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PatientProfiles

Package overview

PatientProfiles contains functions for adding characteristics to OMOP CDM tables containing patient level data (e.g. condition occurrence, drug exposure, and so on) and OMOP CDM cohort tables. The characteristics that can be added include an individual´s sex, age, and days of prior observation Time varying characteristics, such as age, can be estimated relative to any date in the corresponding table. In addition, PatientProfiles also provides functionality for identifying intersections between a cohort table and OMOP CDM tables containing patient level data or other cohort tables.

Package installation

You can install the latest version of PatientProfiles like so:

install.packages("PatientProfiles")

Citation

citation("PatientProfiles")
#> To cite package 'PatientProfiles' in publications use:
#> 
#>   Catala M, Guo Y, Du M, Lopez-Guell K, Burn E, Mercade-Besora N
#>   (????). _PatientProfiles: Identify Characteristics of Patients in the
#>   OMOP Common Data Model_. R package version 1.2.3,
#>   <https://darwin-eu.github.io/PatientProfiles/>.
#> 
#> A BibTeX entry for LaTeX users is
#> 
#>   @Manual{,
#>     title = {PatientProfiles: Identify Characteristics of Patients in the OMOP Common Data Model},
#>     author = {Marti Catala and Yuchen Guo and Mike Du and Kim Lopez-Guell and Edward Burn and Nuria Mercade-Besora},
#>     note = {R package version 1.2.3},
#>     url = {https://darwin-eu.github.io/PatientProfiles/},
#>   }

Example usage

Create a reference to data in the OMOP CDM format

The PatientProfiles package is designed to work with data in the OMOP CDM format, so our first step is to create a reference to the data using the CDMConnector package.

library(CDMConnector)
library(PatientProfiles)
library(dplyr)

Creating a connection to a Postgres database would for example look like:

con <- DBI::dbConnect(
  RPostgres::Postgres(),
  dbname = Sys.getenv("CDM5_POSTGRESQL_DBNAME"),
  host = Sys.getenv("CDM5_POSTGRESQL_HOST"),
  user = Sys.getenv("CDM5_POSTGRESQL_USER"),
  password = Sys.getenv("CDM5_POSTGRESQL_PASSWORD")
)

cdm <- cdmFromCon(
  con,
  cdmSchema = Sys.getenv("CDM5_POSTGRESQL_CDM_SCHEMA"),
  writeSchema = Sys.getenv("CDM5_POSTGRESQL_RESULT_SCHEMA")
)

To see how you would create a reference to your database please consult the CDMConnector package documentation. For this example though we’ll work with simulated data, and we’ll generate an example cdm reference like so:

cdm <- mockPatientProfiles(numberIndividuals = 1000)

Adding individuals´ characteristics

Adding characteristics to patient-level data

Say we wanted to get individuals´sex and age at condition start date for records in the condition occurrence table. We can use the addAge and addSex functions to do this:

cdm$condition_occurrence |>
  glimpse()
#> Rows: ??
#> Columns: 6
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ person_id                 <int> 589, 319, 166, 603, 886, 239, 509, 516, 79, …
#> $ condition_start_date      <date> 1963-06-06, 1981-02-01, 1985-08-30, 1982-07…
#> $ condition_end_date        <date> 1963-12-19, 1981-09-11, 1998-09-06, 1984-11…
#> $ condition_occurrence_id   <int> 1, 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 1…
#> $ condition_concept_id      <int> 9, 10, 6, 10, 4, 7, 2, 5, 10, 5, 4, 6, 4, 8,…
#> $ condition_type_concept_id <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…

cdm$condition_occurrence <- cdm$condition_occurrence |>
  addAge(indexDate = "condition_start_date") |>
  addSex()

cdm$condition_occurrence |>
  glimpse()
#> Rows: ??
#> Columns: 8
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ person_id                 <int> 589, 166, 603, 886, 516, 79, 426, 352, 798, …
#> $ condition_start_date      <date> 1963-06-06, 1985-08-30, 1982-07-06, 1929-06…
#> $ condition_end_date        <date> 1963-12-19, 1998-09-06, 1984-11-06, 1935-01…
#> $ condition_occurrence_id   <int> 1, 3, 4, 5, 8, 9, 12, 14, 15, 17, 19, 20, 21…
#> $ condition_concept_id      <int> 9, 6, 10, 4, 5, 10, 6, 8, 2, 3, 4, 9, 9, 8, …
#> $ condition_type_concept_id <int> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ age                       <int> 14, 8, 8, 22, 24, 17, 5, 15, 10, 50, 14, 16,…
#> $ sex                       <chr> "Female", "Female", "Female", "Male", "Femal…

