Type: Package
Title: Identify Characteristics of Patients in the OMOP Common Data Model
Version: 1.4.2
Maintainer: Martí Català <marti.catalasabate@ndorms.ox.ac.uk>
Description: Identify the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model.
License: Apache License (≥ 2)
Encoding: UTF-8
RoxygenNote: 7.3.2
Suggests: bit64, CodelistGenerator, covr, DBI, dbplyr, DT, duckdb (≥ 0.9.0), ggplot2, glue, gt, here, Hmisc, knitr, odbc, omock, patchwork, rmarkdown, RPostgres, scales, spelling, testthat (≥ 3.1.5), tictoc, withr
Imports: CDMConnector (≥ 1.3.1), cli, dplyr, lifecycle, omopgenerics (≥ 1.0.0), purrr, rlang, stringr, tidyr
URL: https://darwin-eu.github.io/PatientProfiles/
BugReports: https://github.com/darwin-eu/PatientProfiles/issues
Language: en-US
Depends: R (≥ 4.1.0)
Config/testthat/edition: 3
Config/testthat/parallel: true
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2025-07-09 11:39:10 UTC; martics
Author: Martí Català ORCID iD [aut, cre], Yuchen Guo ORCID iD [aut], Mike Du ORCID iD [aut], Kim Lopez-Guell ORCID iD [aut], Edward Burn ORCID iD [aut], Nuria Mercade-Besora ORCID iD [aut], Xintong Li ORCID iD [ctb], Xihang Chen ORCID iD [ctb]
Repository: CRAN
Date/Publication: 2025-07-09 13:20:05 UTC

PatientProfiles: Identify Characteristics of Patients in the OMOP Common Data Model

Description

logo

Identify the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model.

Author(s)

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

Authors:

Other contributors:

See Also

Useful links:

Compute the age of the individuals at a certain date

Description

Compute the age of the individuals at a certain date

Usage

addAge(
  x,
  indexDate = "cohort_start_date",
  ageName = "age",
  ageGroup = NULL,
  ageMissingMonth = 1,
  ageMissingDay = 1,
  ageImposeMonth = FALSE,
  ageImposeDay = FALSE,
  missingAgeGroupValue = "None",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

indexDate

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

ageName

Name of the new column that contains age.

ageGroup

List of age groups to be added.

ageMissingMonth

Month of the year assigned to individuals with missing month of birth. By default: 1.

ageMissingDay

day of the month assigned to individuals with missing day of birth. By default: 1.

ageImposeMonth

Whether the month of the date of birth will be considered as missing for all the individuals.

ageImposeDay

Whether the day of the date of birth will be considered as missing for all the individuals.

missingAgeGroupValue

Value to include if missing age.

name

Name of the new table, if NULL a temporary table is returned.

Value

tibble with the age column added.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addAge()
mockDisconnect(cdm = cdm)

Query to add the age of the individuals at a certain date

Description

'r lifecycle::badge("experimental")' Same as 'addAge()', except query is not computed to a table.

Usage

addAgeQuery(
  x,
  indexDate = "cohort_start_date",
  ageName = "age",
  ageGroup = NULL,
  ageMissingMonth = 1,
  ageMissingDay = 1,
  ageImposeMonth = FALSE,
  ageImposeDay = FALSE,
  missingAgeGroupValue = "None"
)

Arguments

x

Table with individuals in the cdm.

indexDate

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

ageName

Name of the new column that contains age.

ageGroup

List of age groups to be added.

ageMissingMonth

Month of the year assigned to individuals with missing month of birth. By default: 1.

ageMissingDay

day of the month assigned to individuals with missing day of birth. By default: 1.

ageImposeMonth

Whether the month of the date of birth will be considered as missing for all the individuals.

ageImposeDay

Whether the day of the date of birth will be considered as missing for all the individuals.

missingAgeGroupValue

Value to include if missing age.

Value

tibble with the age column added.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addAgeQuery()

mockDisconnect(cdm = cdm)

Categorize a numeric variable

Description

Categorize a numeric variable

Usage

addCategories(
  x,
  variable,
  categories,
  missingCategoryValue = "None",
  overlap = FALSE,
  includeLowerBound = TRUE,
  includeUpperBound = TRUE,
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

variable

Target variable that we want to categorize.

categories

List of lists of named categories with lower and upper limit.

missingCategoryValue

Value to assign to those individuals not in any named category. If NULL or NA, missing values will not be changed.

overlap

TRUE if the categories given overlap.

includeLowerBound

Whether to include the lower bound in the group.

includeUpperBound

Whether to include the upper bound in the group.

name

Name of the new table, if NULL a temporary table is returned.

Value

The x table with the categorical variable added.

Examples


cdm <- mockPatientProfiles()

result <- cdm$cohort1 |>
  addAge() |>
  addCategories(
    variable = "age",
    categories = list("age_group" = list(
      "0 to 39" = c(0, 39), "40 to 79" = c(40, 79), "80 to 150" = c(80, 150)
    ))
  )
mockDisconnect(cdm = cdm)

Add cdm name

Description

Add cdm name

Usage

addCdmName(table, cdm = omopgenerics::cdmReference(table))

Arguments

table

Table in the cdm

cdm

A cdm reference object

Value

Table with an extra column with the cdm names

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles()
cdm$cohort1 |>
  addCdmName()

It creates columns to indicate number of occurrences of intersection with a cohort

Description

It creates columns to indicate number of occurrences of intersection with a cohort

Usage

addCohortIntersectCount(
  x,
  targetCohortTable,
  targetCohortId = NULL,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  targetStartDate = "cohort_start_date",
  targetEndDate = "cohort_end_date",
  window = list(c(0, Inf)),
  nameStyle = "{cohort_name}_{window_name}",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

targetCohortTable

name of the cohort that we want to check for overlap.

targetCohortId

vector of cohort definition ids to include.

