Type:	Package
Title:	Assess Study Cohorts Using a Common Data Model
Version:	0.1.6
Description:	Phenotype study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model. Diagnostics are run at the database, code list, cohort, and population level to assess whether study cohorts are ready for research.
License:	Apache License (≥ 2)
Encoding:	UTF-8
Depends:	R (≥ 4.1.0)
Suggests:	CDMConnector (≥ 1.6.1), duckdb, DBI, gt, omock, testthat (≥ 3.0.0), knitr, visOmopResults (≥ 1.0.0), glue, RPostgres, PatientProfiles (≥ 1.2.2), ggplot2, ggpubr, stringr, shiny, DiagrammeR, DiagrammeRsvg, reactable, reactablefmtr, rsvg, sortable, shinycssloaders, here, DT, bslib, shinyWidgets, plotly, tidyr, scales, usethis, rmarkdown, CohortSurvival (≥ 1.0.2)
Config/testthat/edition:	3
RoxygenNote:	7.3.2
Imports:	CodelistGenerator (≥ 3.4.0), CohortCharacteristics (≥ 1.0.0), CohortConstructor (≥ 0.4.0), cli, dplyr, IncidencePrevalence (≥ 1.2.0), omopgenerics (≥ 1.2.0), OmopSketch (≥ 0.5.0), magrittr, purrr, rlang, vctrs
URL:	https://ohdsi.github.io/PhenotypeR/
VignetteBuilder:	knitr
NeedsCompilation:	no
Packaged:	2025-06-22 13:15:54 UTC; eburn
Author:	Edward Burn [aut, cre], Marti Catala [aut], Xihang Chen [aut], Marta Alcalde-Herraiz [aut], Albert Prats-Uribe [aut]
Maintainer:	Edward Burn <edward.burn@ndorms.ox.ac.uk>
Repository:	CRAN
Date/Publication:	2025-06-22 13:30:02 UTC

PhenotypeR: Assess Study Cohorts Using a Common Data Model

Description

Phenotype study cohorts in data mapped to the Observational Medical Outcomes Partnership Common Data Model. Diagnostics are run at the database, code list, cohort, and population level to assess whether study cohorts are ready for research.

Author(s)

Maintainer: Edward Burn edward.burn@ndorms.ox.ac.uk (ORCID)

Authors:

• Marti Catala marti.catalasabate@ndorms.ox.ac.uk (ORCID)

• Xihang Chen xihang.chen@ndorms.ox.ac.uk (ORCID)

• Marta Alcalde-Herraiz marta.alcaldeherraiz@ndorms.ox.ac.uk (ORCID)

• Albert Prats-Uribe albert.prats-uribe@ndorms.ox.ac.uk (ORCID)

See Also

Useful links:

• https://ohdsi.github.io/PhenotypeR/

Pipe operator

Description

See magrittr::%>% for details.

Usage

lhs %>% rhs

Arguments

Value

The result of calling 'rhs(lhs)'.

Adds the cohort_codelist attribute to a cohort

Description

'addCodelistAttribute()' allows the users to add a codelist to a cohort in OMOP CDM.

This is particularly important for the use of 'codelistDiagnostics()', as the underlying assumption is that the cohort that is fed into 'codelistDiagnostics()' has a cohort_codelist attribute attached to it.

Usage

addCodelistAttribute(cohort, codelist, cohortName = names(codelist))

Arguments

Value

A cohort

Examples

library(PhenotypeR)

cdm <- mockPhenotypeR()

cohort <- addCodelistAttribute(cohort = cdm$my_cohort, codelist = list("cohort_1" = 1L))
attr(cohort, "cohort_codelist")

CDMConnector::cdmDisconnect(cdm)

Run codelist-level diagnostics

Description

'codelistDiagnostics()' runs phenotypeR diagnostics on the cohort_codelist attribute on the cohort. Thus codelist attribute of the cohort must be populated. If it is missing then it could be populated using 'addCodelistAttribute()' function.

Furthermore 'codelistDiagnostics()' requires achilles tables to be present in the cdm so that concept counts could be derived.

Usage

codelistDiagnostics(cohort)

Arguments

Value

A summarised result

Examples

library(CohortConstructor)
library(PhenotypeR)

cdm <- mockPhenotypeR()

cdm$arthropathies <- conceptCohort(cdm,
                                   conceptSet = list("arthropathies" = c(40475132)),
                                   name = "arthropathies")

result <- codelistDiagnostics(cdm$arthropathies)

CDMConnector::cdmDisconnect(cdm = cdm)

Run cohort-level diagnostics

Description

Runs phenotypeR diagnostics on the cohort. The diganostics include: * Age groups and sex summarised. * A summary of visits of everyone in the cohort using visit_occurrence table. * A summary of age and sex density of the cohort. * Attritions of the cohorts. * Overlap between cohorts (if more than one cohort is being used).