We could, for example, then limit our data to only males aged between 18 and 65

cdm$condition_occurrence |>
  filter(age >= 18 & age <= 65) |>
  filter(sex == "Male")
#> # Source:   SQL [?? x 8]
#> # Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#>    person_id condition_start_date condition_end_date condition_occurrence_id
#>        <int> <date>               <date>                               <int>
#>  1       886 1929-06-26           1935-01-20                               5
#>  2        76 2028-07-04           2029-12-03                              17
#>  3       515 1954-03-05           1959-05-13                              31
#>  4       530 1970-10-26           1978-07-20                              34
#>  5       669 1951-08-25           1958-06-03                              46
#>  6       358 1948-06-10           1955-05-15                              53
#>  7       478 1995-08-05           1995-12-11                              73
#>  8        50 1987-09-10           1994-09-17                              79
#>  9       987 1961-09-30           1963-08-07                              84
#> 10       592 1945-11-05           1947-05-11                              96
#> # ℹ more rows
#> # ℹ 4 more variables: condition_concept_id <int>,
#> #   condition_type_concept_id <int>, age <int>, sex <chr>

Adding characteristics of a cohort

As with other tables in the OMOP CDM, we can work in a similar way with cohort tables. For example, say we have the below cohort table

cdm$cohort1 |>
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ cohort_definition_id <int> 1, 1, 3, 3, 2, 1, 3, 3, 1, 2, 2, 1, 2, 2, 1, 1, 2…
#> $ subject_id           <int> 179, 961, 340, 386, 462, 250, 94, 142, 266, 753, …
#> $ cohort_start_date    <date> 1934-07-20, 1911-01-27, 1948-02-15, 1960-07-29, …
#> $ cohort_end_date      <date> 1936-07-30, 1936-06-27, 1950-01-18, 1963-11-05, …

We can add age, age groups, sex, and days of prior observation to a cohort like so

cdm$cohort1 <- cdm$cohort1 |>
  addAge(
    indexDate = "cohort_start_date",
    ageGroup = list(c(0, 18), c(19, 65), c(66, 100))
  ) |>
  addSex() |>
  addPriorObservation()

cdm$cohort1 |>
  glimpse()
#> Rows: ??
#> Columns: 8
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ cohort_definition_id <int> 2, 3, 2, 2, 2, 3, 3, 3, 1, 2, 3, 2, 1, 3, 1, 2, 2…
#> $ subject_id           <int> 1, 2, 4, 5, 7, 8, 9, 11, 12, 13, 14, 16, 17, 18, …
#> $ cohort_start_date    <date> 1951-06-11, 1943-05-05, 1910-06-06, 1942-04-27, …
#> $ cohort_end_date      <date> 1951-10-18, 1944-04-30, 1919-09-18, 1968-09-14, …
#> $ age                  <int> 8, 7, 5, 10, 30, 0, 0, 4, 27, 12, 3, 5, 22, 7, 4,…
#> $ age_group            <chr> "0 to 18", "0 to 18", "0 to 18", "0 to 18", "19 t…
#> $ sex                  <chr> "Male", "Male", "Male", "Female", "Female", "Male…
#> $ prior_observation    <int> 3083, 2681, 1982, 3769, 11317, 182, 7, 1555, 1011…

We could use this information to subset the cohort. For example limiting to those with at least 365 days of prior observation available before their cohort start date like so

cdm$cohort1 |>
  filter(prior_observation >= 365)
#> # Source:   SQL [?? x 8]
#> # Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#>    cohort_definition_id subject_id cohort_start_date cohort_end_date   age
#>                   <int>      <int> <date>            <date>          <int>
#>  1                    2          1 1951-06-11        1951-10-18          8
#>  2                    3          2 1943-05-05        1944-04-30          7
#>  3                    2          4 1910-06-06        1919-09-18          5
#>  4                    2          5 1942-04-27        1968-09-14         10
#>  5                    2          7 1954-12-26        1957-05-08         30
#>  6                    3         11 1943-04-05        1966-10-09          4
#>  7                    1         12 1957-09-06        1958-04-04         27
#>  8                    2         13 1930-11-09        1937-07-20         12
#>  9                    3         14 1912-06-18        1942-03-20          3
#> 10                    2         16 1977-09-15        1978-04-13          5
#> # ℹ more rows
#> # ℹ 3 more variables: age_group <chr>, sex <chr>, prior_observation <int>

Cohort intersections

Detect the presence of another cohort in a certain window

We can use addCohortIntersectFlag to add a flag for the presence (or not) of a cohort in a certain window.