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.

targetStartDate

date of reference in cohort table, either for start (in overlap) or on its own (for incidence).

targetEndDate

date of reference in cohort table, either for end (overlap) or NULL (if incidence).

window

window to consider events of.

nameStyle

naming of the added column or columns, should include required parameters.

name

Name of the new table, if NULL a temporary table is returned.

Value

table with added columns with overlap information.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addCohortIntersectCount(
    targetCohortTable = "cohort2"
  )

mockDisconnect(cdm = cdm)

Date of cohorts that are present in a certain window

Description

Date of cohorts that are present in a certain window

Usage

addCohortIntersectDate(
  x,
  targetCohortTable,
  targetCohortId = NULL,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  targetDate = "cohort_start_date",
  order = "first",
  window = c(0, Inf),
  nameStyle = "{cohort_name}_{window_name}",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

targetCohortTable

Cohort table to.

targetCohortId

Cohort IDs of interest from the other cohort table. If NULL, all cohorts will be used with a time variable added for each cohort of interest.

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.

targetDate

Date of interest in the other cohort table. Either cohort_start_date or cohort_end_date.

order

date to use if there are multiple records for an individual during the window of interest. Either first or last.

window

Window of time to identify records relative to the indexDate. Records outside of this time period will be ignored.

nameStyle

naming of the added column or columns, should include required parameters.

name

Name of the new table, if NULL a temporary table is returned.

Value

x along with additional columns for each cohort of interest.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addCohortIntersectDate(targetCohortTable = "cohort2")

mockDisconnect(cdm = cdm)

It creates columns to indicate the number of days between the current table and a target cohort

Description

It creates columns to indicate the number of days between the current table and a target cohort

Usage

addCohortIntersectDays(
  x,
  targetCohortTable,
  targetCohortId = NULL,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  targetDate = "cohort_start_date",
  order = "first",
  window = c(0, Inf),
  nameStyle = "{cohort_name}_{window_name}",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

targetCohortTable

Cohort table to.

targetCohortId

Cohort IDs of interest from the other cohort table. If NULL, all cohorts will be used with a days variable added for each cohort of interest.

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.

targetDate

Date of interest in the other cohort table. Either cohort_start_date or cohort_end_date.

order

date to use if there are multiple records for an individual during the window of interest. Either first or last.

window

Window of time to identify records relative to the indexDate. Records outside of this time period will be ignored.

nameStyle

naming of the added column or columns, should include required parameters.

name

Name of the new table, if NULL a temporary table is returned.

Value

x along with additional columns for each cohort of interest.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addCohortIntersectDays(targetCohortTable = "cohort2")

mockDisconnect(cdm = cdm)

It creates columns to indicate the presence of cohorts

Description

It creates columns to indicate the presence of cohorts

Usage

addCohortIntersectFlag(
  x,
  targetCohortTable,
  targetCohortId = NULL,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  targetStartDate = "cohort_start_date",
  targetEndDate = "cohort_end_date",
  window = list(c(0, Inf)),
  nameStyle = "{cohort_name}_{window_name}",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

targetCohortTable

name of the cohort that we want to check for overlap.

targetCohortId

vector of cohort definition ids to include.

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.

targetStartDate

date of reference in cohort table, either for start (in overlap) or on its own (for incidence).

targetEndDate

date of reference in cohort table, either for end (overlap) or NULL (if incidence).

window

window to consider events of.

nameStyle

naming of the added column or columns, should include required parameters.

name

Name of the new table, if NULL a temporary table is returned.

Value

table with added columns with overlap information.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addCohortIntersectFlag(
    targetCohortTable = "cohort2"
  )
mockDisconnect(cdm = cdm)

Add cohort name for each cohort_definition_id

Description

Add cohort name for each cohort_definition_id

Usage

addCohortName(cohort)

Arguments

cohort

cohort to which add the cohort name

Value

cohort with an extra column with the cohort names

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles()
cdm$cohort1 |>
  addCohortName()

It creates column to indicate the count overlap information between a table and a concept

Description

It creates column to indicate the count overlap information between a table and a concept

Usage

addConceptIntersectCount(
  x,
  conceptSet,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  window = list(c(0, Inf)),
  targetStartDate = "event_start_date",
  targetEndDate = "event_end_date",
  inObservation = TRUE,
  nameStyle = "{concept_name}_{window_name}",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

conceptSet

Concept set list.

indexDate

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

censorDate

whether to censor overlap events at a date column of x

window

window to consider events in.

targetStartDate

Event start date to use for the intersection.

targetEndDate

Event end date to use for the intersection.

inObservation

If TRUE only records inside an observation period will be considered.

nameStyle

naming of the added column or columns, should include required parameters.

name

Name of the new table, if NULL a temporary table is returned.

