Help for package PatientProfiles

Type:

Package

Title:

Identify Characteristics of Patients in the OMOP Common Data Model

Version:

1.6.0

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

Suggests:

bit64, CDMConnector (≥ 1.3.1), CodelistGenerator, CohortConstructor, covr, DBI, dbplyr, DT, duckdb (≥ 0.9.0), ggplot2, glue, gt, here, Hmisc, knitr, odbc, omock (≥ 0.7.0), patchwork, rmarkdown, RPostgres, scales, spelling, testthat (≥ 3.1.5), tictoc, withr

Imports:

cli, clock, dplyr, lifecycle, omopgenerics (≥ 1.3.1), 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:

Config/testthat/parallel:

true

VignetteBuilder:

knitr

Config/roxygen2/version:

8.0.0

NeedsCompilation:

Packaged:

2026-07-25 09:31:44 UTC; martics

Author:

Martí Català

[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]

Repository:

CRAN

Date/Publication:

2026-07-25 11:30:02 UTC

PatientProfiles: 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.

Author(s)

Maintainer: Martí Català marti.catalasabate@ndorms.ox.ac.uk (ORCID)

Authors:

Martí Català marti.catalasabate@ndorms.ox.ac.uk (ORCID)
Yuchen Guo yuchen.guo@ndorms.ox.ac.uk (ORCID)
Mike Du mike.du@ndorms.ox.ac.uk (ORCID)
Kim Lopez-Guell kim.lopez@spc.ox.ac.uk (ORCID)
Edward Burn edward.burn@ndorms.ox.ac.uk (ORCID)
Nuria Mercade-Besora nuria.mercadebesora@ndorms.ox.ac.uk (ORCID)

Other contributors:

Xintong Li xintong.li@ndorms.ox.ac.uk (ORCID) [contributor]
Xihang Chen xihang.chen@ndorms.ox.ac.uk (ORCID) [contributor]

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,
  ageUnit = "years",
  missingAgeGroupValue = "None",
  name = NULL,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

ageName

Name of the age column to add.

ageGroup

If not NULL, a list of age-group vectors.

ageMissingMonth

Month of the year assigned when month of birth is missing.

ageMissingDay

Day of the month assigned when day of birth is missing.

ageImposeMonth

If TRUE, month of birth is treated as missing for all individuals.

ageImposeDay

If TRUE, day of birth is treated as missing for all individuals.

ageUnit

Unit in which to express age: "years", "months", or "days".

missingAgeGroupValue

Value to use when age is missing.

name

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

type

Type of the created column(s). Counts, days, age, and observation durations can be "numeric" or "integer". Flag columns can also be "logical". Field columns can use "auto" to preserve the source type, or can be converted to "numeric", "integer", "logical", or "character".

Value

tibble with the age column added.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addAge()

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

Description

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,
  ageUnit = "years",
  missingAgeGroupValue = "None",
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

ageName

Name of the age column to add.

ageGroup

If not NULL, a list of age-group vectors.

ageMissingMonth

Month of the year assigned when month of birth is missing.

ageMissingDay

Day of the month assigned when day of birth is missing.

ageImposeMonth

If TRUE, month of birth is treated as missing for all individuals.

ageImposeDay

If TRUE, day of birth is treated as missing for all individuals.

ageUnit

Unit in which to express age: "years", "months", or "days".

missingAgeGroupValue

Value to use when age is missing.

type

Value

tibble with the age column added.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addAgeQuery()

Add the birth day of an individual to a table

Description

The function accounts for leap years and corrects the invalid dates to the next valid date.

Usage

addBirthday(
  x,
  birthday = 0,
  birthdayName = "birthday",
  ageMissingMonth = 1L,
  ageMissingDay = 1L,
  ageImposeMonth = FALSE,
  ageImposeDay = FALSE,
  ageUnit = "years",
  name = NULL
)

Arguments

x

A table containing individuals in a CDM reference.

birthday

Day of birth to add.

birthdayName

Name of the birthday column to add.

ageMissingMonth

Month of the year assigned when month of birth is missing.

ageMissingDay

Day of the month assigned when day of birth is missing.

ageImposeMonth

If TRUE, month of birth is treated as missing for all individuals.

ageImposeDay

If TRUE, day of birth is treated as missing for all individuals.

ageUnit

Unit in which to express age: "years", "months", or "days".

name

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

Value

The table with a new column containing the birth day.

