximum number of records of an ingredient to estimate dose coverage. If an ingredient has more, a random sample equal to sampleSize will be considered. If NULL, all records will be used.

Value

The function returns information of the coverage of computeDailyDose.R for the selected ingredients and concept sets

Examples


library(DrugUtilisation)

cdm <- mockDrugUtilisation()

summariseDoseCoverage(cdm = cdm, ingredientConceptId = 1125315)

Summarise the drug restart for each follow-up period of interest.

Description

Summarise the drug restart for each follow-up period of interest.

Usage

summariseDrugRestart(
  cohort,
  cohortId = NULL,
  switchCohortTable,
  switchCohortId = NULL,
  strata = list(),
  followUpDays = Inf,
  censorDate = NULL,
  incident = TRUE,
  restrictToFirstDiscontinuation = TRUE
)

Arguments

cohort

A cohort_table object.

cohortId

A cohort definition id to restrict by. If NULL, all cohorts will be included.

switchCohortTable

A cohort table in the cdm that contains possible alternative treatments.

switchCohortId

The cohort ids to be used from switchCohortTable. If NULL all cohort definition ids are used.

strata

A list of variables to stratify results. These variables must have been added as additional columns in the cohort table.

followUpDays

A vector of number of days to follow up. It can be multiple values.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

incident

Whether the switch treatment has to be incident (start after discontinuation) or not (it can start before the discontinuation and last till after).

restrictToFirstDiscontinuation

Whether to consider only the first discontinuation episode or all of them.

Value

A summarised_result object with the percentages of restart, switch and not exposed per follow-up period given.

Examples


library(DrugUtilisation)

cdm <- mockDrugUtilisation()

conceptlist <- list(acetaminophen = 1125360, metformin = c(1503297, 1503327))
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "switch_cohort",
                                        conceptSet = conceptlist)

result <- cdm$cohort1 |>
  summariseDrugRestart(switchCohortTable = "switch_cohort")

tableDrugRestart(result)

This function is used to summarise the dose utilisation table over multiple cohorts.

Description

This function is used to summarise the dose utilisation table over multiple cohorts.

Usage

summariseDrugUtilisation(
  cohort,
  cohortId = NULL,
  strata = list(),
  estimates = c("q25", "median", "q75", "mean", "sd", "count_missing",
    "percentage_missing"),
  ingredientConceptId = NULL,
  conceptSet = NULL,
  indexDate = "cohort_start_date",
  censorDate = "cohort_end_date",
  restrictIncident = TRUE,
  gapEra = 1,
  numberExposures = TRUE,
  numberEras = TRUE,
  daysExposed = TRUE,
  daysPrescribed = TRUE,
  timeToExposure = TRUE,
  initialExposureDuration = TRUE,
  initialQuantity = TRUE,
  cumulativeQuantity = TRUE,
  initialDailyDose = TRUE,
  cumulativeDose = TRUE
)

Arguments

cohort

A cohort_table object.

cohortId

A cohort definition id to restrict by. If NULL, all cohorts will be included.

strata

A list of variables to stratify results. These variables must have been added as additional columns in the cohort table.

estimates

Estimates that we want for the columns.

ingredientConceptId

Ingredient OMOP concept that we are interested for the study.

conceptSet

List of concepts to be included.

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

restrictIncident

Whether to include only incident prescriptions in the analysis. If FALSE all prescriptions that overlap with the study period will be included.

gapEra

Number of days between two continuous exposures to be considered in the same era.

numberExposures

Whether to include 'number_exposures' (number of drug exposure records between indexDate and censorDate).

numberEras

Whether to include 'number_eras' (number of continuous exposure episodes between indexDate and censorDate).

daysExposed

daysPrescribed

Whether to include 'days_prescribed' (sum of the number of days for each prescription that contribute in the analysis).

timeToExposure

Whether to include 'time_to_exposure' (number of days between indexDate and the first episode).

initialExposureDuration

Whether to include 'initial_exposure_duration' (number of prescribed days of the first drug exposure record).

initialQuantity

Whether to include 'initial_quantity' (quantity of the first drug exposure record).

cumulativeQuantity

Whether to include 'cumulative_quantity' (sum of the quantity of the different exposures considered in the analysis).

initialDailyDose

Whether to include 'initial_daily_dose_{unit}' (daily dose of the first considered prescription).

cumulativeDose

Whether to include 'cumulative_dose_{unit}' (sum of the cumulative dose of the analysed drug exposure records).