Value

table with added columns with overlap information

Examples


library(PatientProfiles)
cdm <- mockPatientProfiles()
concept <- dplyr::tibble(
  concept_id = c(1125315),
  domain_id = "Drug",
  vocabulary_id = NA_character_,
  concept_class_id = "Ingredient",
  standard_concept = "S",
  concept_code = NA_character_,
  valid_start_date = as.Date("1900-01-01"),
  valid_end_date = as.Date("2099-01-01"),
  invalid_reason = NA_character_
) |>
  dplyr::mutate(concept_name = paste0("concept: ", .data$concept_id))
cdm <- CDMConnector::insertTable(cdm, "concept", concept)

cdm$cohort1 |>
  addConceptIntersectCount(conceptSet = list("acetaminophen" = 1125315))

mockDisconnect(cdm = cdm)

It creates column to indicate the date overlap information between a table and a concept

Description

It creates column to indicate the date overlap information between a table and a concept

Usage

addConceptIntersectDate(
  x,
  conceptSet,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  window = list(c(0, Inf)),
  targetDate = "event_start_date",
  order = "first",
  inObservation = TRUE,
  nameStyle = "{concept_name}_{window_name}",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

conceptSet

Concept set list.

indexDate

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

censorDate

whether to censor overlap events at a date column of x

window

window to consider events in.

targetDate

Event date to use for the intersection.

order

last or first date to use for date/days calculations.

inObservation

If TRUE only records inside an observation period will be considered.

nameStyle

naming of the added column or columns, should include required parameters.

name

Name of the new table, if NULL a temporary table is returned.

Value

table with added columns with overlap information

Examples


library(PatientProfiles)
cdm <- mockPatientProfiles()
concept <- dplyr::tibble(
  concept_id = c(1125315),
  domain_id = "Drug",
  vocabulary_id = NA_character_,
  concept_class_id = "Ingredient",
  standard_concept = "S",
  concept_code = NA_character_,
  valid_start_date = as.Date("1900-01-01"),
  valid_end_date = as.Date("2099-01-01"),
  invalid_reason = NA_character_
) |>
  dplyr::mutate(concept_name = paste0("concept: ", .data$concept_id))
cdm <- CDMConnector::insertTable(cdm, "concept", concept)

cdm$cohort1 |>
  addConceptIntersectDate(conceptSet = list("acetaminophen" = 1125315))

mockDisconnect(cdm = cdm)

It creates column to indicate the days of difference from an index date to a concept

Description

It creates column to indicate the days of difference from an index date to a concept

Usage

addConceptIntersectDays(
  x,
  conceptSet,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  window = list(c(0, Inf)),
  targetDate = "event_start_date",
  order = "first",
  inObservation = TRUE,
  nameStyle = "{concept_name}_{window_name}",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

conceptSet

Concept set list.

indexDate

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

censorDate

whether to censor overlap events at a date column of x

window

window to consider events in.

targetDate

Event date to use for the intersection.

order

last or first date to use for date/days calculations.

inObservation

If TRUE only records inside an observation period will be considered.

nameStyle

naming of the added column or columns, should include required parameters.

name

Name of the new table, if NULL a temporary table is returned.

Value

table with added columns with overlap information

Examples


library(PatientProfiles)
cdm <- mockPatientProfiles()
concept <- dplyr::tibble(
  concept_id = c(1125315),
  domain_id = "Drug",
  vocabulary_id = NA_character_,
  concept_class_id = "Ingredient",
  standard_concept = "S",
  concept_code = NA_character_,
  valid_start_date = as.Date("1900-01-01"),
  valid_end_date = as.Date("2099-01-01"),
  invalid_reason = NA_character_
) |>
  dplyr::mutate(concept_name = paste0("concept: ", .data$concept_id))
cdm <- CDMConnector::insertTable(cdm, "concept", concept)

cdm$cohort1 |>
  addConceptIntersectDays(conceptSet = list("acetaminophen" = 1125315))

mockDisconnect(cdm = cdm)

It adds a custom column (field) from the intersection with a certain table subsetted by concept id. In general it is used to add the first value of a certain measurement.

Description

It adds a custom column (field) from the intersection with a certain table subsetted by concept id. In general it is used to add the first value of a certain measurement.

Usage

addConceptIntersectField(
  x,
  conceptSet,
  field,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  window = list(c(0, Inf)),
  targetDate = "event_start_date",
  order = "first",
  inObservation = TRUE,
  allowDuplicates = FALSE,
  nameStyle = "{field}_{concept_name}_{window_name}",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

conceptSet

Concept set list.

field

Column in the standard omop table that you want to add.

indexDate

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

censorDate

Whether to censor overlap events at a date column of x

window

Window to consider events in.

targetDate

Event date to use for the intersection.

order

'last' or 'first' to refer to which event consider if multiple events are present in the same window.

inObservation

If TRUE only records inside an observation period will be considered.

allowDuplicates

Whether to allow multiple records with same conceptSet, person_id and targetDate. If switched to TRUE, it can have a different and unpredictable behavior depending on the cdm_source.

nameStyle

naming of the added column or columns, should include required parameters.

name

Name of the new table, if NULL a temporary table is returned.

Value

Table with the 'field' value obtained from the intersection

Examples


library(PatientProfiles)
cdm <- mockPatientProfiles()
concept <- dplyr::tibble(
  concept_id = c(1125315),
  domain_id = "Drug",
  vocabulary_id = NA_character_,
  concept_class_id = "Ingredient",
  standard_concept = "S",
  concept_code = NA_character_,
  valid_start_date = as.Date("1900-01-01"),
  valid_end_date = as.Date("2099-01-01"),
  invalid_reason = NA_character_
) |>
  dplyr::mutate(concept_name = paste0("concept: ", .data$concept_id))
cdm <- CDMConnector::insertTable(cdm, "concept", concept)

cdm$cohort1 |>
  addConceptIntersectField(
    conceptSet = list("acetaminophen" = 1125315),
    field = "drug_type_concept_id"
  )

mockDisconnect(cdm = cdm)

It creates column to indicate the flag overlap information between a table and a concept

Description

It creates column to indicate the flag overlap information between a table and a concept

Usage

addConceptIntersectFlag(
  x,
  conceptSet,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  window = list(c(0, Inf)),
  targetStartDate = "event_start_date",
  targetEndDate = "event_end_date",
  inObservation = TRUE,
  nameStyle = "{concept_name}_{window_name}",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

conceptSet

Concept set list.

indexDate

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

censorDate

whether to censor overlap events at a date column of x

window

window to consider events in.

targetStartDate

Event start date to use for the intersection.

targetEndDate

Event end date to use for the intersection.

inObservation

If TRUE only records inside an observation period will be considered.

nameStyle

naming of the added column or columns, should include required parameters.

name

Name of the new table, if NULL a temporary table is returned.