Examples


library(PatientProfiles)
library(dplyr)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addBirthday() |>
  glimpse()

cdm$cohort1 |>
  addBirthday(birthday = 5, birthdayName = "bithday_5th") |>
  glimpse()

Add the birth day of an individual to a table

Description

Same as addBirthday(), except query is not computed to a table.

The function accounts for leap years and corrects the invalid dates to the next valid date.

Usage

addBirthdayQuery(
  x,
  birthdayName = "birthday",
  birthday = 0,
  ageMissingMonth = 1,
  ageMissingDay = 1,
  ageImposeMonth = FALSE,
  ageImposeDay = FALSE,
  ageUnit = "years"
)

Arguments

x

A table containing individuals in a CDM reference.

birthdayName

Name of the birthday column to add.

birthday

Day of birth to add.

ageMissingMonth

Month of the year assigned when month of birth is missing.

ageMissingDay

Day of the month assigned when day of birth is missing.

ageImposeMonth

If TRUE, month of birth is treated as missing for all individuals.

ageImposeDay

If TRUE, day of birth is treated as missing for all individuals.

ageUnit

Unit in which to express age: "years", "months", or "days".

Value

The table with a query that add the new column containing the birth day.

Examples


library(PatientProfiles)
library(dplyr)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addBirthdayQuery() |>
  glimpse()

cdm$cohort1 |>
  addBirthdayQuery(birthday = 5) |>
  glimpse()

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

A table containing individuals in a CDM reference.

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


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

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)
    ))
  )

Add cdm name

Description

Add cdm name

Usage

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

Arguments

table

A table to process.

cdm

A cdm_reference object.

Value

Table with an extra column with the cdm names

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addCdmName()

Add the first or last cohort event and its date

Description

addCohortEventDate() finds the first or last event in each window. When no event is observed before the applicable boundary, the event is reported as "end_of_observation" if the observation period boundary is reached or "censor" if the window boundary or censorDate is reached. The date value represents that boundary.

Usage

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

Arguments

x

A table containing individuals in a CDM reference.

targetCohortTable

Name of the cohort table to intersect with.

targetCohortId

Cohort definition IDs to include from targetCohortTable. If NULL, all cohorts are included.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

targetDate

Name or names of date columns in the target tables to use for the intersection.

order

Which record to use when multiple records occur in a window: "first" or "last".

window

Window or windows of time relative to indexDate to consider.

multipleEvents

How events occurring on the same date are handled. If NULL, the first event in the original event order is returned. If TRUE, all simultaneous event names are sorted alphabetically and joined with "; ". A character vector gives priority to the specified event names; the first matching name is returned, with unspecified names following in alphabetical order. Boundary labels ("censor" and "end_of_observation") are never combined with event names.

nameStyle

Naming pattern for the added columns. It must contain {value} and can also contain {window_name}.

name

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

Value

x with an event column and a date column for every window.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

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

Add the first or last cohort event and its relative days

Description

addCohortEventDays() finds the first or last event in each window. When no event is observed before the applicable boundary, the event is reported as "end_of_observation" if the observation period boundary is reached or "censor" if the window boundary or censorDate is reached. The days value represents that boundary.

