Package 'CohortCharacteristics' reference manual

Title:	Summarise and Visualise Characteristics of Patients in the OMOP CDM
Description:	Summarise and visualise the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model (CDM).
Authors:	Martí Català [aut, cre] (ORCID: <https://orcid.org/0000-0003-3308-9905>), Yuchen Guo [aut] (ORCID: <https://orcid.org/0000-0002-0847-4855>), Mike Du [ctb] (ORCID: <https://orcid.org/0000-0002-9517-8834>), Kim Lopez-Guell [aut] (ORCID: <https://orcid.org/0000-0002-8462-8668>), Edward Burn [aut] (ORCID: <https://orcid.org/0000-0002-9286-1128>), Nuria Mercade-Besora [aut] (ORCID: <https://orcid.org/0009-0006-7948-3747>), Marta Alcalde [aut] (ORCID: <https://orcid.org/0009-0002-4405-1814>)
Maintainer:	Martí Català <[email protected]>
License:	Apache License (>= 2)
Version:	1.1.1
Built:	2026-06-07 07:16:42 UTC
Source:	https://github.com/darwin-eu/cohortcharacteristics

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)
availablePlotColumns(result)

Arguments

result

A summarised_result object.

Value

Character vector with the available columns.

Examples

{
cdm <- mockCohortCharacteristics()

result <- summariseCharacteristics(cdm$cohort1)

availablePlotColumns(result)

}
{
cdm <- mockCohortCharacteristics()

result <- summariseCharacteristics(cdm$cohort1)

availablePlotColumns(result)

}

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)
availableTableColumns(result)

Arguments

result

A summarised_result object.

Value

Character vector with the available columns.

Examples

{
cdm <- mockCohortCharacteristics()

result <- summariseCharacteristics(cdm$cohort1)

availableTableColumns(result)

}
{
cdm <- mockCohortCharacteristics()

result <- summariseCharacteristics(cdm$cohort1)

availableTableColumns(result)

}

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")
)
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: 
library(CohortCharacteristics)
library(omock)
library(CDMConnector)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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

benchmarkCohortCharacteristics(cdm$my_cohort)


## End(Not run)
## Not run: 
library(CohortCharacteristics)
library(omock)
library(CDMConnector)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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

benchmarkCohortCharacteristics(cdm$my_cohort)


## End(Not run)

It creates a mock database for testing CohortCharacteristics package

Description

It creates a mock database for testing CohortCharacteristics package

Usage

mockCohortCharacteristics(
  numberIndividuals = 10,
  ...,
  source = "local",
  con = lifecycle::deprecated(),
  writeSchema = lifecycle::deprecated(),
  seed = lifecycle::deprecated()
)
mockCohortCharacteristics(
  numberIndividuals = 10,
  ...,
  source = "local",
  con = lifecycle::deprecated(),
  writeSchema = lifecycle::deprecated(),
  seed = lifecycle::deprecated()
)

Arguments

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.

source

Source for the mock cdm, it can either be 'local' or 'duckdb'.

con

deprecated.

writeSchema

deprecated.

seed

deprecated.

Value

A mock cdm_reference object created following user's specifications.

Examples


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()



library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

Create a ggplot from the output of summariseCharacteristics.

Description

Usage

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

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).

style

Visual theme to apply. Character, or NULL. If a character, this may be either the name of a built-in style (see plotStyle()), or a path to a .yml file that defines a custom style. If NULL, the function will use the explicit default style, unless a global style option is set (see setGlobalPlotOptions()), or a _brand.yml file is present (in that order). Refer to the package vignette on styles to learn more.

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"
  )


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"
  )

create a ggplot from the output of summariseLargeScaleCharacteristics.

Description

Usage

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

cdm <- mockCohortCharacteristics(numberIndividuals = 1000)

cdm[["cohort1"]] <- cdm[["cohort1"]] |>
  filter(get_year(cohort_start_date) >= 2000) |>
  recordCohortAttrition("Restrict to cohort_start_date >= 2000") |>
  filter(get_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()



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

cdm <- mockCohortCharacteristics(numberIndividuals = 1000)

cdm[["cohort1"]] <- cdm[["cohort1"]] |>
  filter(get_year(cohort_start_date) >= 2000) |>
  recordCohortAttrition("Restrict to cohort_start_date >= 2000") |>
  filter(get_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()

Plot the result of summariseCohortCount.

