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:	Marti Catala [aut, cre] , Yuchen Guo [aut] , Mike Du [ctb] , Kim Lopez-Guell [aut] , Edward Burn [aut] , Nuria Mercade-Besora [aut] , Marta Alcalde [aut]
Maintainer:	Marti Catala <[email protected]>
License:	Apache License (>= 2)
Version:	0.4.0
Built:	2024-11-27 05:25:31 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)

mockDisconnect(cdm)
}
{
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)
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)
}
{
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")
)
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)
## 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)

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
)
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)


library(CohortCharacteristics)
library(CDMConnector)

cdm <- mockCohortCharacteristics()

mockDisconnect(cdm = cdm)

Create a ggplot from the output of summariseCharacteristics.

Description

Usage

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

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

Usage

plotCohortAttrition(
  result,
  show = c("subjects", "records"),
  cohortId = lifecycle::deprecated()
)
plotCohortAttrition(
  result,
  show = c("subjects", "records"),
  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.
`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)


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

Usage

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


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

Usage

plotCohortOverlap(
  result,
  uniqueCombinations = TRUE,
  y = NULL,
  facet = c("cdm_name", "cohort_name_reference"),
  colour = "variable_name",
  .options = lifecycle::deprecated()
)
plotCohortOverlap(
  result,
  uniqueCombinations = TRUE,
  y = NULL,
  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.
`y`	Variables to use in y axis, if NULL all variables not present in facet are used.
`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)


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

overlap <- summariseCohortOverlap(cdm$cohort2)

plotCohortOverlap(overlap, uniqueCombinations = FALSE)

mockDisconnect(cdm)

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")
)
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)

## 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

Usage

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

Arguments

`result`	A summarised_result object.
`reference`	A named character to set up the reference.
`missings`	Value to replace the missing value with. If NULL missing values will be eliminated.
`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)
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(
    reference = c(variable_level = "-365 to -1"),
    colour = "variable_name",
    missings = NULL
  ) |>
  ggplotly()

cdmDisconnect(cdm)

## End(Not run)

## 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(
    reference = c(variable_level = "-365 to -1"),
    colour = "variable_name",
    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"
)
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)

## 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)

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(),
  otherVariablesEstimates = lifecycle::deprecated()
)
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(),
  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', 'binary' or 'categorical' keywords.
`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)

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)
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)

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

cdm <- mockCohortCharacteristics()

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

mockDisconnect(cdm)

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()

mockDisconnect(cdm)

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"
)
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)

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()
)
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)


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)
)
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)
## 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

Usage

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

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)`.

Value

A formatted table.

Examples


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

result <- summariseCharacteristics(cdm$cohort1)

tableCharacteristics(result)

mockDisconnect(cdm)


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

result <- summariseCharacteristics(cdm$cohort1)

tableCharacteristics(result)

mockDisconnect(cdm)

Create a visual table from the output of summariseCohortAttrition.

Description

Usage

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

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)`.

Value

A formatted table.

Examples


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

result <- summariseCohortAttrition(cdm$cohort2)

tableCohortAttrition(result)

mockDisconnect(cdm)


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

result <- summariseCohortAttrition(cdm$cohort2)

tableCohortAttrition(result)

mockDisconnect(cdm)

Format a summarise_characteristics object into a visual table.

Description

Usage

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

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)`.

Value

A formatted table.

Examples


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

result <- summariseCohortCount(cdm$cohort1)

tableCohortCount(result)

mockDisconnect(cdm = cdm)


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

result <- summariseCohortCount(cdm$cohort1)

tableCohortCount(result)

mockDisconnect(cdm = cdm)

Format a summariseOverlapCohort result into a visual table.

Description

Usage

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

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)`.

Value

A formatted table.

Examples


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

overlap <- summariseCohortOverlap(cdm$cohort2)

tableCohortOverlap(overlap)

mockDisconnect(cdm = cdm)


library(CohortCharacteristics)

cdm <- mockCohortCharacteristics()

overlap <- summariseCohortOverlap(cdm$cohort2)

tableCohortOverlap(overlap)

mockDisconnect(cdm = cdm)

Format a summariseCohortTiming result into a visual table.

Description

Usage

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

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)`.

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)

## 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)

Format a summarise_large_scale_characteristics object into a visual table.

Description

Usage

tableLargeScaleCharacteristics(
  result,
  topConcepts = NULL,
  type = "gt",
  header = c("cdm_name", "cohort_name", strataColumns(result), "variable_level"),
  groupColumn = c("table_name", "type", "analysis"),
  hide = character()
)
tableLargeScaleCharacteristics(
  result,
  topConcepts = NULL,
  type = "gt",
  header = c("cdm_name", "cohort_name", strataColumns(result), "variable_level"),
  groupColumn = c("table_name", "type", "analysis"),
  hide = character()
)

Arguments

`result`	A summarised_result object.
`topConcepts`	Number of concepts to restrict the table.
`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)`.

Value

A formatted table.

Examples

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

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,
  eventInWindow = "condition_occurrence",
  episodeInWindow = "drug_exposure"
)

tableLargeScaleCharacteristics(result)

cdmDisconnect(cdm)

## End(Not run)

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

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,
  eventInWindow = "condition_occurrence",
  episodeInWindow = "drug_exposure"
)

tableLargeScaleCharacteristics(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.