Usage

addCohortEventDays(
  x,
  targetCohortTable,
  targetCohortId = NULL,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  targetDate = "cohort_start_date",
  order = "first",
  window = c(0, Inf),
  multipleEvents = NULL,
  nameStyle = "{value}_{window_name}",
  name = NULL,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

targetCohortTable

Name of the cohort table to intersect with.

targetCohortId

Cohort definition IDs to include from targetCohortTable. If NULL, all cohorts are included.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

targetDate

Name or names of date columns in the target tables to use for the intersection.

order

Which record to use when multiple records occur in a window: "first" or "last".

window

Window or windows of time relative to indexDate to consider.

multipleEvents

nameStyle

Naming pattern for the added columns. It must contain {value} and can also contain {window_name}.

name

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

type

Value

x with an event column and a days column of the requested type, relative to indexDate, for every window.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

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

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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

targetCohortTable

Name of the cohort table to intersect with.

targetCohortId

Cohort definition IDs to include from targetCohortTable. If NULL, all cohorts are included.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

targetStartDate

Name or names of start-date columns in the target tables to use for the intersection.

targetEndDate

Name or names of end-date columns in the target tables to use for the intersection. If NULL, the target is treated as a point event.

window

Window or windows of time relative to indexDate to consider.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

type

Value

The original table (x) with one added column per intersection with the desired cohort in a specific window. One column will be created for each combination of window and cohort. The value of the column will be the number of intersections in the desired window, or NA if the individual is not in observation at any time in the window.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

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

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

A table containing individuals in a CDM reference.

targetCohortTable

Name of the cohort table to intersect with.

targetCohortId

Cohort definition IDs to include from targetCohortTable. If NULL, all cohorts are included.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

targetDate

Name or names of date columns in the target tables to use for the intersection.

order

Which record to use when multiple records occur in a window: "first" or "last".

window

Window or windows of time relative to indexDate to consider.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

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


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

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

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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

targetCohortTable

Name of the cohort table to intersect with.

targetCohortId

Cohort definition IDs to include from targetCohortTable. If NULL, all cohorts are included.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

targetDate

Name or names of date columns in the target tables to use for the intersection.

order

Which record to use when multiple records occur in a window: "first" or "last".

window

Window or windows of time relative to indexDate to consider.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

type

Value

x along with additional columns for each cohort of interest.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

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

It creates a column with the field of a desired intersection

Description

It creates a column with the field of a desired intersection

Usage

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

Arguments

x

A table containing individuals in a CDM reference.

targetCohortTable

Name of the cohort table to intersect with.

field

Name or names of columns in the target tables to add to x.

targetCohortId

Cohort definition IDs to include from targetCohortTable. If NULL, all cohorts are included.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

targetDate

Name or names of date columns in the target tables to use for the intersection.

order

Which record to use when multiple records occur in a window: "first" or "last".

window

Window or windows of time relative to indexDate to consider.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

type

Value

table with added columns with overlap information.

Examples


library(PatientProfiles)
library(dplyr)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort2 <- cdm$cohort2 |>
  mutate(even = if_else(subject_id %% 2, "yes", "no")) |>
  compute(name = "cohort2")

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

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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

targetCohortTable

Name of the cohort table to intersect with.

targetCohortId

Cohort definition IDs to include from targetCohortTable. If NULL, all cohorts are included.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

targetStartDate

Name or names of start-date columns in the target tables to use for the intersection.

targetEndDate

Name or names of end-date columns in the target tables to use for the intersection. If NULL, the target is treated as a point event.

window

Window or windows of time relative to indexDate to consider.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

type

Value

The original table (x) with one added column per intersection with the desired cohort in a specific window. One column will be created for each combination of window and cohort. The value of the column can either indicate presence (1 or TRUE), no intersection (0 or FALSE), or NA if the individual is not in observation at any time of the window. The representation depends on type.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

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

Add cohort name for each cohort_definition_id

Description

Add cohort name for each cohort_definition_id

Usage

addCohortName(cohort)

Arguments

cohort

A cohort_table object.

Value

cohort with an extra column with the cohort names

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")
cdm$cohort1 |>
  addCohortName()

Add the first or last concept event and its date

Description

addConceptEventDate() finds the first or last event from a set of concepts in each window. When no event is observed before the applicable boundary, the event is reported as "end_of_observation" if the observation period boundary is reached or "censor" if the window boundary or censorDate is reached. The date value represents that boundary.

