Type: Package
Title: Summarise and Visualise Characteristics of Patients in the OMOP CDM
Version: 1.0.0
Maintainer: Marti Catala <marti.catalasabate@ndorms.ox.ac.uk>
Description: Summarise and visualise the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model (CDM).
License: Apache License (≥ 2)
Encoding: UTF-8
RoxygenNote: 7.3.2
Imports: CDMConnector (≥ 1.6.0), dplyr, tidyr, rlang, cli, stringr, omopgenerics (≥ 1.2.0), PatientProfiles (≥ 1.3.1), snakecase, lifecycle, purrr
URL: https://darwin-eu.github.io/CohortCharacteristics/
BugReports: https://github.com/darwin-eu/CohortCharacteristics/issues
Language: en-US
Depends: R (≥ 4.1)
Config/testthat/edition: 3
Config/testthat/parallel: true
VignetteBuilder: knitr
Suggests: CodelistGenerator, visOmopResults (≥ 1.0.0), CohortConstructor, covr, DBI, dbplyr, DiagrammeR, DiagrammeRsvg, DrugUtilisation, DT, duckdb (≥ 1.0.0), flextable, ggplot2, ggpubr, glue, grid, gt, here, Hmisc, htmltools, knitr, odbc, omock, plotly, png, reactable, rmarkdown, RPostgres, rsvg, scales, spelling, testthat (≥ 3.1.5), tictoc, withr, reactable, DT
NeedsCompilation: no
Packaged: 2025-05-20 14:27:03 UTC; martics
Author: Marti Catala ORCID iD [aut, cre], Yuchen Guo ORCID iD [aut], Mike Du ORCID iD [ctb], Kim Lopez-Guell ORCID iD [aut], Edward Burn ORCID iD [aut], Nuria Mercade-Besora ORCID iD [aut], Marta Alcalde ORCID iD [aut]
Repository: CRAN
Date/Publication: 2025-05-20 22:30:11 UTC

CohortCharacteristics: Summarise and Visualise Characteristics of Patients in the OMOP CDM

Description

logo

Summarise and visualise the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model (CDM).

Author(s)

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

Authors:

Other contributors:

See Also

Useful links:

Available columns to use in facet and colour arguments in plot functions.

Description

Available columns to use in facet and colour arguments in plot functions.

Usage

availablePlotColumns(result)

Arguments

result

A summarised_result object.

Value

Character vector with the available columns.

Examples

{
cdm <- mockCohortCharacteristics()

result <- summariseCharacteristics(cdm$cohort1)

availablePlotColumns(result)

mockDisconnect(cdm)
}

Available columns to use in header, groupColumn and hide arguments in table functions.

Description

Available columns to use in header, groupColumn and hide arguments in table functions.

Usage

availableTableColumns(result)

Arguments

result

A summarised_result object.

Value

Character vector with the available columns.

Examples

{
cdm <- mockCohortCharacteristics()

result <- summariseCharacteristics(cdm$cohort1)

availableTableColumns(result)

mockDisconnect(cdm)
}

Benchmark the main functions of CohortCharacteristics package.

Description

Benchmark the main functions of CohortCharacteristics package.

Usage

benchmarkCohortCharacteristics(
  cohort,
  analysis = c("count", "attrition", "characteristics", "overlap", "timing",
    "large scale characteristics")
)

Arguments

cohort

A cohort_table from a cdm_reference.

analysis

Set of analysis to perform, must be a subset of: "count", "attrition", "characteristics", "overlap", "timing" and "large scale characteristics".

Value

A summarised_result object.

Examples

## Not run: 
CDMConnector::requireEunomia()
con <- duckdb::dbConnect(duckdb::duckdb(), CDMConnector::eunomiaDir())
cdm <- CDMConnector::cdmFromCon(
  con = con, cdmSchema = "main", writeSchema = "main"
)

cdm <- CDMConnector::generateConceptCohortSet(
  cdm = cdm,
  conceptSet = list(sinusitis = 40481087, pharyngitis = 4112343),
  name = "my_cohort"
)

benchmarkCohortCharacteristics(cdm$my_cohort)


## End(Not run)

Helper for consistent documentation of cohort.

Description

Helper for consistent documentation of cohort.

Arguments

cohort

A cohort_table object.