Value

A summary of drug utilisation stratified by cohort_name and strata_name

Examples


library(DrugUtilisation)
library(CodelistGenerator)

cdm <- mockDrugUtilisation()
cdm <- generateIngredientCohortSet(cdm = cdm,
                                   ingredient = "acetaminophen",
                                   name = "dus_cohort")

cdm$dus_cohort |>
  summariseDrugUtilisation(ingredientConceptId = 1125315)

Summarise the indications of individuals in a drug cohort

Description

Summarise the observed indications of patients in a drug cohort based on their presence in an indication cohort in a specified time window. If an individual is not in one of the indication cohorts, they will be considered to have an unknown indication if they are present in one of the specified OMOP CDM clinical tables. Otherwise, if they are neither in an indication cohort or a clinical table they will be considered as having no observed indication.

Usage

summariseIndication(
  cohort,
  strata = list(),
  indicationCohortName,
  cohortId = NULL,
  indicationCohortId = NULL,
  indicationWindow = list(c(0, 0)),
  unknownIndicationTable = NULL,
  indexDate = "cohort_start_date",
  mutuallyExclusive = TRUE,
  censorDate = NULL
)

Arguments

cohort

A cohort_table object.

strata

A list of variables to stratify results. These variables must have been added as additional columns in the cohort table.

indicationCohortName

Name of the cohort table with potential indications.

cohortId

A cohort definition id to restrict by. If NULL, all cohorts will be included.

indicationCohortId

The target cohort ID to add indication. If NULL all cohorts will be considered.

indicationWindow

The time window over which to identify indications.

unknownIndicationTable

Tables in the OMOP CDM to search for unknown indications.

indexDate

Name of a column that indicates the date to start the analysis.

mutuallyExclusive

Whether to report indications as mutually exclusive or report them as independent results.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

Value

A summarised result

Examples


library(DrugUtilisation)
library(CDMConnector)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockDrugUtilisation()

indications <- list(headache = 378253, asthma = 317009)
cdm <- generateConceptCohortSet(cdm = cdm,
                                conceptSet = indications,
                                name = "indication_cohorts")

cdm <- generateIngredientCohortSet(cdm = cdm,
                                   name = "drug_cohort",
                                   ingredient = "acetaminophen")

cdm$drug_cohort |>
  summariseIndication(
    indicationCohortName = "indication_cohorts",
    unknownIndicationTable = "condition_occurrence",
    indicationWindow = list(c(-Inf, 0))
  ) |>
  glimpse()

Summarise proportion Of patients covered

Description

Gives the proportion of patients still in observation who are in the cohort on any given day following their first cohort entry. This is known as the “proportion of patients covered” (PPC) method for assessing treatment persistence.

Usage

summariseProportionOfPatientsCovered(
  cohort,
  cohortId = NULL,
  strata = list(),
  followUpDays = NULL
)

Arguments

cohort

A cohort_table object.

cohortId

A cohort definition id to restrict by. If NULL, all cohorts will be included.

strata

A list of variables to stratify results. These variables must have been added as additional columns in the cohort table.

followUpDays

Number of days to follow up individuals for. If NULL the maximum amount of days from an individuals first cohort start date to their last cohort end date will be used

Value

A summarised result

Examples


library(DrugUtilisation)

cdm <- mockDrugUtilisation(numberIndividuals = 100)

result <- cdm$cohort1 |>
  summariseProportionOfPatientsCovered(followUpDays = 365)

tidy(result)