Usage

addConceptEventDate(
  x,
  conceptSet,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  targetDate = "event_start_date",
  order = "first",
  window = list(c(0, Inf)),
  multipleEvents = NULL,
  nameStyle = "{value}_{window_name}",
  name = NULL
)

Arguments

x

A table containing individuals in a CDM reference.

conceptSet

A named list of concept sets.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

targetDate

Name or names of date columns in the target tables to use for the intersection.

order

Which record to use when multiple records occur in a window: "first" or "last".

window

Window or windows of time relative to indexDate to consider.

multipleEvents

nameStyle

Naming pattern for the added columns. It must contain {value} and can also contain {window_name}.

name

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

Value

x with an event column and a date column for every window.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addConceptEventDate(conceptSet = list(acetaminophen = 1125315L))

Add the first or last concept event and its relative days

Description

addConceptEventDays() finds the first or last event from a set of concepts in each window. When no event is observed before the applicable boundary, the event is reported as "end_of_observation" if the observation period boundary is reached or "censor" if the window boundary or censorDate is reached. The days value represents that boundary.

Usage

addConceptEventDays(
  x,
  conceptSet,
  indexDate = "cohort_start_date",
  censorDate = NULL,
  targetDate = "event_start_date",
  order = "first",
  window = list(c(0, Inf)),
  multipleEvents = NULL,
  nameStyle = "{value}_{window_name}",
  name = NULL,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

conceptSet

A named list of concept sets.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

targetDate

Name or names of date columns in the target tables to use for the intersection.

order

Which record to use when multiple records occur in a window: "first" or "last".

window

Window or windows of time relative to indexDate to consider.

multipleEvents

nameStyle

Naming pattern for the added columns. It must contain {value} and can also contain {window_name}.

name

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

type

Value

x with an event column and a days column of the requested type, relative to indexDate, for every window.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addConceptEventDays(conceptSet = list(acetaminophen = 1125315L))

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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

conceptSet

A named list of concept sets.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

window

Window or windows of time relative to indexDate to consider.

targetStartDate

Name or names of start-date columns in the target tables to use for the intersection.

targetEndDate

Name or names of end-date columns in the target tables to use for the intersection. If NULL, the target is treated as a point event.

inObservation

If TRUE, only records that occur during an observation period are considered.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

type

Value

The original table (x) with one added column per intersection with the desired conceptSet in a specific window. One column will be created for each combination of window and conceptSet. The value of the column will be the number of intersections in the desired window, or NA if the individual is not in observation at any time in the window.

Examples


library(PatientProfiles)
library(omopgenerics, warn.conflicts = TRUE)
library(dplyr, warn.conflicts = TRUE)

cdm <- mockPatientProfiles(source = "duckdb")

concept <- 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_
) |>
  mutate(concept_name = paste0("concept: ", .data$concept_id))
cdm <- insertTable(cdm, "concept", concept)

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

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

A table containing individuals in a CDM reference.

conceptSet

A named list of concept sets.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

window

Window or windows of time relative to indexDate to consider.

targetDate

Name or names of date columns in the target tables to use for the intersection.

order

Which record to use when multiple records occur in a window: "first" or "last".

inObservation

If TRUE, only records that occur during an observation period are considered.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

Value

table with added columns with overlap information

Examples


library(PatientProfiles)
library(omopgenerics, warn.conflicts = TRUE)
library(dplyr, warn.conflicts = TRUE)

cdm <- mockPatientProfiles(source = "duckdb")

concept <- 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_
) |>
  mutate(concept_name = paste0("concept: ", .data$concept_id))
cdm <- insertTable(cdm, "concept", concept)

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

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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

conceptSet

A named list of concept sets.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

window

Window or windows of time relative to indexDate to consider.

targetDate

Name or names of date columns in the target tables to use for the intersection.

order

Which record to use when multiple records occur in a window: "first" or "last".