Helper for consistent documentation of cohortId.

Description

Helper for consistent documentation of cohortId.

Arguments

cohortId

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

It creates a mock database for testing CohortCharacteristics package

Description

It creates a mock database for testing CohortCharacteristics package

Usage

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

Arguments

con

A DBI connection to create the cdm mock object.

writeSchema

Name of an schema on the same connection with writing permissions.

numberIndividuals

Number of individuals to create in the cdm reference.

...

User self defined tables to put in cdm, it can input as many as the user want.

seed

A number to set the seed. If NULL seed is not used.

Value

A mock cdm_reference object created following user's specifications.

Examples


library(CohortCharacteristics)
library(CDMConnector)

cdm <- mockCohortCharacteristics()

mockDisconnect(cdm = cdm)

Create a ggplot from the output of summariseCharacteristics.

Description

[Experimental]

Usage

plotCharacteristics(
  result,
  plotType = "barplot",
  facet = NULL,
  colour = NULL,
  plotStyle = lifecycle::deprecated()
)

Arguments

result

A summarised_result object.

plotType

Either barplot, scatterplot or boxplot. If barplot or scatterplot subset to just one estimate.

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).

plotStyle

deprecated.

Value

A ggplot.

Examples


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

cdm <- mockCohortCharacteristics()

results <- summariseCharacteristics(
  cohort = cdm$cohort1,
  ageGroup = list(c(0, 19), c(20, 39), c(40, 59), c(60, 79), c(80, 150)),
  tableIntersectCount = list(
    tableName = "visit_occurrence", window = c(-365, -1)
  ),
  cohortIntersectFlag = list(
    targetCohortTable = "cohort2", window = c(-365, -1)
  )
)

results |>
  filter(
    variable_name == "Cohort2 flag -365 to -1", estimate_name == "percentage"
  ) |>
  plotCharacteristics(
    plotType = "barplot",
    colour = "variable_level",
    facet = c("cdm_name", "cohort_name")
  )

results |>
  filter(variable_name == "Age", estimate_name == "mean") |>
  plotCharacteristics(
    plotType = "scatterplot",
    facet = "cdm_name"
  )

results |>
  filter(variable_name == "Age", group_level == "cohort_1") |>
  plotCharacteristics(
    plotType = "boxplot",
    facet = "cdm_name",
    colour = "cohort_name"
  )

mockDisconnect(cdm)

create a ggplot from the output of summariseLargeScaleCharacteristics.

Description

[Experimental]

Usage

plotCohortAttrition(
  result,
  show = c("subjects", "records"),
  type = "htmlwidget",
  cohortId = lifecycle::deprecated()
)

Arguments

result

A summarised_result object.

show

Which variables to show in the attrition plot, it can be 'subjects', 'records' or both.

type

type of the output, it can either be: 'htmlwidget', 'png', or 'DiagrammeR'.

cohortId

deprecated.

Value

A grViz visualisation.

Examples


library(CohortCharacteristics)
library(omopgenerics)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockCohortCharacteristics(numberIndividuals = 1000)

cdm[["cohort1"]] <- cdm[["cohort1"]] |>
  filter(year(cohort_start_date) >= 2000) |>
  recordCohortAttrition("Restrict to cohort_start_date >= 2000") |>
  filter(year(cohort_end_date) < 2020) |>
  recordCohortAttrition("Restrict to cohort_end_date < 2020") |>
  compute(temporary = FALSE, name = "cohort1")

result <- summariseCohortAttrition(cdm$cohort1)

result |>
  filter(group_level == "cohort_2") |>
  plotCohortAttrition()

mockDisconnect(cdm)

Plot the result of summariseCohortCount.

Description

[Experimental]

Usage

plotCohortCount(result, x = NULL, facet = c("cdm_name"), colour = NULL)

Arguments

result

A summarised_result object.

x

Variables to use in x axis.

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 ggplot.

Examples


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

cdm <- mockCohortCharacteristics(numberIndividuals = 100)

counts <- cdm$cohort2 |>
  addSex() |>
  addAge(ageGroup = list(c(0, 29), c(30, 59), c(60, Inf))) |>
  summariseCohortCount(strata = list("age_group", "sex", c("age_group", "sex"))) |>
  filter(variable_name == "Number subjects")

counts |>
  plotCohortCount(
    x = "sex",
    facet = cohort_name ~ age_group,
    colour = "sex"
  )

mockDisconnect(cdm)

Plot the result of summariseCohortOverlap.