Value

table with added columns with overlap information

Examples


library(PatientProfiles)
cdm <- mockPatientProfiles()
concept <- dplyr::tibble(
  concept_id = c(1125315),
  domain_id = "Drug",
  vocabulary_id = NA_character_,
  concept_class_id = "Ingredient",
  standard_concept = "S",
  concept_code = NA_character_,
  valid_start_date = as.Date("1900-01-01"),
  valid_end_date = as.Date("2099-01-01"),
  invalid_reason = NA_character_
) |>
  dplyr::mutate(concept_name = paste0("concept: ", .data$concept_id))
cdm <- CDMConnector::insertTable(cdm, "concept", concept)

cdm$cohort1 |>
  addConceptIntersectFlag(conceptSet = list("acetaminophen" = 1125315))

mockDisconnect(cdm = cdm)

Add concept name for each concept_id

Description

Add concept name for each concept_id

Usage

addConceptName(table, column = NULL, nameStyle = "{column}_name")

Arguments

table

cdm_table that contains column.

column

Column to add the concept names from. If NULL any column that its name ends with 'concept_id' will be used.

nameStyle

Name of the new column.

Value

table with an extra column with the concept names.

Examples


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

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

cdm$drug_exposure |>
  addConceptName(column = "drug_concept_id", nameStyle = "drug_name") |>
  glimpse()

cdm$drug_exposure |>
  addConceptName() |>
  glimpse()

Add a column with the individual birth date

Description

Add a column with the individual birth date

Usage

addDateOfBirth(
  x,
  dateOfBirthName = "date_of_birth",
  missingDay = 1,
  missingMonth = 1,
  imposeDay = FALSE,
  imposeMonth = FALSE,
  name = NULL
)

Arguments

x

Table in the cdm that contains 'person_id' or 'subject_id'.

dateOfBirthName

Name of the column to be added with the date of birth.

missingDay

Day of the individuals with no or imposed day of birth.

missingMonth

Month of the individuals with no or imposed month of birth.

imposeDay

Whether to impose day of birth.

imposeMonth

Whether to impose month of birth.

name

Name of the new table, if NULL a temporary table is returned.

Value

The function returns the table x with an extra column that contains the date of birth.

Examples


library(PatientProfiles)
cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addDateOfBirth()

mockDisconnect(cdm = cdm)

Query to add a column with the individual birth date

Description

'r lifecycle::badge("experimental")' Same as 'addDateOfBirth()', except query is not computed to a table.

Usage

addDateOfBirthQuery(
  x,
  dateOfBirthName = "date_of_birth",
  missingDay = 1,
  missingMonth = 1,
  imposeDay = FALSE,
  imposeMonth = FALSE
)

Arguments

x

Table in the cdm that contains 'person_id' or 'subject_id'.

dateOfBirthName

Name of the column to be added with the date of birth.

missingDay

Day of the individuals with no or imposed day of birth.

missingMonth

Month of the individuals with no or imposed month of birth.

imposeDay

Whether to impose day of birth.

imposeMonth

Whether to impose month of birth.

Value

The function returns the table x with an extra column that contains the date of birth.

Examples


library(PatientProfiles)
cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addDateOfBirthQuery()

mockDisconnect(cdm = cdm)

Add date of death for individuals. Only death within the same observation period than 'indexDate' will be observed.

Description

Add date of death for individuals. Only death within the same observation period than 'indexDate' will be observed.

Usage

addDeathDate(
  x,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  window = c(0, Inf),
  deathDateName = "date_of_death",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

indexDate

Variable in x that contains the window origin.

censorDate

Name of a column to stop followup.

window

window to consider events over.

deathDateName

name of the new column to be added.

name

Name of the new table, if NULL a temporary table is returned.

Value

table x with the added column with death information added.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addDeathDate()

mockDisconnect(cdm = cdm)

Add days to death for individuals. Only death within the same observation period than 'indexDate' will be observed.

Description

Add days to death for individuals. Only death within the same observation period than 'indexDate' will be observed.

Usage

addDeathDays(
  x,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  window = c(0, Inf),
  deathDaysName = "days_to_death",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

indexDate

Variable in x that contains the window origin.

censorDate

Name of a column to stop followup.

window

window to consider events over.

deathDaysName

name of the new column to be added.

name

Name of the new table, if NULL a temporary table is returned.

Value

table x with the added column with death information added.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addDeathDays()

mockDisconnect(cdm = cdm)

Add flag for death for individuals. Only death within the same observation period than 'indexDate' will be observed.

Description

Add flag for death for individuals. Only death within the same observation period than 'indexDate' will be observed.

Usage

addDeathFlag(
  x,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  window = c(0, Inf),
  deathFlagName = "death",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

indexDate

Variable in x that contains the window origin.

censorDate

Name of a column to stop followup.

window

window to consider events over.

deathFlagName

name of the new column to be added.

name

Name of the new table, if NULL a temporary table is returned.