Description

Usage

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

Arguments

result

A summarised_result object.

x

Variables to use in x axis.

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).

style

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"
  )



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"
  )

Plot the result of summariseCohortOverlap.

Description

Usage

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

Arguments

result

A summarised_result object.

uniqueCombinations

Whether to restrict to unique reference and comparator comparisons.

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).

style

.options

deprecated.

Value

A ggplot.

Examples


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

overlap <- summariseCohortOverlap(cdm$cohort2)

plotCohortOverlap(overlap, uniqueCombinations = FALSE)



library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

overlap <- summariseCohortOverlap(cdm$cohort2)

plotCohortOverlap(overlap, uniqueCombinations = FALSE)

Plot summariseCohortTiming results.

Description

Usage

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

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.

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).

style

Value

A ggplot.

Examples

## Not run: 
library(CohortCharacteristics)
library(omock)
library(DrugUtilisation)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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 = cdm)

## End(Not run)

## Not run: 
library(CohortCharacteristics)
library(omock)
library(DrugUtilisation)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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 = cdm)

## End(Not run)

create a ggplot from the output of summariseLargeScaleCharacteristics.

Description

Usage

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

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.

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.

style

Value

A ggplot.

Examples

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

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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)

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

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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)

create a ggplot from the output of summariseLargeScaleCharacteristics.

Description

Usage

plotLargeScaleCharacteristics(
  result,
  facet = c("cdm_name", "cohort_name"),
  colour = "variable_level",
  style = NULL
)
plotLargeScaleCharacteristics(
  result,
  facet = c("cdm_name", "cohort_name"),
  colour = "variable_level",
  style = NULL
)

Arguments

result

A summarised_result object.

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).

style

Value

A ggplot2 object.

Examples

## Not run: 
library(CohortCharacteristics)
library(omock)
library(DrugUtilisation)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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)

## Not run: 
library(CohortCharacteristics)
library(omock)
library(DrugUtilisation)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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)

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
)
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
)

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", for "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', ... as valid labels, see available estimates using PatientProfiles::availableEstimates()

weights

Column in cohort that points to weights of each individual.

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()


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()

Summarise attrition associated with cohorts in a cohort table

Description

Summarise attrition associated with cohorts in a cohort table

Usage

summariseCohortAttrition(cohort, cohortId = NULL)
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()


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

cdm <- mockCohortCharacteristics()

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

Summarise the cohort codelist attribute

Description

Usage

summariseCohortCodelist(cohort, cohortId = NULL)
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(dplyr, warn.conflicts = FALSE)
library(omock)
library(CDMConnector)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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

result <- summariseCohortCodelist(cdm$my_cohort)

glimpse(result)

tidy(result)


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

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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())
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()


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

cdm <- mockCohortCharacteristics()

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

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"
)
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()


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

cdm <- mockCohortCharacteristics()

summariseCohortOverlap(cdm$cohort2) |>
  glimpse()

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()
)
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()



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

cdm <- mockCohortCharacteristics(numberIndividuals = 100)

summariseCohortTiming(cdm$cohort2) |>
  glimpse()

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

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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

Usage

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

Arguments

result

A summarised_result object.

type

Character string specifying the desired output table format. See visOmopResults::tableType() for supported table types. If type = NULL, global options (set via visOmopResults::setGlobalTableOptions()) will be used if available; otherwise, a default 'gt' table is created.

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).

style

Defines the visual formatting of the table. This argument can be provided in one of the following ways:

Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.
YAML file path: Provide the path to an existing .yml file defining a new style.
List of custome R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type.

If style = NULL, the function will use global options (see visOmopResults::setGlobalTableOptions()) or an existing ⁠_brand.yml⁠ file (if found); otherwise, the default style is applied. For more details, see the Styles vignette in visOmopResults website.