Description

[Experimental]

Usage

plotCohortOverlap(
  result,
  uniqueCombinations = TRUE,
  facet = c("cdm_name", "cohort_name_reference"),
  colour = "variable_name",
  .options = lifecycle::deprecated()
)

Arguments

result

A summarised_result object.

uniqueCombinations

Whether to restrict to unique reference and comparator comparisons.

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).

.options

deprecated.

Value

A ggplot.

Examples


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

overlap <- summariseCohortOverlap(cdm$cohort2)

plotCohortOverlap(overlap, uniqueCombinations = FALSE)

mockDisconnect(cdm)

Plot summariseCohortTiming results.

Description

[Experimental]

Usage

plotCohortTiming(
  result,
  plotType = "boxplot",
  timeScale = "days",
  uniqueCombinations = TRUE,
  facet = c("cdm_name", "cohort_name_reference"),
  colour = c("cohort_name_comparator")
)

Arguments

result

A summarised_result object.

plotType

Type of desired formatted table, possibilities are "boxplot" and "densityplot".

timeScale

Time scale to show, it can be "days" or "years".

uniqueCombinations

Whether to restrict to unique reference and comparator comparisons.

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 ggplot.

Examples

## Not run: 
library(CohortCharacteristics)
library(duckdb)
library(CDMConnector)
library(DrugUtilisation)

con <- dbConnect(duckdb(), eunomiaDir())
cdm <- cdmFromCon(con, cdmSchem = "main", writeSchema = "main")

cdm <- generateIngredientCohortSet(
  cdm = cdm,
  name = "my_cohort",
  ingredient = c("acetaminophen", "morphine", "warfarin")
)

timings <- summariseCohortTiming(cdm$my_cohort)

plotCohortTiming(
  timings,
  timeScale = "years",
  uniqueCombinations = FALSE,
  facet = c("cdm_name", "cohort_name_reference"),
  colour = c("cohort_name_comparator")
)

plotCohortTiming(
  timings,
  plotType = "densityplot",
  timeScale = "years",
  uniqueCombinations = FALSE,
  facet = c("cdm_name", "cohort_name_reference"),
  colour = c("cohort_name_comparator")
)

cdmDisconnect(cdm)

## End(Not run)

create a ggplot from the output of summariseLargeScaleCharacteristics.

Description

[Experimental]

Usage

plotComparedLargeScaleCharacteristics(
  result,
  colour,
  reference = NULL,
  facet = NULL,
  missings = 0
)

Arguments

result

A summarised_result object.

colour

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

reference

A named character to set up the reference. It must be one of the levels of reference.

facet

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

missings

Value to replace the missing value with. If NULL missing values will be eliminated.

Value

A ggplot.

Examples

## Not run: 
library(CohortCharacteristics)
library(duckdb)
library(CDMConnector)
library(DrugUtilisation)
library(plotly, warn.conflicts = FALSE)

con <- dbConnect(duckdb(), eunomiaDir())
cdm <- cdmFromCon(con, cdmSchem = "main", writeSchema = "main")

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

resultsLsc <- cdm$my_cohort |>
  summariseLargeScaleCharacteristics(
    window = list(c(-365, -1), c(1, 365)),
    eventInWindow = "condition_occurrence"
  )

resultsLsc |>
  plotComparedLargeScaleCharacteristics(
    colour = "variable_level",
    reference = "-365 to -1",
    missings = NULL
  ) |>
  ggplotly()

cdmDisconnect(cdm)

## End(Not run)

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).

create a ggplot from the output of summariseLargeScaleCharacteristics.