Value

table x with the added column with death information added.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addDeathFlag()

mockDisconnect(cdm = cdm)

Compute demographic characteristics at a certain date

Description

Compute demographic characteristics at a certain date

Usage

addDemographics(
  x,
  indexDate = "cohort_start_date",
  age = TRUE,
  ageName = "age",
  ageMissingMonth = 1,
  ageMissingDay = 1,
  ageImposeMonth = FALSE,
  ageImposeDay = FALSE,
  ageGroup = NULL,
  missingAgeGroupValue = "None",
  sex = TRUE,
  sexName = "sex",
  missingSexValue = "None",
  priorObservation = TRUE,
  priorObservationName = "prior_observation",
  priorObservationType = "days",
  futureObservation = TRUE,
  futureObservationName = "future_observation",
  futureObservationType = "days",
  dateOfBirth = FALSE,
  dateOfBirthName = "date_of_birth",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

indexDate

Variable in x that contains the date to compute the demographics characteristics.

age

TRUE or FALSE. If TRUE, age will be calculated relative to indexDate.

ageName

Age variable name.

ageMissingMonth

Month of the year assigned to individuals with missing month of birth.

ageMissingDay

day of the month assigned to individuals with missing day of birth.

ageImposeMonth

TRUE or FALSE. Whether the month of the date of birth will be considered as missing for all the individuals.

ageImposeDay

TRUE or FALSE. Whether the day of the date of birth will be considered as missing for all the individuals.

ageGroup

if not NULL, a list of ageGroup vectors.

missingAgeGroupValue

Value to include if missing age.

sex

TRUE or FALSE. If TRUE, sex will be identified.

sexName

Sex variable name.

missingSexValue

Value to include if missing sex.

priorObservation

TRUE or FALSE. If TRUE, days of between the start of the current observation period and the indexDate will be calculated.

priorObservationName

Prior observation variable name.

priorObservationType

Whether to return a "date" or the number of "days".

futureObservation

TRUE or FALSE. If TRUE, days between the indexDate and the end of the current observation period will be calculated.

futureObservationName

Future observation variable name.

futureObservationType

Whether to return a "date" or the number of "days".

dateOfBirth

TRUE or FALSE, if true the date of birth will be return.

dateOfBirthName

dateOfBirth column name.

name

Name of the new table, if NULL a temporary table is returned.

Value

cohort table with the added demographic information columns.

Examples


library(PatientProfiles)
cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addDemographics()

mockDisconnect(cdm = cdm)

Query to add demographic characteristics at a certain date

Description

'r lifecycle::badge("experimental")' Same as 'addDemographics()', except query is not computed to a table.

Usage

addDemographicsQuery(
  x,
  indexDate = "cohort_start_date",
  age = TRUE,
  ageName = "age",
  ageMissingMonth = 1,
  ageMissingDay = 1,
  ageImposeMonth = FALSE,
  ageImposeDay = FALSE,
  ageGroup = NULL,
  missingAgeGroupValue = "None",
  sex = TRUE,
  sexName = "sex",
  missingSexValue = "None",
  priorObservation = TRUE,
  priorObservationName = "prior_observation",
  priorObservationType = "days",
  futureObservation = TRUE,
  futureObservationName = "future_observation",
  futureObservationType = "days",
  dateOfBirth = FALSE,
  dateOfBirthName = "date_of_birth"
)

Arguments

x

Table with individuals in the cdm.

indexDate

Variable in x that contains the date to compute the demographics characteristics.

age

TRUE or FALSE. If TRUE, age will be calculated relative to indexDate.

ageName

Age variable name.

ageMissingMonth

Month of the year assigned to individuals with missing month of birth.

ageMissingDay

day of the month assigned to individuals with missing day of birth.

ageImposeMonth

TRUE or FALSE. Whether the month of the date of birth will be considered as missing for all the individuals.

ageImposeDay

TRUE or FALSE. Whether the day of the date of birth will be considered as missing for all the individuals.

ageGroup

if not NULL, a list of ageGroup vectors.

missingAgeGroupValue

Value to include if missing age.

sex

TRUE or FALSE. If TRUE, sex will be identified.

sexName

Sex variable name.

missingSexValue

Value to include if missing sex.

priorObservation

TRUE or FALSE. If TRUE, days of between the start of the current observation period and the indexDate will be calculated.

priorObservationName

Prior observation variable name.

priorObservationType

Whether to return a "date" or the number of "days".

futureObservation

TRUE or FALSE. If TRUE, days between the indexDate and the end of the current observation period will be calculated.

futureObservationName

Future observation variable name.

futureObservationType

Whether to return a "date" or the number of "days".

dateOfBirth

TRUE or FALSE, if true the date of birth will be return.

dateOfBirthName

dateOfBirth column name.

Value

cohort table with the added demographic information columns.

Examples


library(PatientProfiles)
cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addDemographicsQuery()

mockDisconnect(cdm = cdm)

Compute the number of days till the end of the observation period at a certain date

Description

Compute the number of days till the end of the observation period at a certain date

Usage

addFutureObservation(
  x,
  indexDate = "cohort_start_date",
  futureObservationName = "future_observation",
  futureObservationType = "days",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

indexDate

Variable in x that contains the date to compute the future observation.

futureObservationName

name of the new column to be added.

futureObservationType

Whether to return a "date" or the number of "days".

name

Name of the new table, if NULL a temporary table is returned.

Value

cohort table with added column containing future observation of the individuals.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addFutureObservation()

mockDisconnect(cdm = cdm)

Query to add the number of days till the end of the observation period at a certain date

Description

'r lifecycle::badge("experimental")' Same as 'addFutureObservation()', except query is not computed to a table.