inObservation

If TRUE, only records that occur during an observation period are considered.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

type

Value

table with added columns with overlap information

Examples


library(PatientProfiles)
library(omopgenerics, warn.conflicts = TRUE)
library(dplyr, warn.conflicts = TRUE)

cdm <- mockPatientProfiles(source = "duckdb")

concept <- 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_
) |>
  mutate(concept_name = paste0("concept: ", .data$concept_id))
cdm <- insertTable(cdm, "concept", concept)

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

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,
  type = "auto"
)

Arguments

x

A table containing individuals in a CDM reference.

conceptSet

A named list of concept sets.

field

Name or names of columns in the target tables to add to x.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

window

Window or windows of time relative to indexDate to consider.

targetDate

Name or names of date columns in the target tables to use for the intersection.

order

Which record to use when multiple records occur in a window: "first" or "last".

inObservation

If TRUE, only records that occur during an observation period are considered.

allowDuplicates

Whether to allow multiple records for the same person, target, and date. If TRUE, multiple values are collapsed into a semicolon-separated character value; otherwise, duplicates result in an error.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

type

Value

Table with the field value obtained from the intersection

Examples


library(PatientProfiles)
library(omopgenerics, warn.conflicts = TRUE)
library(dplyr, warn.conflicts = TRUE)

cdm <- mockPatientProfiles(source = "duckdb")

concept <- 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_
) |>
  mutate(concept_name = paste0("concept: ", .data$concept_id))
cdm <- insertTable(cdm, "concept", concept)

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

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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

conceptSet

A named list of concept sets.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

window

Window or windows of time relative to indexDate to consider.

targetStartDate

Name or names of start-date columns in the target tables to use for the intersection.

targetEndDate

Name or names of end-date columns in the target tables to use for the intersection. If NULL, the target is treated as a point event.

inObservation

If TRUE, only records that occur during an observation period are considered.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

type

Value

The original table (x) with one added column per intersection with the desired conceptSet in a specific window. One column will be created for each combination of window and conceptSet. The value of the column can either indicate presence (1 or TRUE), no intersection (0 or FALSE), or NA if the individual is not in observation at any time of the window. The representation depends on type.

Examples


library(PatientProfiles)
library(omopgenerics, warn.conflicts = TRUE)
library(dplyr, warn.conflicts = TRUE)

cdm <- mockPatientProfiles(source = "duckdb")

concept <- 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_
) |>
  mutate(concept_name = paste0("concept: ", .data$concept_id))
cdm <- insertTable(cdm, "concept", concept)

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

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

A table to process.

column

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

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

Value

table with an extra column with the concept names.

Examples


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

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

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

A table containing individuals in a CDM reference.

dateOfBirthName

Name of the date-of-birth column to add.

missingDay

Day of the month assigned when day of birth is missing.

missingMonth

Month of the year assigned when month of birth is missing.

imposeDay

If TRUE, day of birth is treated as missing for all individuals.

imposeMonth

If TRUE, month of birth is treated as missing for all individuals.

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(source = "duckdb")

cdm$cohort1 |>
  addDateOfBirth()

Query to add a column with the individual birth date

Description

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

A table containing individuals in a CDM reference.

dateOfBirthName

Name of the date-of-birth column to add.

missingDay

Day of the month assigned when day of birth is missing.

missingMonth

Month of the year assigned when month of birth is missing.

imposeDay

If TRUE, day of birth is treated as missing for all individuals.

imposeMonth

If TRUE, month of birth is treated as missing for all individuals.

Value

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

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addDateOfBirthQuery()

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

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

window

Window or windows of time relative to indexDate to consider.