Description

[Experimental]

Usage

plotLargeScaleCharacteristics(
  result,
  facet = c("cdm_name", "cohort_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(CohortCharacteristics)
library(duckdb)
library(CDMConnector)
library(DrugUtilisation)

con <- dbConnect(duckdb(), eunomiaDir())
cdm <- cdmFromCon(con, cdmSchem = "main", writeSchema = "main")

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

resultsLsc <- cdm$my_cohort |>
  summariseLargeScaleCharacteristics(
    window = list(c(-365, -1), c(1, 365)),
    eventInWindow = "condition_occurrence"
  )

resultsLsc |>
  plotLargeScaleCharacteristics(
    facet = c("cdm_name", "cohort_name"),
    colour = "variable_level"
  )

cdmDisconnect(cdm)

## 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, bind, exportSummarisedResult, groupColumns, importSummarisedResult, settings, settingsColumns, strataColumns, suppress, tidy

PatientProfiles

mockDisconnect

Helper for consistent documentation of result.

Description

Helper for consistent documentation of result.

Arguments

result

A summarised_result object.

Helper for consistent documentation of strata.

Description

Helper for consistent documentation of strata.

Arguments

strata

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

Summarise characteristics of cohorts in a cohort table

Description

Summarise characteristics of cohorts in a cohort table

Usage

summariseCharacteristics(
  cohort,
  cohortId = NULL,
  strata = list(),
  counts = TRUE,
  demographics = TRUE,
  ageGroup = NULL,
  tableIntersectFlag = list(),
  tableIntersectCount = list(),
  tableIntersectDate = list(),
  tableIntersectDays = list(),
  cohortIntersectFlag = list(),
  cohortIntersectCount = list(),
  cohortIntersectDate = list(),
  cohortIntersectDays = list(),
  conceptIntersectFlag = list(),
  conceptIntersectCount = list(),
  conceptIntersectDate = list(),
  conceptIntersectDays = list(),
  otherVariables = character(),
  estimates = list(),
  weights = NULL,
  otherVariablesEstimates = lifecycle::deprecated()
)

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.

counts

TRUE or FALSE. If TRUE, record and person counts will be produced.

demographics

TRUE or FALSE. If TRUE, patient demographics (cohort start date, cohort end date, age, sex, prior observation, and future observation will be summarised).

ageGroup

A list of age groups to stratify results by.

tableIntersectFlag

A list of arguments that uses PatientProfiles::addTableIntersectFlag() to add variables to summarise.

tableIntersectCount

A list of arguments that uses PatientProfiles::addTableIntersectCount() to add variables to summarise.

tableIntersectDate

A list of arguments that uses PatientProfiles::addTableIntersectDate() to add variables to summarise.

tableIntersectDays

A list of arguments that uses PatientProfiles::addTableIntersectDays() to add variables to summarise.

cohortIntersectFlag

A list of arguments that uses PatientProfiles::addCohortIntersectFlag() to add variables to summarise.

cohortIntersectCount

A list of arguments that uses PatientProfiles::addCohortIntersectCount() to add variables to summarise.

cohortIntersectDate

A list of arguments that uses PatientProfiles::addCohortIntersectDate() to add variables to summarise.

cohortIntersectDays

A list of arguments that uses PatientProfiles::addCohortIntersectDays() to add variables to summarise.

conceptIntersectFlag

A list of arguments that uses PatientProfiles::addConceptIntersectFlag() to add variables to summarise.

conceptIntersectCount

A list of arguments that uses PatientProfiles::addConceptIntersectCount() to add variables to summarise.

conceptIntersectDate

A list of arguments that uses PatientProfiles::addConceptIntersectDate() to add variables to summarise.

conceptIntersectDays

A list of arguments that uses PatientProfiles::addConceptIntersectDays() to add variables to summarise.

otherVariables

Other variables contained in cohort that you want to be summarised.

estimates

To modify the default estimates for a variable. By default: 'min', 'q25', 'median', 'q75', 'max' for "date", "numeric" and "integer" variables ("numeric" and "integer" also use 'mean' and 'sd' estimates). 'count' and 'percentage' for "categorical" and "binary". You have to provide them as a list: list(age = c("median", "density")). You can also use 'date', 'numeric', 'integer', 'binary', 'categorical', 'demographics', 'intersect', 'other', 'table_intersect_count', ...

weights

Column in cohort that points to weights of each individual.

otherVariablesEstimates

deprecated.

Value

A summary of the characteristics of the cohorts in the cohort table.