This function is used to summarise treatments received

Description

This function is used to summarise treatments received

Usage

summariseTreatment(
  cohort,
  window,
  treatmentCohortName,
  cohortId = NULL,
  treatmentCohortId = NULL,
  strata = list(),
  indexDate = "cohort_start_date",
  censorDate = NULL,
  mutuallyExclusive = FALSE
)

Arguments

cohort

A cohort_table object.

window

Time window over which to summarise the treatments.

treatmentCohortName

Name of a cohort in the cdm that contains the treatments of interest.

cohortId

A cohort definition id to restrict by. If NULL, all cohorts will be included.

treatmentCohortId

Cohort definition id of interest from treatmentCohortName.

strata

A list of variables to stratify results. These variables must have been added as additional columns in the cohort table.

indexDate

Name of a column that indicates the date to start the analysis.

censorDate

Name of a column that indicates the date to stop the analysis, if NULL end of individuals observation is used.

mutuallyExclusive

Whether to include mutually exclusive treatments or not.

Value

A summary of treatments stratified by cohort_name and strata_name

Examples


library(DrugUtilisation)

cdm <- mockDrugUtilisation()
cdm$cohort1 |>
  summariseTreatment(
    treatmentCohortName = "cohort2",
    window = list(c(0, 30), c(31, 365))
  )

Helper for consistent documentation of `table`.

Description

Helper for consistent documentation of table.

Arguments

type

Type of table. Check supported types with visOmopResults::tableType().

header

Columns to use as header. See options with availableTableColumns(result).

groupColumn

Columns to group by. See options with availableTableColumns(result).

hide

Columns to hide from the visualisation. See options with availableTableColumns(result).

.options

A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.

Format a dose_coverage object into a visual table.

Description

Format a dose_coverage object into a visual table.

Usage

tableDoseCoverage(
  result,
  header = c("variable_name", "estimate_name"),
  groupColumn = c("cdm_name", "ingredient_name"),
  type = "gt",
  hide = c("variable_level", "sample_size"),
  .options = list()
)

Arguments

result

A summarised_result object.

header

Columns to use as header. See options with availableTableColumns(result).

groupColumn

Columns to group by. See options with availableTableColumns(result).

type

Type of table. Check supported types with visOmopResults::tableType().

hide

Columns to hide from the visualisation. See options with availableTableColumns(result).

.options

A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.

Value

A table with a formatted version of summariseDrugCoverage() results.

Examples


library(DrugUtilisation)

cdm <- mockDrugUtilisation()

result <- summariseDoseCoverage(cdm, 1125315)

tableDoseCoverage(result)

Format a drug_restart object into a visual table.

Description

Format a drug_restart object into a visual table.

Usage

tableDrugRestart(
  result,
  header = c("cdm_name", "cohort_name"),
  groupColumn = "variable_name",
  type = "gt",
  hide = c("censor_date", "restrict_to_first_discontinuation", "follow_up_days",
    "cohort_table_name", "incident", "switch_cohort_table"),
  .options = list()
)

Arguments

result

A summarised_result object.

header

Columns to use as header. See options with availableTableColumns(result).

groupColumn

Columns to group by. See options with availableTableColumns(result).

type

Type of table. Check supported types with visOmopResults::tableType().

hide

Columns to hide from the visualisation. See options with availableTableColumns(result).

.options

A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.

Value

A table with a formatted version of summariseDrugRestart() results.

Examples


library(DrugUtilisation)

cdm <- mockDrugUtilisation()

conceptlist <- list(acetaminophen = 1125360, metformin = c(1503297, 1503327))
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "switch_cohort",
                                        conceptSet = conceptlist)

result <- cdm$cohort1 |>
  summariseDrugRestart(switchCohortTable = "switch_cohort")

tableDrugRestart(result)

Format a drug_utilisation object into a visual table.

Description

Format a drug_utilisation object into a visual table.