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


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addDeathDate()

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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

window

Window or windows of time relative to indexDate to consider.

deathDaysName

name of the new column to be added.

name

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

type

Value

table x with the added column with death information added.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addDeathDays()

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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

window

Window or windows of time relative to indexDate to consider.

deathFlagName

name of the new column to be added.

name

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

type

Value

The original table (x) with one added column per window indicating whether the individual's death record intersects that window. The value of the column can either indicate presence (1 or TRUE), no intersection (0 or FALSE), or NA if the individual is not in observation at any time of the window. The representation depends on type.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addDeathFlag()

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,
  ageUnit = "years",
  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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

age

If TRUE, age is calculated relative to indexDate.

ageName

Name of the age column to add.

ageMissingMonth

Month of the year assigned when month of birth is missing.

ageMissingDay

Day of the month assigned when day of birth is missing.

ageImposeMonth

If TRUE, month of birth is treated as missing for all individuals.

ageImposeDay

If TRUE, day of birth is treated as missing for all individuals.

ageUnit

Unit in which to express age: "years", "months", or "days".

ageGroup

If not NULL, a list of age-group vectors.

missingAgeGroupValue

Value to use when age is missing.

sex

If TRUE, sex is identified.

sexName

Name of the sex column to add.

missingSexValue

Value to use when sex is missing.

priorObservation

If TRUE, the time between the start of the current observation period and indexDate is calculated.

priorObservationName

Name of the prior-observation column to add.

priorObservationType

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

futureObservation

If TRUE, the time between indexDate and the end of the current observation period is calculated.

futureObservationName

Name of the future-observation column to add.

futureObservationType

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

dateOfBirth

If TRUE, date of birth is returned.

dateOfBirthName

Name of the date-of-birth column to add.

name

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

type

Value

cohort table with the added demographic information columns.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addDemographics()

cdm$cohort1 |>
  addDemographics(indexDate = as.Date("2010-01-01"))

Query to add demographic characteristics at a certain date

Description

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,
  ageUnit = "years",
  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",
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

age

If TRUE, age is calculated relative to indexDate.

ageName

Name of the age column to add.

ageMissingMonth

Month of the year assigned when month of birth is missing.

ageMissingDay

Day of the month assigned when day of birth is missing.

ageImposeMonth

If TRUE, month of birth is treated as missing for all individuals.

ageImposeDay

If TRUE, day of birth is treated as missing for all individuals.

ageUnit

Unit in which to express age: "years", "months", or "days".

ageGroup

If not NULL, a list of age-group vectors.

missingAgeGroupValue

Value to use when age is missing.

sex

If TRUE, sex is identified.

sexName

Name of the sex column to add.

missingSexValue

Value to use when sex is missing.

priorObservation

If TRUE, the time between the start of the current observation period and indexDate is calculated.

priorObservationName

Name of the prior-observation column to add.

priorObservationType

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

futureObservation

If TRUE, the time between indexDate and the end of the current observation period is calculated.

futureObservationName

Name of the future-observation column to add.

futureObservationType

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

dateOfBirth

If TRUE, date of birth is returned.

dateOfBirthName

Name of the date-of-birth column to add.

type

Value

cohort table with the added demographic information columns.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addDemographicsQuery()

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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

futureObservationName

Name of the future-observation column to add.

futureObservationType

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

name

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

type

Value

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

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addFutureObservation()

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

Description

Same as addFutureObservation(), except query is not computed to a table.

Usage

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

Arguments

x

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

futureObservationName

Name of the future-observation column to add.

futureObservationType

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

type

Value

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

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addFutureObservationQuery()

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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

window

Window or windows of time relative to indexDate to consider.

completeInterval

If TRUE, individuals must be observed for the full requested interval.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

type

Value

Cohort table with an added column assessing observation. Values are 1/TRUE in observation and 0/FALSE otherwise, according to type.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addInObservation()

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

Description

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",
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

window

Window or windows of time relative to indexDate to consider.

completeInterval

If TRUE, individuals must be observed for the full requested interval.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

type

Value

Cohort table with an added column assessing observation. Values are 1/TRUE in observation and 0/FALSE otherwise, according to type.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addInObservationQuery()

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

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

nameObservationPeriodId

Name of the observation-period ID column to add.

name

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

Value

Table with the current observation period id added.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addObservationPeriodId()

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

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

nameObservationPeriodId

Name of the observation-period ID column to add.

Value

Table with the current observation period id added.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addObservationPeriodIdQuery()

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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

priorObservationName

Name of the prior-observation column to add.

priorObservationType

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

name

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

type

Value

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

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addPriorObservation()

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

Description

Same as addPriorObservation(), except query is not computed to a table.