Examples


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

cdm <- mockCohortCharacteristics()

cdm$cohort1 |>
  addSex() |>
  addAge(
    ageGroup = list(c(0, 40), c(41, 150))
  ) |>
  summariseCharacteristics(
    strata = list("sex", "age_group"),
    cohortIntersectFlag = list(
      "Cohort 2 Flag" = list(
        targetCohortTable = "cohort2", window = c(-365, 0)
      )
    ),
    cohortIntersectCount = list(
      "Cohort 2 Count" = list(
        targetCohortTable = "cohort2", window = c(-365, 0)
      )
    )
  ) |>
  glimpse()

mockDisconnect(cdm)

Summarise attrition associated with cohorts in a cohort table

Description

Summarise attrition associated with cohorts in a cohort table

Usage

summariseCohortAttrition(cohort, cohortId = NULL)

Arguments

cohort

A cohort_table object.

cohortId

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

Value

A summary of the attrition for the cohorts in the cohort table.

Examples


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

cdm <- mockCohortCharacteristics()

summariseCohortAttrition(cohort = cdm$cohort1) |>
  glimpse()

mockDisconnect(cdm)

Summarise the cohort codelist attribute

Description

[Experimental]

Usage

summariseCohortCodelist(cohort, cohortId = NULL)

Arguments

cohort

A cohort_table object.

cohortId

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

Value

A summarised_result object with the exported cohort codelist information.

Examples


library(CohortCharacteristics)
library(CDMConnector)
library(duckdb)
library(dplyr, warn.conflicts = FALSE)

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

cdm <- generateConceptCohortSet(cdm = cdm,
                                conceptSet = list(pharyngitis = 4112343L),
                                name = "my_cohort")

result <- summariseCohortCodelist(cdm$my_cohort)

glimpse(result)

tidy(result)

Summarise counts for cohorts in a cohort table

Description

Summarise counts for cohorts in a cohort table

Usage

summariseCohortCount(cohort, cohortId = NULL, strata = list())

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.

Value

A summary of counts of the cohorts in the cohort table.

Examples


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

cdm <- mockCohortCharacteristics()

summariseCohortCount(cohort = cdm$cohort1) |>
  glimpse()

mockDisconnect(cdm)

Summarise overlap between cohorts in a cohort table

Description

Summarise overlap between cohorts in a cohort table

Usage

summariseCohortOverlap(
  cohort,
  cohortId = NULL,
  strata = list(),
  overlapBy = "subject_id"
)

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.

overlapBy

Columns in cohort to use as record identifiers.

Value

A summary of overlap between cohorts in the cohort table.

Examples


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

cdm <- mockCohortCharacteristics()

summariseCohortOverlap(cdm$cohort2) |>
  glimpse()

mockDisconnect(cdm)

Summarise timing between entries into cohorts in a cohort table

Description

Summarise timing between entries into cohorts in a cohort table

Usage

summariseCohortTiming(
  cohort,
  cohortId = NULL,
  strata = list(),
  restrictToFirstEntry = TRUE,
  estimates = c("min", "q25", "median", "q75", "max", "density"),
  density = lifecycle::deprecated()
)

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.

restrictToFirstEntry

If TRUE only an individual's first entry per cohort will be considered. If FALSE all entries per individual will be considered.

estimates

Summary statistics to use when summarising timing.

density

deprecated.

Value

A summary of timing between entries into cohorts in the cohort table.

Examples


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

cdm <- mockCohortCharacteristics(numberIndividuals = 100)

summariseCohortTiming(cdm$cohort2) |>
  glimpse()

mockDisconnect(cdm)

This function is used to summarise the large scale characteristics of a cohort table

Description

This function is used to summarise the large scale characteristics of a cohort table

Usage

summariseLargeScaleCharacteristics(
  cohort,
  cohortId = NULL,
  strata = list(),
  window = list(c(-Inf, -366), c(-365, -31), c(-30, -1), c(0, 0), c(1, 30), c(31, 365),
    c(366, Inf)),
  eventInWindow = NULL,
  episodeInWindow = NULL,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  includeSource = FALSE,
  minimumFrequency = 0.005,
  excludedCodes = c(0)
)

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.

window

Temporal windows that we want to characterize.

eventInWindow

Tables to characterise the events in the window. eventInWindow must be provided if episodeInWindow is not specified.

episodeInWindow

Tables to characterise the episodes in the window. episodeInWindow must be provided if eventInWindow is not specified.

indexDate

Variable in x that contains the date to compute the intersection.

censorDate

whether to censor overlap events at a specific date or a column date of x

includeSource

Whether to include source concepts.

minimumFrequency

Minimum frequency of codes to be reported. If a concept_id has a frequency smaller than minimumFrequency in a certain window that estimate will be eliminated from the result object.

excludedCodes

Codes excluded.