Usage

tableDrugUtilisation(
  result,
  header = c("cdm_name"),
  groupColumn = c("cohort_name", strataColumns(result)),
  type = "gt",
  hide = c("variable_level", "censor_date", "cohort_table_name", "gap_era", "index_date",
    "restrict_incident"),
  .options = list()
)

Arguments

result

A summarised_result object.

header

Columns to use as header. See options with availableTableColumns(result).

groupColumn

Columns to group by. See options with availableTableColumns(result).

type

Type of table. Check supported types with visOmopResults::tableType().

hide

Columns to hide from the visualisation. See options with availableTableColumns(result).

.options

A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.

Value

A table with a formatted version of summariseIndication() results.

Examples


library(DrugUtilisation)
library(CodelistGenerator)

cdm <- mockDrugUtilisation()
codelist <- getDrugIngredientCodes(cdm = cdm, name = "acetaminophen")
cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "dus_cohort",
                                        conceptSet = codelist)

drugUse <- cdm$dus_cohort |>
  summariseDrugUtilisation(ingredientConceptId = 1125315)

tableDrugUtilisation(drugUse)

Create a table showing indication results

Description

Create a table showing indication results

Usage

tableIndication(
  result,
  header = c("cdm_name", "cohort_name", strataColumns(result)),
  groupColumn = "variable_name",
  hide = c("window_name", "mutually_exclusive", "unknown_indication_table",
    "censor_date", "cohort_table_name", "index_date", "indication_cohort_name"),
  type = "gt",
  .options = list()
)

Arguments

result

A summarised_result object.

header

Columns to use as header. See options with availableTableColumns(result).

groupColumn

Columns to group by. See options with availableTableColumns(result).

hide

Columns to hide from the visualisation. See options with availableTableColumns(result).

type

Type of table. Check supported types with visOmopResults::tableType().

.options

A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.

Value

A table with a formatted version of summariseIndication() results.

Examples


library(DrugUtilisation)

cdm <- mockDrugUtilisation()

result <- cdm$cohort1 |>
  summariseIndication(
    indicationCohortName = "cohort2",
    indicationWindow = list(c(-30, 0)),
    unknownIndicationTable = "condition_occurrence"
  )

tableIndication(result)

Create a table with proportion of patients covered results

Description

Create a table with proportion of patients covered results

Usage

tableProportionOfPatientsCovered(
  result,
  header = c("cohort_name", strataColumns(result)),
  groupColumn = "cdm_name",
  type = "gt",
  hide = c("variable_name", "variable_level", "cohort_table_name"),
  .options = list()
)

Arguments

result

A summarised_result object.

header

Columns to use as header. See options with availableTableColumns(result).

groupColumn

Columns to group by. See options with availableTableColumns(result).

type

Type of table. Check supported types with visOmopResults::tableType().

hide

Columns to hide from the visualisation. See options with availableTableColumns(result).

.options

A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.

Value

A table with a formatted version of summariseProportionOfPatientsCovered() results.

Examples


library(DrugUtilisation)

cdm <- mockDrugUtilisation()

cdm <- generateDrugUtilisationCohortSet(cdm = cdm,
                                        name = "my_cohort",
                                        conceptSet = list(drug_of_interest = c(1503297, 1503327)))

result <- cdm$my_cohort |>
  summariseProportionOfPatientsCovered(followUpDays = 365)

tableProportionOfPatientsCovered(result)

Format a summarised_treatment result into a visual table.

Description

Format a summarised_treatment result into a visual table.

Usage

tableTreatment(
  result,
  header = c("cdm_name", "cohort_name"),
  groupColumn = "variable_name",
  type = "gt",
  hide = c("window_name", "mutually_exclusive", "censor_date", "cohort_table_name",
    "index_date", "treatment_cohort_name"),
  .options = list()
)

Arguments

result

A summarised_result object.

header

Columns to use as header. See options with availableTableColumns(result).

groupColumn

Columns to group by. See options with availableTableColumns(result).

type

Type of table. Check supported types with visOmopResults::tableType().

hide

Columns to hide from the visualisation. See options with availableTableColumns(result).