Usage

cohortDiagnostics(cohort, survival = FALSE, match = TRUE, matchedSample = 1000)

Arguments

Value

A summarised result

Examples

library(PhenotypeR)

cdm <- mockPhenotypeR()

result <- cohortDiagnostics(cdm$my_cohort,
                            match = TRUE)

CDMConnector::cdmDisconnect(cdm = cdm)

Helper for consistent documentation of 'cohort'.

Description

Helper for consistent documentation of 'cohort'.

Arguments

Database diagnostics

Description

phenotypeR diagnostics on the cdm object.

Diagnostics include: * Summarise a cdm_reference object, creating a snapshot with the metadata of the cdm_reference object. * Summarise the observation period table getting some overall statistics in a summarised_result object.

Usage

databaseDiagnostics(cdm)

Arguments

Value

A summarised result

Examples

library(PhenotypeR)

cdm <- mockPhenotypeR()

result <- databaseDiagnostics(cdm)

CDMConnector::cdmDisconnect(cdm = cdm)

Helper for consistent documentation of 'directory'.

Description

Helper for consistent documentation of 'directory'.

Arguments

Helper for consistent documentation of 'matched' and 'match'.

Description

Helper for consistent documentation of 'matched' and 'match'.

Arguments

Function to create a mock cdm reference for mockPhenotypeR

Description

'mockPhenotypeR()' creates an example dataset that can be used to show how the package works

Usage

mockPhenotypeR(
  nPerson = 100,
  con = DBI::dbConnect(duckdb::duckdb()),
  writeSchema = "main",
  seed = 111
)

Arguments

Value

cdm object

Examples

library(PhenotypeR)

cdm <- mockPhenotypeR()

cdm

Phenotype a cohort

Description

This comprises all the diagnostics that are being offered in this package, this includes:

* A diagnostics on the database via 'databaseDiagnostics'. * A diagnostics on the cohort_codelist attribute of the cohort via 'codelistDiagnostics'. * A diagnostics on the cohort via 'cohortDiagnostics'. * A diagnostics on the population via 'populationDiagnostics'. * A diagnostics on the matched cohort via 'matchedDiagnostics'.

Usage

phenotypeDiagnostics(
  cohort,
  databaseDiagnostics = TRUE,
  codelistDiagnostics = TRUE,
  cohortDiagnostics = TRUE,
  survival = FALSE,
  match = TRUE,
  matchedSample = 1000,
  populationDiagnostics = TRUE,
  populationSample = 1e+06,
  populationDateRange = as.Date(c(NA, NA))
)

Arguments

Value

A summarised result

Examples

library(PhenotypeR)

cdm <- mockPhenotypeR()

result <- phenotypeDiagnostics(cdm$my_cohort)

CDMConnector::cdmDisconnect(cdm = cdm)

Population-level diagnostics

Description

phenotypeR diagnostics on the cohort of input with relation to a denomination population. Diagnostics include:

* Incidence * Prevalence

Usage

populationDiagnostics(
  cohort,
  populationSample = 1e+06,
  populationDateRange = as.Date(c(NA, NA))
)

Arguments

Value

A summarised result

Examples

library(PhenotypeR)
library(dplyr)

cdm <- mockPhenotypeR()

dateStart <- cdm$my_cohort |>
  summarise(start = min(cohort_start_date, na.rm = TRUE)) |>
  pull("start")
dateEnd   <- cdm$my_cohort |>
  summarise(start = max(cohort_start_date, na.rm = TRUE)) |>
  pull("start")

result <- cdm$my_cohort |>
  populationDiagnostics(populationDateRange = c(dateStart, dateEnd))

CDMConnector::cdmDisconnect(cdm = cdm)

Helper for consistent documentation of 'populationSample'.

Description

Helper for consistent documentation of 'populationSample'.

Arguments

Objects exported from other packages

Description

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

CodelistGenerator

summariseAchillesCodeUse, summariseCodeUse, summariseCohortCodeUse, summariseOrphanCodes

omopgenerics

bind, exportSummarisedResult, importSummarisedResult, settings, suppress

Helper for consistent documentation of 'result'.

Description

Helper for consistent documentation of 'result'.

Arguments

Create a shiny app summarising your phenotyping results

Description

A shiny app that is designed for any diagnostics results from phenotypeR, this includes:

* A diagnostics on the database via 'databaseDiagnostics'. * A diagnostics on the cohort_codelist attribute of the cohort via 'codelistDiagnostics'. * A diagnostics on the cohort via 'cohortDiagnostics'. * A diagnostics on the population via 'populationDiagnostics'. * A diagnostics on the matched cohort via 'matchedDiagnostics'.

Usage

shinyDiagnostics(
  result,
  directory,
  minCellCount = 5,
  open = rlang::is_interactive()
)

Arguments

Value

A shiny app

Examples

library(PhenotypeR)

cdm <- mockPhenotypeR()

result <- phenotypeDiagnostics(cdm$my_cohort)

shinyDiagnostics(result, tempdir())

CDMConnector::cdmDisconnect(cdm = cdm)

Helper for consistent documentation of 'survival'.

Description

Helper for consistent documentation of 'survival'.

Arguments