Usage

addFutureObservationQuery(
  x,
  indexDate = "cohort_start_date",
  futureObservationName = "future_observation",
  futureObservationType = "days"
)

Arguments

x

Table with individuals in the cdm.

indexDate

Variable in x that contains the date to compute the future observation.

futureObservationName

name of the new column to be added.

futureObservationType

Whether to return a "date" or the number of "days".

Value

cohort table with added column containing future observation of the individuals.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addFutureObservationQuery()

mockDisconnect(cdm = cdm)

Indicate if a certain record is within the observation period

Description

Indicate if a certain record is within the observation period

Usage

addInObservation(
  x,
  indexDate = "cohort_start_date",
  window = c(0, 0),
  completeInterval = FALSE,
  nameStyle = "in_observation",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

indexDate

Variable in x that contains the date to compute the observation flag.

window

window to consider events of.

completeInterval

If the individuals are in observation for the full window.

nameStyle

Name of the new columns to create, it must contain "window_name" if multiple windows are provided.

name

Name of the new table, if NULL a temporary table is returned.

Value

cohort table with the added binary column assessing inObservation.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addInObservation()

mockDisconnect(cdm = cdm)

Query to add a new column to indicate if a certain record is within the observation period

Description

'r lifecycle::badge("experimental")' Same as 'addInObservation()', except query is not computed to a table.

Usage

addInObservationQuery(
  x,
  indexDate = "cohort_start_date",
  window = c(0, 0),
  completeInterval = FALSE,
  nameStyle = "in_observation"
)

Arguments

x

Table with individuals in the cdm.

indexDate

Variable in x that contains the date to compute the observation flag.

window

window to consider events of.

completeInterval

If the individuals are in observation for the full window.

nameStyle

Name of the new columns to create, it must contain "window_name" if multiple windows are provided.

Value

cohort table with the added binary column assessing inObservation.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addInObservationQuery()

mockDisconnect(cdm = cdm)

Add the ordinal number of the observation period associated that a given date is in.

Description

Add the ordinal number of the observation period associated that a given date is in.

Usage

addObservationPeriodId(
  x,
  indexDate = "cohort_start_date",
  nameObservationPeriodId = "observation_period_id",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

indexDate

Variable in x that contains the date to compute the observation flag.

nameObservationPeriodId

Name of the new column.

name

Name of the new table, if NULL a temporary table is returned.

Value

Table with the current observation period id added.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addObservationPeriodId()

mockDisconnect(cdm = cdm)

Add the ordinal number of the observation period associated that a given date is in. Result is not computed, only query is added.

Description

Add the ordinal number of the observation period associated that a given date is in. Result is not computed, only query is added.

Usage

addObservationPeriodIdQuery(
  x,
  indexDate = "cohort_start_date",
  nameObservationPeriodId = "observation_period_id"
)

Arguments

x

Table with individuals in the cdm.

indexDate

Variable in x that contains the date to compute the observation flag.

nameObservationPeriodId

Name of the new column.

Value

Table with the current observation period id added.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addObservationPeriodIdQuery()

mockDisconnect(cdm = cdm)

Compute the number of days of prior observation in the current observation period at a certain date

Description

Compute the number of days of prior observation in the current observation period at a certain date

Usage

addPriorObservation(
  x,
  indexDate = "cohort_start_date",
  priorObservationName = "prior_observation",
  priorObservationType = "days",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

indexDate

Variable in x that contains the date to compute the prior observation.

priorObservationName

name of the new column to be added.

priorObservationType

Whether to return a "date" or the number of "days".

name

Name of the new table, if NULL a temporary table is returned.

Value

cohort table with added column containing prior observation of the individuals.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addPriorObservation()

mockDisconnect(cdm = cdm)

Query to add the number of days of prior observation in the current observation period at a certain date

Description

'r lifecycle::badge("experimental")' Same as 'addPriorObservation()', except query is not computed to a table.

Usage

addPriorObservationQuery(
  x,
  indexDate = "cohort_start_date",
  priorObservationName = "prior_observation",
  priorObservationType = "days"
)

Arguments

x

Table with individuals in the cdm.

indexDate

Variable in x that contains the date to compute the prior observation.

priorObservationName

name of the new column to be added.

priorObservationType

Whether to return a "date" or the number of "days".

Value

cohort table with added column containing prior observation of the individuals.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addPriorObservationQuery()

mockDisconnect(cdm = cdm)

Compute the sex of the individuals

Description

Compute the sex of the individuals

Usage

addSex(x, sexName = "sex", missingSexValue = "None", name = NULL)

Arguments

x

Table with individuals in the cdm.

sexName

name of the new column to be added.

missingSexValue

Value to include if missing sex.

name

Name of the new table, if NULL a temporary table is returned.

Value

table x with the added column with sex information.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addSex()

mockDisconnect(cdm = cdm)

Query to add the sex of the individuals

Description

'r lifecycle::badge("experimental")' Same as 'addSex()', except query is not computed to a table.

Usage

addSexQuery(x, sexName = "sex", missingSexValue = "None")

Arguments

x

Table with individuals in the cdm.

sexName

name of the new column to be added.

missingSexValue

Value to include if missing sex.

Value

table x with the added column with sex information.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addSexQuery()

mockDisconnect(cdm = cdm)

Compute number of intersect with an omop table.

Description

Compute number of intersect with an omop table.

Usage

addTableIntersectCount(
  x,
  tableName,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  window = list(c(0, Inf)),
  targetStartDate = startDateColumn(tableName),
  targetEndDate = endDateColumn(tableName),
  inObservation = TRUE,
  nameStyle = "{table_name}_{window_name}",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

tableName

Name of the table to intersect with. Options: visit_occurrence, condition_occurrence, drug_exposure, procedure_occurrence, device_exposure, measurement, observation, drug_era, condition_era, specimen, episode.