.options

A named list with additional formatting options. visOmopResults::tableOptions() shows allowed arguments and their default values.

Value

A table with a formatted version of summariseTreatment() results.

Examples


library(DrugUtilisation)

cdm <- mockDrugUtilisation()

result <- cdm$cohort1 |>
  summariseTreatment(
    treatmentCohortName = "cohort2",
    window = list(c(0, 30), c(31, 365))
  )

tableTreatment(result)

DrugUtilisation: Summarise Patient-Level Drug Utilisation in Data Mapped to the OMOP Common Data Model

Description

Author(s)

See Also

To add a new column with the cumulative dose. To add multiple columns use addDrugUtilisation() for efficiency.

Description

Usage

Arguments

Value

Examples

To add a new column with the cumulative quantity. To add multiple columns use addDrugUtilisation() for efficiency.

Description

Usage

Arguments

Value

Examples

To add a new column with the days exposed. To add multiple columns use addDrugUtilisation() for efficiency.

Description

Usage

Arguments

Value

Examples

To add a new column with the days prescribed. To add multiple columns use addDrugUtilisation() for efficiency.

Description

Usage

Arguments

Value

Examples

Add drug restart information as a column per follow-up period of interest.

Description

Usage

Arguments

Value

Examples

Add new columns with drug use related information

Description

Usage

Arguments

Value

Examples

Add a variable indicating individuals indications

Description

Usage

Arguments

Value

Examples

To add a new column with the initial daily dose. To add multiple columns use addDrugUtilisation() for efficiency.

Description

Usage

Arguments

Value

Examples

To add a new column with the duration of the first exposure. To add multiple columns use addDrugUtilisation() for efficiency.

Description

Usage

Arguments

Value

Examples

To add a new column with the initial quantity. To add multiple columns use addDrugUtilisation() for efficiency.

Description

Usage

Arguments

Value

Examples

To add a new column with the number of eras. To add multiple columns use addDrugUtilisation() for efficiency.

Description

Usage

Arguments

Value

Examples

To add a new column with the number of exposures. To add multiple columns use addDrugUtilisation() for efficiency.

Description

Usage

Arguments

Value

Examples

To add a new column with the time to exposure. To add multiple columns use addDrugUtilisation() for efficiency.

Description

Usage

Arguments

To add a new column with the cumulative dose. To add multiple columns use `addDrugUtilisation()` for efficiency.

To add a new column with the cumulative quantity. To add multiple columns use `addDrugUtilisation()` for efficiency.

To add a new column with the days exposed. To add multiple columns use `addDrugUtilisation()` for efficiency.

To add a new column with the days prescribed. To add multiple columns use `addDrugUtilisation()` for efficiency.

To add a new column with the initial daily dose. To add multiple columns use `addDrugUtilisation()` for efficiency.

To add a new column with the duration of the first exposure. To add multiple columns use `addDrugUtilisation()` for efficiency.

To add a new column with the initial quantity. To add multiple columns use `addDrugUtilisation()` for efficiency.

To add a new column with the number of eras. To add multiple columns use `addDrugUtilisation()` for efficiency.

To add a new column with the number of exposures. To add multiple columns use `addDrugUtilisation()` for efficiency.

To add a new column with the time to exposure. To add multiple columns use `addDrugUtilisation()` for efficiency.

Helper for consistent documentation of `cdm`.

Helper for consistent documentation of `censorDate`.

Helper for consistent documentation of `cohort`.

Helper for consistent documentation of `cohortId`.

Helper for consistent documentation of `name` for computed tables.

Helper for consistent documentation of `conceptSet`.

Helper for consistent documentation of `daysPrescribed`.

Helper for consistent documentation of `add`/`summariseDrugUtilisation` functions.

Helper for consistent documentation of `gapEra`.