cdm$cohort1 |>
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ cohort_definition_id <int> 1, 2, 1, 1, 2, 3, 1, 3, 1, 2
#> $ subject_id           <int> 10, 8, 3, 7, 5, 1, 9, 2, 6, 4
#> $ cohort_start_date    <date> 1985-11-23, 1944-09-11, 1936-05-07, 1928-06-24, 1…
#> $ cohort_end_date      <date> 1992-07-12, 1959-01-19, 1949-07-18, 1935-07-14, 1…

cdm$cohort1 <- cdm$cohort1 |>
  addCohortIntersectFlag(
    targetCohortTable = "cohort2",
    window = c(-Inf, -1)
  )

cdm$cohort1 |>
  glimpse()
#> Rows: ??
#> Columns: 7
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ cohort_definition_id <int> 2, 3, 1, 1, 2, 1, 2, 1, 1, 3
#> $ subject_id           <int> 5, 1, 9, 6, 4, 10, 8, 3, 7, 2
#> $ cohort_start_date    <date> 1974-06-17, 1999-09-16, 1990-07-10, 1990-04-28, 2…
#> $ cohort_end_date      <date> 1977-01-11, 2013-08-01, 1995-05-04, 2001-08-14, 2…
#> $ cohort_3_minf_to_m1  <dbl> 0, 1, 0, 1, 0, 0, 0, 0, 0, 0
#> $ cohort_1_minf_to_m1  <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0
#> $ cohort_2_minf_to_m1  <dbl> 1, 0, 0, 0, 1, 0, 0, 0, 0, 0

Count appearances of a certain cohort in a certain window

If we wanted the number of appearances, we could instead use the addCohortIntersectCount function

cdm$cohort1 |>
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ cohort_definition_id <int> 3, 2, 3, 2, 2, 2, 1, 2, 1, 3
#> $ subject_id           <int> 1, 6, 9, 5, 7, 2, 8, 4, 3, 10
#> $ cohort_start_date    <date> 1967-07-08, 1975-11-03, 1940-01-29, 1929-05-03, 1…
#> $ cohort_end_date      <date> 1990-06-21, 1977-03-03, 1957-10-05, 1929-12-21, 1…

cdm$cohort1 <- cdm$cohort1 |>
  addCohortIntersectCount(
    targetCohortTable = "cohort2",
    targetCohortId = 1,
    window = list("short_term" = c(1, 30), "mid_term" = c(31, 180))
  )

cdm$cohort1 |>
  glimpse()
#> Rows: ??
#> Columns: 6
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ cohort_definition_id <int> 2, 3, 2, 3, 2, 2, 1, 2, 1, 3
#> $ subject_id           <int> 2, 1, 6, 9, 5, 7, 8, 4, 3, 10
#> $ cohort_start_date    <date> 1949-08-26, 1967-07-08, 1975-11-03, 1940-01-29, 1…
#> $ cohort_end_date      <date> 1951-07-06, 1990-06-21, 1977-03-03, 1957-10-05, 1…
#> $ cohort_1_short_term  <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
#> $ cohort_1_mid_term    <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0

Add a column with the first/last event in a certain window

Say we wanted the date at which an individual was in another cohort then we can use the addCohortIntersectDate function. As there might be multiple records for the other cohort, we can also choose the first or the last appearance in that cohort.

First occurrence:

cdm$cohort1 |>
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ cohort_definition_id <int> 1, 1, 2, 3, 2, 3, 2, 2, 1, 1
#> $ subject_id           <int> 6, 5, 1, 2, 9, 8, 7, 10, 4, 3
#> $ cohort_start_date    <date> 1924-03-08, 1926-02-10, 1965-08-30, 1914-02-07, 1…
#> $ cohort_end_date      <date> 1924-08-05, 1931-04-12, 1980-09-12, 1915-02-13, 1…

cdm$cohort1 <- cdm$cohort1 |>
  addCohortIntersectDate(
    targetCohortTable = "cohort2",
    targetCohortId = 1,
    order = "first",
    window = c(-Inf, Inf)
  )

cdm$cohort1 |>
  glimpse()
#> Rows: ??
#> Columns: 5
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ cohort_definition_id <int> 1, 3, 1, 2, 3, 2, 2, 2, 1, 1
#> $ subject_id           <int> 6, 8, 5, 1, 2, 9, 7, 10, 4, 3
#> $ cohort_start_date    <date> 1924-03-08, 1948-08-27, 1926-02-10, 1965-08-30, 1…
#> $ cohort_end_date      <date> 1924-08-05, 1950-04-06, 1931-04-12, 1980-09-12, 1…
#> $ cohort_1_minf_to_inf <date> 1911-03-03, 1934-09-06, NA, NA, NA, NA, NA, NA, …