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.

window

window to consider events in.

targetStartDate

Column name with start date for comparison.

targetEndDate

Column name with end date for comparison.

inObservation

If TRUE only records inside an observation period will be considered.

nameStyle

naming of the added column or columns, should include required parameters.

name

Name of the new table, if NULL a temporary table is returned.

Value

table with added columns with intersect information.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addTableIntersectCount(tableName = "visit_occurrence")

mockDisconnect(cdm = cdm)

Compute date of intersect with an omop table.

Description

Compute date of intersect with an omop table.

Usage

addTableIntersectDate(
  x,
  tableName,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  window = list(c(0, Inf)),
  targetDate = startDateColumn(tableName),
  inObservation = TRUE,
  order = "first",
  nameStyle = "{table_name}_{window_name}",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

tableName

Name of the table to intersect with. Options: visit_occurrence, condition_occurrence, drug_exposure, procedure_occurrence, device_exposure, measurement, observation, drug_era, condition_era, specimen, episode.

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.

window

window to consider events in.

targetDate

Target date in tableName.

inObservation

If TRUE only records inside an observation period will be considered.

order

which record is considered in case of multiple records (only required for date and days options).

nameStyle

naming of the added column or columns, should include required parameters.

name

Name of the new table, if NULL a temporary table is returned.

Value

table with added columns with intersect information.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addTableIntersectDate(tableName = "visit_occurrence")

mockDisconnect(cdm = cdm)

Compute time to intersect with an omop table.

Description

Compute time to intersect with an omop table.

Usage

addTableIntersectDays(
  x,
  tableName,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  window = list(c(0, Inf)),
  targetDate = startDateColumn(tableName),
  inObservation = TRUE,
  order = "first",
  nameStyle = "{table_name}_{window_name}",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

tableName

Name of the table to intersect with. Options: visit_occurrence, condition_occurrence, drug_exposure, procedure_occurrence, device_exposure, measurement, observation, drug_era, condition_era, specimen, episode.

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.

window

window to consider events in.

targetDate

Target date in tableName.

inObservation

If TRUE only records inside an observation period will be considered.

order

which record is considered in case of multiple records (only required for date and days options).

nameStyle

naming of the added column or columns, should include required parameters.

name

Name of the new table, if NULL a temporary table is returned.

Value

table with added columns with intersect information.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addTableIntersectDays(tableName = "visit_occurrence")

mockDisconnect(cdm = cdm)

Intersecting the cohort with columns of an OMOP table of user's choice. It will add an extra column to the cohort, indicating the intersected entries with the target columns in a window of the user's choice.

Description

Intersecting the cohort with columns of an OMOP table of user's choice. It will add an extra column to the cohort, indicating the intersected entries with the target columns in a window of the user's choice.

Usage

addTableIntersectField(
  x,
  tableName,
  field,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  window = list(c(0, Inf)),
  targetDate = startDateColumn(tableName),
  inObservation = TRUE,
  order = "first",
  allowDuplicates = FALSE,
  nameStyle = "{table_name}_{extra_value}_{window_name}",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

tableName

Name of the table to intersect with. Options: visit_occurrence, condition_occurrence, drug_exposure, procedure_occurrence, device_exposure, measurement, observation, drug_era, condition_era, specimen, episode.

field

The columns from the table in tableName to intersect over. For example, if the user uses visit_occurrence in tableName then for field the possible options include visit_occurrence_id, visit_concept_id, visit_type_concept_id.

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.

window

window to consider events in when intersecting with the chosen column.

targetDate

The dates in the target columns in tableName that the user may want to restrict to.

inObservation

If TRUE only records inside an observation period will be considered.

order

which record is considered in case of multiple records (only required for date and days options).

allowDuplicates

Whether to allow multiple records with same conceptSet, person_id and targetDate. If switched to TRUE, it can have a different and unpredictable behavior depending on the cdm_source.

nameStyle

naming of the added column or columns, should include required parameters.

name

Name of the new table, if NULL a temporary table is returned.

Value

table with added columns with intersect information.

Examples


cdm <- mockPatientProfiles()
cdm$cohort1 |>
  addTableIntersectField(
    tableName = "visit_occurrence",
    field = "visit_concept_id",
    order = "last",
    window = c(-Inf, -1)
  )
mockDisconnect(cdm = cdm)

Compute a flag intersect with an omop table.

Description

Compute a flag intersect with an omop table.

Usage

addTableIntersectFlag(
  x,
  tableName,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  window = list(c(0, Inf)),
  targetStartDate = startDateColumn(tableName),
  targetEndDate = endDateColumn(tableName),
  inObservation = TRUE,
  nameStyle = "{table_name}_{window_name}",
  name = NULL
)

Arguments

x

Table with individuals in the cdm.

tableName

Name of the table to intersect with. Options: visit_occurrence, condition_occurrence, drug_exposure, procedure_occurrence, device_exposure, measurement, observation, drug_era, condition_era, specimen, episode.

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.

window

window to consider events in.

targetStartDate

Column name with start date for comparison.

targetEndDate

Column name with end date for comparison.

inObservation

If TRUE only records inside an observation period will be considered.

nameStyle

naming of the added column or columns, should include required parameters.

name

Name of the new table, if NULL a temporary table is returned.

Value

table with added columns with intersect information.

Examples


cdm <- mockPatientProfiles()

cdm$cohort1 |>
  addTableIntersectFlag(tableName = "visit_occurrence")

mockDisconnect(cdm = cdm)

Show the available estimates that can be used for the different variable_type supported.