Usage

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

Arguments

x

A table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

priorObservationName

Name of the prior-observation column to add.

priorObservationType

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

type

Value

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

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addPriorObservationQuery()

Compute the sex of the individuals

Description

Compute the sex of the individuals

Usage

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

Arguments

x

A table containing individuals in a CDM reference.

sexName

Name of the sex column to add.

missingSexValue

Value to use when sex is missing.

name

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

Value

table x with the added column with sex information.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addSex()

Query to add the sex of the individuals

Description

Same as addSex(), except query is not computed to a table.

Usage

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

Arguments

x

A table containing individuals in a CDM reference.

sexName

Name of the sex column to add.

missingSexValue

Value to use when sex is missing.

Value

table x with the added column with sex information.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

cdm$cohort1 |>
  addSexQuery()

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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

tableName

Names of one or more OMOP CDM tables to intersect with.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

window

Window or windows of time relative to indexDate to consider.

targetStartDate

Name or names of start-date columns in the target tables to use for the intersection.

targetEndDate

Name or names of end-date columns in the target tables to use for the intersection. If NULL, the target is treated as a point event.

inObservation

If TRUE, only records that occur during an observation period are considered.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

type

Value

The original table (x) with one added column per intersection with the desired table in a specific window. One column will be created for each combination of window and table. The value of the column will be the number of intersections in the desired window, or NA if the individual is not in observation at any time in the window.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

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

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

A table containing individuals in a CDM reference.

tableName

Names of one or more OMOP CDM tables to intersect with.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

window

Window or windows of time relative to indexDate to consider.

targetDate

Name or names of date columns in the target tables to use for the intersection.

inObservation

If TRUE, only records that occur during an observation period are considered.

order

Which record to use when multiple records occur in a window: "first" or "last".

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

Value

table with added columns with intersect information.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

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

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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

tableName

Names of one or more OMOP CDM tables to intersect with.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

window

Window or windows of time relative to indexDate to consider.

targetDate

Name or names of date columns in the target tables to use for the intersection.

inObservation

If TRUE, only records that occur during an observation period are considered.

order

Which record to use when multiple records occur in a window: "first" or "last".

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

type

Value

table with added columns with intersect information.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

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

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}_{field}_{window_name}",
  name = NULL,
  type = "auto"
)

Arguments

x

A table containing individuals in a CDM reference.

tableName

Names of one or more OMOP CDM tables to intersect with.

field

Name or names of columns in the target tables to add to x.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

window

Window or windows of time relative to indexDate to consider.

targetDate

Name or names of date columns in the target tables to use for the intersection.

inObservation

If TRUE, only records that occur during an observation period are considered.

order

Which record to use when multiple records occur in a window: "first" or "last".

allowDuplicates

Whether to allow multiple records for the same person, target, and date. If TRUE, multiple values are collapsed into a semicolon-separated character value; otherwise, duplicates result in an error.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

type

Value

table with added columns with intersect information.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

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

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,
  type = "numeric"
)

Arguments

x

A table containing individuals in a CDM reference.

tableName

Names of one or more OMOP CDM tables to intersect with.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

censorDate

Date or name of a date column in x on which to censor follow-up. If NULL, no censoring is applied.

window

Window or windows of time relative to indexDate to consider.

targetStartDate

Name or names of start-date columns in the target tables to use for the intersection.

targetEndDate

Name or names of end-date columns in the target tables to use for the intersection. If NULL, the target is treated as a point event.

inObservation

If TRUE, only records that occur during an observation period are considered.

nameStyle

Naming pattern for the added column or columns. It should include the required formatting variables. If more than one tableName is provided, it must include {table_name}.

name

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

type

Value

The original table (x) with one added column per intersection with the desired table in a specific window. One column will be created for each combination of window and table. The value of the column can either indicate presence (1 or TRUE), no intersection (0 or FALSE), or NA if the individual is not in observation at any time of the window. The representation depends on type.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

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

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.

endDateColumn(tableName)

Arguments

tableName

Name of the table.