Value

The output of this function is a ResultSummary containing the relevant information.

Examples

## Not run: 
library(CohortCharacteristics)
library(duckdb)
library(CDMConnector)
library(DrugUtilisation)
library(dplyr, warn.conflicts = FALSE)

con <- dbConnect(duckdb(), eunomiaDir())
cdm <- cdmFromCon(con, cdmSchem = "main", writeSchema = "main")

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

cdm$my_cohort |>
  summariseLargeScaleCharacteristics(
    window = list(c(-365, -1), c(1, 365)),
    eventInWindow = "condition_occurrence"
  ) |>
  glimpse()

cdmDisconnect(cdm)

## End(Not run)

Format a summarise_characteristics object into a visual table.

Description

[Experimental]

Usage

tableCharacteristics(
  result,
  type = "gt",
  header = c("cdm_name", "cohort_name"),
  groupColumn = character(),
  hide = c(additionalColumns(result), settingsColumns(result)),
  .options = list()
)

Arguments

result

A summarised_result object.

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.

Value

A formatted table.

Examples


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

result <- summariseCharacteristics(cdm$cohort1)

tableCharacteristics(result)

mockDisconnect(cdm)

Create a visual table from the output of summariseCohortAttrition.

Description

[Experimental]

Usage

tableCohortAttrition(
  result,
  type = "gt",
  header = "variable_name",
  groupColumn = c("cdm_name", "cohort_name"),
  hide = c("variable_level", "reason_id", "estimate_name", settingsColumns(result)),
  .options = list()
)

Arguments

result

A summarised_result object.

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.

Value

A formatted table.

Examples


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

result <- summariseCohortAttrition(cdm$cohort2)

tableCohortAttrition(result)

mockDisconnect(cdm)

Create a visual table from ⁠<summarised_result>⁠ object from summariseCohortCodelist()

Description

[Experimental]

Usage

tableCohortCodelist(result, type = "reactable")

Arguments

result

A summarised_result object.

type

Type of table. Supported types: "gt", "flextable", "tibble", "datatable", "reactable".

Value

A visual table with the results.

Examples


library(CohortCharacteristics)
library(CDMConnector)
library(duckdb)
library(dplyr, warn.conflicts = FALSE)

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

cdm <- generateConceptCohortSet(cdm = cdm,
                                conceptSet = list(pharyngitis = 4112343L),
                                name = "my_cohort")

result <- summariseCohortCodelist(cdm$my_cohort)

tableCohortCodelist(result)

Format a summarise_characteristics object into a visual table.

Description

[Experimental]

Usage

tableCohortCount(
  result,
  type = "gt",
  header = "cohort_name",
  groupColumn = character(),
  hide = c("variable_level", settingsColumns(result)),
  .options = list()
)

Arguments

result

A summarised_result object.

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.

Value

A formatted table.

Examples


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

result <- summariseCohortCount(cdm$cohort1)

tableCohortCount(result)

mockDisconnect(cdm = cdm)

Format a summariseOverlapCohort result into a visual table.

Description

[Experimental]

Usage

tableCohortOverlap(
  result,
  uniqueCombinations = TRUE,
  type = "gt",
  header = c("variable_name"),
  groupColumn = c("cdm_name"),
  hide = c("variable_level", settingsColumns(result)),
  .options = list()
)

Arguments

result

A summarised_result object.

uniqueCombinations

Whether to restrict to unique reference and comparator comparisons.

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.

Value

A formatted table.

Examples


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

overlap <- summariseCohortOverlap(cdm$cohort2)

tableCohortOverlap(overlap)

mockDisconnect(cdm = cdm)

Format a summariseCohortTiming result into a visual table.