Description

Show the available estimates that can be used for the different variable_type supported.

Usage

availableEstimates(variableType = NULL, fullQuantiles = FALSE)

Arguments

variableType

A set of variable types.

fullQuantiles

Whether to display the exact quantiles that can be computed or only the qXX to summarise all of them.

Value

A tibble with the available estimates.

Examples


library(PatientProfiles)

availableEstimates()
availableEstimates("numeric")
availableEstimates(c("numeric", "categorical"))

Benchmark intersections and demographics functions for a certain source (cdm).

Description

Benchmark intersections and demographics functions for a certain source (cdm).

Usage

benchmarkPatientProfiles(cdm, n = 50000, iterations = 1)

Arguments

cdm

A cdm_reference object.

n

Size of the synthetic cohorts used to benchmark.

iterations

Number of iterations to run the benchmark.

Value

A summarise_result object with the summary statistics.

Get the name of the end date column for a certain table in the cdm

Description

Get the name of the end date column for a certain table in the cdm

Usage

endDateColumn(tableName)

Arguments

tableName

Name of the table.

Value

Name of the end date column in that table.

Examples


library(PatientProfiles)
endDateColumn("condition_occurrence")

Filter a cohort according to cohort_definition_id column, the result is not computed into a table. only a query is added. Used usually as internal functions of other packages.

Description

Filter a cohort according to cohort_definition_id column, the result is not computed into a table. only a query is added. Used usually as internal functions of other packages.

Usage

filterCohortId(cohort, cohortId = NULL)

Arguments

cohort

A 'cohort_table' object.

cohortId

A vector with cohort ids.

Value

A 'cohort_table' object.

Filter the rows of a 'cdm_table' to the ones in observation that 'indexDate' is in observation.

Description

Filter the rows of a 'cdm_table' to the ones in observation that 'indexDate' is in observation.

Usage

filterInObservation(x, indexDate)

Arguments

x

A 'cdm_table' object.

indexDate

Name of a column of x that is a date.

Value

A 'cdm_table' that is a subset of the original table.

Examples

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

cdm$condition_occurrence |>
  filterInObservation(indexDate = "condition_start_date") |>
  dplyr::compute()

## End(Not run)

Function to disconnect from the mock

Description

Function to disconnect from the mock

Usage

mockDisconnect(cdm)

Arguments

cdm

A cdm_reference object.

It creates a mock database for testing PatientProfiles package

Description

It creates a mock database for testing PatientProfiles package

Usage

mockPatientProfiles(
  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 permisions.

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(PatientProfiles)
library(CDMConnector)

cdm <- mockPatientProfiles()

mockDisconnect(cdm = cdm)

Objects exported from other packages

Description

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

omopgenerics

settings, suppress

Get the name of the source concept_id column for a certain table in the cdm

Description

Get the name of the source concept_id column for a certain table in the cdm

Usage

sourceConceptIdColumn(tableName)

Arguments

tableName

Name of the table.

Value

Name of the source_concept_id column in that table.

Examples


library(PatientProfiles)
sourceConceptIdColumn("condition_occurrence")

Get the name of the standard concept_id column for a certain table in the cdm

Description

Get the name of the standard concept_id column for a certain table in the cdm

Usage

standardConceptIdColumn(tableName)

Arguments

tableName

Name of the table.

Value

Name of the concept_id column in that table.

Examples


library(PatientProfiles)
standardConceptIdColumn("condition_occurrence")

Get the name of the start date column for a certain table in the cdm

Description

Get the name of the start date column for a certain table in the cdm

Usage

startDateColumn(tableName)

Arguments

tableName

Name of the table.

Value

Name of the start date column in that table.

Examples


library(PatientProfiles)
startDateColumn("condition_occurrence")

Summarise variables using a set of estimate functions. The output will be a formatted summarised_result object.

Description

Summarise variables using a set of estimate functions. The output will be a formatted summarised_result object.

Usage

summariseResult(
  table,
  group = list(),
  includeOverallGroup = FALSE,
  strata = list(),
  includeOverallStrata = TRUE,
  variables = NULL,
  estimates = c("min", "q25", "median", "q75", "max", "count", "percentage"),
  counts = TRUE,
  weights = NULL
)

Arguments

table

Table with different records.

group

List of groups to be considered.

includeOverallGroup

TRUE or FALSE. If TRUE, results for an overall group will be reported when a list of groups has been specified.

strata

List of the stratifications within each group to be considered.

includeOverallStrata

TRUE or FALSE. If TRUE, results for an overall strata will be reported when a list of strata has been specified.

variables

Variables to summarise, it can be a list to point to different set of estimate names.

estimates

Estimates to obtain, it can be a list to point to different set of variables.

counts

Whether to compute number of records and number of subjects.

weights

Name of the column in the table that contains the weights to be used when measuring the estimates.

Value

A summarised_result object with the summarised data of interest.

Examples


library(PatientProfiles)
library(dplyr)

cdm <- mockPatientProfiles()
x <- cdm$cohort1 |>
  addDemographics() |>
  collect()
result <- summariseResult(x)
mockDisconnect(cdm = cdm)

Classify the variables between 5 types: "numeric", "categorical", "binary", "date", or NA.

Description

Classify the variables between 5 types: "numeric", "categorical", "binary", "date", or NA.

Usage

variableTypes(table)

Arguments

table

Tibble.

Value

Tibble with the variables type and classification.

Examples


library(PatientProfiles)
x <- dplyr::tibble(
  person_id = c(1, 2),
  start_date = as.Date(c("2020-05-02", "2021-11-19")),
  asthma = c(0, 1)
)
variableTypes(x)