Value

Name of the end date column in that table.

Examples


library(PatientProfiles)

endDateColumn("condition_occurrence")

Helper for consistent documentation of `field`.

Description

Helper for consistent documentation of field.

Arguments

field

Name or names of columns in the target tables to add to x.

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 table containing individuals in a CDM reference.

indexDate

Name of a date column in x, or a single date to use for all rows, used as the reference date.

Value

A cdm_table that is a subset of the original table.

Examples

## Not run: 
library(PatientProfiles)
library(omock)

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

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


## End(Not run)

Deprecated

Description

Deprecated

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

Arguments

numberIndividuals

Number of individuals to create in the cdm reference.

...

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

source

Source for the mock cdm, it can either be 'local' or 'duckdb'. By default, vocabulary tables are populated from the GiBleed mock vocabulary provided by omock. A user-provided concept table takes precedence.

con

deprecated.

writeSchema

deprecated.

seed

deprecated.

Value

A mock cdm_reference object created following user's specifications.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles()

Helper for consistent documentation of `multipleEvents`.

Description

Helper for consistent documentation of multipleEvents.

priorObservationType

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

Objects exported from other packages

Description

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

omopgenerics: settings(), suppress()

Helper for consistent documentation of `sex`.

Description

Helper for consistent documentation of sex.

Arguments

sex

If TRUE, sex is identified.

Helper for consistent documentation of `sexName`.

Description

Helper for consistent documentation of sexName.

Arguments

sexName

Name of the sex column to add.

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 = NULL,
  counts = TRUE,
  weights = NULL,
  customEstimates = list()
)

Arguments

table

A table to process.

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.

customEstimates

Named list of custom functions. Each function must accept a variable vector as its first argument and return one numeric value. If weights are supplied, they are passed as the second argument when the function provides one; otherwise the estimate is calculated without weights.

Value

A summarised_result object with the summarised data of interest.

Examples


library(PatientProfiles)

cdm <- mockPatientProfiles(source = "duckdb")

x <- cdm$cohort1 |>
  addDemographics()

# summarise all variables with default estimates
result <- summariseResult(x)
result

# get only counts of records and subjects
result <- summariseResult(x, variables = character())
result

# specify variables and estimates
result <- summariseResult(
  table = x,
  variables = c("cohort_start_date", "age"),
  estimates = c("mean", "median", "density")
)
result

# different estimates for each variable
result <- summariseResult(
  table = x,
  variables = list(c("age", "prior_observation"), "sex"),
  estimates = list(c("min", "max"), c("count", "percentage"))
)

# add a custom estimate
ess <- function(x) sum(x^2) / sum(x)
result <- summariseResult(
  table = x,
  variables = "age",
  estimates = "ess",
  customEstimates = list(ess = ess)
)

Description

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

Usage

variableTypes(table)

Arguments

table

A table to process.

Value

Tibble with the variables type and classification.

Examples


library(PatientProfiles)
library(dplyr, warn.conflicts = TRUE)

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

variableTypes(x)

Helper for consistent documentation of `window`.

Description

Helper for consistent documentation of window.

Arguments

window

Window or windows of time relative to indexDate to consider.

Helper for consistent documentation of `x`.

Description

Helper for consistent documentation of x.

Arguments

x

A table containing individuals in a CDM reference.

Package {PatientProfiles}

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

Description

Author(s)

See Also

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

Add the birth day of an individual to a table

Description

Usage

Arguments

Value

Examples

Add the birth day of an individual to a table

Description

Usage

Arguments

Value

Examples

Categorize a numeric variable

Description

Usage

Arguments

Value

Examples

Add cdm name

Description

Usage

Arguments

Value

Examples

Add the first or last cohort event and its date

Description

Usage

Arguments

Value

Examples

Add the first or last cohort event and its relative days

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 a column with the field of a desired intersection

Description

Usage

Arguments

Value

Examples

It creates columns to indicate the presence of cohorts

Description

Usage