Description

[Experimental]

Usage

tableCohortTiming(
  result,
  timeScale = "days",
  uniqueCombinations = TRUE,
  type = "gt",
  header = strataColumns(result),
  groupColumn = c("cdm_name"),
  hide = c("variable_level", settingsColumns(result)),
  .options = list()
)

Arguments

result

A summarised_result object.

timeScale

Time scale to show, it can be "days" or "years".

uniqueCombinations

Whether to restrict to unique reference and comparator comparisons.

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.

Value

A formatted table.

Examples

## Not run: 
library(CohortCharacteristics)
library(duckdb)
library(CDMConnector)
library(DrugUtilisation)

con <- dbConnect(duckdb(), eunomiaDir())
cdm <- cdmFromCon(con, cdmSchem = "main", writeSchema = "main")

cdm <- generateIngredientCohortSet(
  cdm = cdm,
  name = "my_cohort",
  ingredient = c("acetaminophen", "morphine", "warfarin")
)

timings <- summariseCohortTiming(cdm$my_cohort)

tableCohortTiming(timings, timeScale = "years")

cdmDisconnect(cdm)

## End(Not run)

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.

Explore and compare the large scale characteristics of cohorts

Description

Explore and compare the large scale characteristics of cohorts

Usage

tableLargeScaleCharacteristics(
  result,
  compareBy = NULL,
  hide = c("type"),
  smdReference = NULL,
  type = "reactable"
)

Arguments

result

A summarised_result object.

compareBy

A column to compare by it can be a choice between "cdm_name", "cohort_name", strata columns, "variable_level" (window) and "type". It can be left NULL for no comparison.

hide

Columns to hide.

smdReference

Level of reference for the Standardised Mean Differences (SMD), it has to be one of the values of compareBy column. If NULL no SMDs are displayed.

type

Type of table to generate, it can be either DT or reactable.

Value

A visual table.

Examples

## Not run: 
library(CohortCharacteristics)
library(duckdb)
library(CDMConnector)
library(dplyr, warn.conflicts = FALSE)

con <- dbConnect(duckdb(), eunomiaDir())
cdm <- cdmFromCon(con = con, cdmSchema = "main", writeSchema = "main")
cdm <- generateConceptCohortSet(
  cdm = cdm,
  conceptSet = list(viral_pharyngitis = 4112343),
  name = "my_cohort"
)

result <- summariseLargeScaleCharacteristics(
  cohort = cdm$my_cohort,
  window = list(c(-Inf, -1), c(0, 0), c(1, Inf)),
  episodeInWindow = "drug_exposure"
)

tableLargeScaleCharacteristics(result)

tableLargeScaleCharacteristics(result,
                               compareBy = "variable_level")

tableLargeScaleCharacteristics(result,
                               compareBy = "variable_level",
                               smdReference = "-inf to -1")

cdmDisconnect(cdm)

## End(Not run)

Visualise the top concepts per each cdm name, cohort, statification and window.

Description

Visualise the top concepts per each cdm name, cohort, statification and window.

Usage

tableTopLargeScaleCharacteristics(result, topConcepts = 10, type = "gt")

Arguments

result

A summarised_result object.

topConcepts

Number of concepts to restrict the table.

type

Type of table, it can be any of the supported visOmopResults::tableType() formats.

Value

A formated table.

Examples

## Not run: 
library(CohortCharacteristics)
library(duckdb)
library(CDMConnector)
library(dplyr, warn.conflicts = FALSE)

con <- dbConnect(duckdb(), eunomiaDir())
cdm <- cdmFromCon(con = con, cdmSchema = "main", writeSchema = "main")
cdm <- generateConceptCohortSet(
  cdm = cdm,
  conceptSet = list(viral_pharyngitis = 4112343),
  name = "my_cohort"
)

result <- summariseLargeScaleCharacteristics(
  cohort = cdm$my_cohort,
  window = list(c(-Inf, -1), c(0, 0), c(1, Inf)),
  episodeInWindow = "drug_exposure"
)

tableTopLargeScaleCharacteristics(result)

cdmDisconnect(cdm)

## End(Not run)

Helper for consistent documentation of timeScale.

Description

Helper for consistent documentation of timeScale.

Arguments

timeScale

Time scale to show, it can be "days" or "years".

Helper for consistent documentation of uniqueCombinations.

Description

Helper for consistent documentation of uniqueCombinations.

Arguments

uniqueCombinations

Whether to restrict to unique reference and comparator comparisons.