.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)



library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

result <- summariseCharacteristics(cdm$cohort1)

tableCharacteristics(result)

Create a visual table from the output of summariseCohortAttrition.

Description

Usage

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

Arguments

result

A summarised_result object.

type

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).

style

Defines the visual formatting of the table. This argument can be provided in one of the following ways:

Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.
YAML file path: Provide the path to an existing .yml file defining a new style.
List of custome R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type.

.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)



library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

result <- summariseCohortAttrition(cdm$cohort2)

tableCohortAttrition(result)

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

Description

Usage

tableCohortCodelist(result, type = "reactable")
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(omock)
library(dplyr, warn.conflicts = FALSE)
library(CDMConnector)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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

result <- summariseCohortCodelist(cdm$my_cohort)

tableCohortCodelist(result)


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

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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

Usage

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

Arguments

result

A summarised_result object.

type

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).

style

Defines the visual formatting of the table. This argument can be provided in one of the following ways:

Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.
YAML file path: Provide the path to an existing .yml file defining a new style.
List of custome R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type.

.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)



library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

result <- summariseCohortCount(cdm$cohort1)

tableCohortCount(result)

Format a summariseOverlapCohort result into a visual table.

Description

Usage

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

Arguments

result

A summarised_result object.

uniqueCombinations

Whether to restrict to unique reference and comparator comparisons.

type

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).

style

Defines the visual formatting of the table. This argument can be provided in one of the following ways:

Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.
YAML file path: Provide the path to an existing .yml file defining a new style.
List of custome R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type.

.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)



library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

overlap <- summariseCohortOverlap(cdm$cohort2)

tableCohortOverlap(overlap)

Format a summariseCohortTiming result into a visual table.

Description

Usage

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

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).

style

Defines the visual formatting of the table. This argument can be provided in one of the following ways:

Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.
YAML file path: Provide the path to an existing .yml file defining a new style.
List of custome R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type.

.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(omock)
library(DrugUtilisation)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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)

## Not run: 
library(CohortCharacteristics)
library(omock)
library(DrugUtilisation)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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)

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"
)
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(omock)
library(CDMConnector)
library(dplyr, warn.conflicts = FALSE)

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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)

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

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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 = NULL,
  style = NULL
)
tableTopLargeScaleCharacteristics(
  result,
  topConcepts = 10,
  type = NULL,
  style = NULL
)

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.

style

Defines the visual formatting of the table. This argument can be provided in one of the following ways:

Pre-defined style: Use the name of a built-in style (e.g., "darwin"). See visOmopResults::tableStyle() for available options.
YAML file path: Provide the path to an existing .yml file defining a new style.
List of custome R code: Supply a block of custom R code or a named list describing styles for each table section. This code must be specific to the selected table type.

Value

A formated table.

Examples

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

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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)

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

cdm <- mockCdmFromDataset(datasetName = "GiBleed", source = "duckdb")

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)

Package 'CohortCharacteristics'

Help Index

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

Description

Usage

Arguments

Value

Examples

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

Description

Usage

Arguments

Value

Examples

Benchmark the main functions of CohortCharacteristics package.

Description

Usage

Arguments

Value

Examples

It creates a mock database for testing CohortCharacteristics package

Description

Usage

Arguments

Value

Examples

Create a ggplot from the output of summariseCharacteristics.

Description

Usage

Arguments

Value

Examples

create a ggplot from the output of summariseLargeScaleCharacteristics.

Description

Usage

Arguments

Value

Examples

Plot the result of summariseCohortCount.

Description

Usage

Arguments

Value

Examples

Plot the result of summariseCohortOverlap.

Description

Usage

Arguments

Value

Examples

Plot summariseCohortTiming results.

Description

Usage

Arguments

Value

Examples

create a ggplot from the output of summariseLargeScaleCharacteristics.

Description

Usage

Arguments

Value

Examples

create a ggplot from the output of summariseLargeScaleCharacteristics.

Description

Usage

Arguments

Value

Examples

Summarise characteristics of cohorts in a cohort table

Description

Usage

Arguments

Value

Examples

Summarise attrition associated with cohorts in a cohort table

Description

Usage

Arguments

Value

Examples

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

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

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