Package 'PatientProfiles' reference manual

Title:	Identify Characteristics of Patients in the OMOP Common Data Model
Description:	Identify the characteristics of patients in data mapped to the Observational Medical Outcomes Partnership (OMOP) common data model.
Authors:	Marti Catala [aut, cre] , Yuchen Guo [aut] , Mike Du [aut] , Kim Lopez-Guell [aut] , Edward Burn [aut] , Nuria Mercade-Besora [aut] , Xintong Li [ctb] , Xihang Chen [ctb]
Maintainer:	Marti Catala <[email protected]>
License:	Apache License (>= 2)
Version:	1.2.1
Built:	2024-10-26 03:34:23 UTC
Source:	CRAN

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
)
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)

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"
)
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)

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,
  name = NULL
)
addCategories(
  x,
  variable,
  categories,
  missingCategoryValue = "None",
  overlap = FALSE,
  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 will values will be given.
`overlap`	TRUE if the categories given overlap.
`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)

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))
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()


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
)
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)


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
)
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)


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
)
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)


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
)
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)


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)
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()


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
)
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$concept_id))
cdm <- CDMConnector::insertTable(cdm, "concept", concept)
result <- cdm$cohort1 %>%
  addConceptIntersectCount(
    conceptSet = list("acetaminophen" = 1125315)
  ) %>%
  dplyr::collect()
mockDisconnect(cdm = cdm)


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$concept_id))
cdm <- CDMConnector::insertTable(cdm, "concept", concept)
result <- cdm$cohort1 %>%
  addConceptIntersectCount(
    conceptSet = list("acetaminophen" = 1125315)
  ) %>%
  dplyr::collect()
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
)
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$concept_id))
cdm <- CDMConnector::insertTable(cdm, "concept", concept)
result <- cdm$cohort1 %>%
  addConceptIntersectDate(
    conceptSet = list("acetaminophen" = 1125315)
  ) %>%
  dplyr::collect()
mockDisconnect(cdm = cdm)


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$concept_id))
cdm <- CDMConnector::insertTable(cdm, "concept", concept)
result <- cdm$cohort1 %>%
  addConceptIntersectDate(
    conceptSet = list("acetaminophen" = 1125315)
  ) %>%
  dplyr::collect()
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
)
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$concept_id))
cdm <- CDMConnector::insertTable(cdm, "concept", concept)
result <- cdm$cohort1 %>%
  addConceptIntersectDays(
    conceptSet = list("acetaminophen" = 1125315)
  ) %>%
  dplyr::collect()
mockDisconnect(cdm = cdm)


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$concept_id))
cdm <- CDMConnector::insertTable(cdm, "concept", concept)
result <- cdm$cohort1 %>%
  addConceptIntersectDays(
    conceptSet = list("acetaminophen" = 1125315)
  ) %>%
  dplyr::collect()
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
)
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$concept_id))
cdm <- CDMConnector::insertTable(cdm, "concept", concept)
result <- cdm$cohort1 %>%
  addConceptIntersectFlag(
    conceptSet = list("acetaminophen" = 1125315)
  ) %>%
  dplyr::collect()
mockDisconnect(cdm = cdm)


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$concept_id))
cdm <- CDMConnector::insertTable(cdm, "concept", concept)
result <- cdm$cohort1 %>%
  addConceptIntersectFlag(
    conceptSet = list("acetaminophen" = 1125315)
  ) %>%
  dplyr::collect()
mockDisconnect(cdm = cdm)

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
)
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)

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
)
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)

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
)
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)


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
)
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)


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
)
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)


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
)
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)


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"
)
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)


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
)
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)

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"
)
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)

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
)
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)


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"
)
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)


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
)
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 colum.
`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)


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"
)
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 colum.

Value

Table with the current observation period id added.

Examples


cdm <- mockPatientProfiles()
cdm$cohort1 %>%
  addObservationPeriodIdQuery()
mockDisconnect(cdm = cdm)


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
)
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)

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"
)
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)

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)
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)


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")
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)


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


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


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


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),
  order = "first",
  nameStyle = "{table_name}_{extra_value}_{window_name}",
  name = NULL
)
addTableIntersectField(
  x,
  tableName,
  field,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  window = list(c(0, Inf)),
  targetDate = startDateColumn(tableName),
  order = "first",
  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.
`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 %>%
  addTableIntersectField(
    tableName = "visit_occurrence",
    field = "visit_concept_id",
    order = "last",
    window = c(-Inf, -1)
  )
mockDisconnect(cdm = cdm)


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


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)
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"))


library(PatientProfiles)

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

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)
endDateColumn(tableName)

Arguments

tableName

Name of the table.

Value

Name of the end date column in that table.

Examples


library(PatientProfiles)
endDateColumn("condition_occurrence")


library(PatientProfiles)
endDateColumn("condition_occurrence")

Function to disconnect from the mock

Description

Function to disconnect from the mock

Usage

mockDisconnect(cdm)
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
)
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)


library(PatientProfiles)
library(CDMConnector)

cdm <- mockPatientProfiles()

mockDisconnect(cdm = cdm)

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)
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")


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)
standardConceptIdColumn(tableName)

Arguments

tableName

Name of the table.

Value

Name of the concept_id column in that table.

Examples


library(PatientProfiles)
standardConceptIdColumn("condition_occurrence")


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)
startDateColumn(tableName)

Arguments

tableName

Name of the table.

Value

Name of the start date column in that table.

Examples


library(PatientProfiles)
startDateColumn("condition_occurrence")


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

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.

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)


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)
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)


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)

Package 'PatientProfiles'

Help Index

Compute the age of the individuals at a certain date

Description

Usage

Arguments

Value

Examples

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

Description

Usage

Arguments

Value

Examples

Categorize a numeric variable

Description

Usage

Arguments

Value

Examples

Add cdm name

Description

Usage

Arguments

Value

Examples

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

Description

Usage

Arguments

Value

Examples

Date of cohorts that are present in a certain window

Description

Usage

Arguments

Value

Examples

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

Description

Usage

Arguments

Value

Examples

It creates columns to indicate the presence of cohorts

Description

Usage

Arguments

Value

Examples

Add cohort name for each cohort_definition_id

Description

Usage

Arguments

Value

Examples

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

Description

Usage

Arguments

Value

Examples

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

Description

Usage

Arguments

Value

Examples

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

Description

Usage

Arguments

Value

Examples

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

Description

Usage

Arguments

Value

Examples