Last occurrence:

cdm$cohort1 |>
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ cohort_definition_id <int> 3, 3, 3, 1, 1, 2, 1, 3, 3, 3
#> $ subject_id           <int> 2, 10, 9, 6, 4, 1, 3, 7, 5, 8
#> $ cohort_start_date    <date> 1966-06-09, 1983-01-25, 1981-07-03, 1977-03-24, 1…
#> $ cohort_end_date      <date> 1968-01-28, 1989-09-13, 1981-08-11, 1994-05-30, 2…

cdm$cohort1 <- cdm$cohort1 |>
  addCohortIntersectDate(
    targetCohortTable = "cohort2",
    targetCohortId = 1,
    order = "last",
    window = c(-Inf, Inf)
  )

cdm$cohort1 |>
  glimpse()
#> Rows: ??
#> Columns: 5
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ cohort_definition_id <int> 1, 2, 3, 3, 3, 3, 1, 1, 3, 3
#> $ subject_id           <int> 6, 1, 8, 2, 10, 9, 4, 3, 7, 5
#> $ cohort_start_date    <date> 1977-03-24, 1987-07-30, 1988-12-08, 1966-06-09, 1…
#> $ cohort_end_date      <date> 1994-05-30, 1989-12-17, 1989-03-20, 1968-01-28, 1…
#> $ cohort_1_minf_to_inf <date> 1989-01-11, 1982-10-15, 1984-10-15, NA, NA, NA, …

Add the number of days instead of the date

Instead of returning a date, we could return the days to the intersection by using addCohortIntersectDays

cdm$cohort1 |>
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ cohort_definition_id <int> 3, 2, 1, 2, 3, 2, 1, 1, 2, 1
#> $ subject_id           <int> 10, 4, 9, 7, 2, 1, 3, 8, 6, 5
#> $ cohort_start_date    <date> 1940-08-16, 1921-10-07, 1962-04-14, 1927-03-26, 1…
#> $ cohort_end_date      <date> 1941-11-27, 1936-03-13, 1965-03-23, 1937-08-27, 1…

cdm$cohort1 <- cdm$cohort1 |>
  addCohortIntersectDays(
    targetCohortTable = "cohort2",
    targetCohortId = 1,
    order = "last",
    window = c(-Inf, Inf)
  )

cdm$cohort1 |>
  glimpse()
#> Rows: ??
#> Columns: 5
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ cohort_definition_id <int> 2, 2, 3, 1, 3, 1, 2, 1, 1, 2
#> $ subject_id           <int> 4, 7, 2, 5, 10, 9, 1, 3, 8, 6
#> $ cohort_start_date    <date> 1921-10-07, 1927-03-26, 1934-06-18, 1971-06-21, 1…
#> $ cohort_end_date      <date> 1936-03-13, 1937-08-27, 1936-02-16, 1981-04-02, 1…
#> $ cohort_1_minf_to_inf <dbl> 2323, 7448, 1496, 827, NA, NA, NA, NA, NA, NA

Combine multiple cohort intersects

If we want to combine multiple cohort intersects we can concatenate the operations using the pipe operator:

cdm$cohort1 |>
  glimpse()
#> Rows: ??
#> Columns: 4
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 2, 3, 2
#> $ subject_id           <int> 3, 7, 1, 8, 6, 10, 2, 9, 5, 4
#> $ cohort_start_date    <date> 1961-02-12, 1961-06-21, 1931-11-05, 1960-11-22, 1…
#> $ cohort_end_date      <date> 1972-11-08, 1977-01-04, 1932-11-15, 1964-07-01, 1…

cdm$cohort1 <- cdm$cohort1 |>
  addCohortIntersectDate(
    targetCohortTable = "cohort2",
    targetCohortId = 1,
    order = "last",
    window = c(-Inf, Inf)
  ) |>
  addCohortIntersectCount(
    targetCohortTable = "cohort2",
    targetCohortId = 1,
    window = c(-Inf, Inf)
  )

cdm$cohort1 |>
  glimpse()
#> Rows: ??
#> Columns: 5
#> Database: DuckDB v1.1.0 [root@Darwin 24.3.0:R 4.4.1/:memory:]
#> $ cohort_definition_id <int> 3, 1, 1, 1, 1, 1, 1, 1, 2, 2
#> $ subject_id           <int> 5, 3, 7, 1, 8, 6, 10, 2, 9, 4
#> $ cohort_start_date    <date> 1951-03-28, 1961-02-12, 1961-06-21, 1931-11-05, 1…
#> $ cohort_end_date      <date> 1957-06-26, 1972-11-08, 1977-01-04, 1932-11-15, 1…
#> $ cohort_1_minf_to_inf <dbl> 1, 0, 0, 0, 0, 0, 0, 0, 0, 0
mockDisconnect(cdm)

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Version

Install

install.packages('PatientProfiles')

Monthly Downloads

2,410

Version

1.3.1

License

Apache License (>= 2)

Maintainer

Marti Catala

Last Published

March 5th, 2025

Functions in PatientProfiles (1.3.1)

addDateOfBirthQuery

Query to add a column with the individual birth date
addDeathDate

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

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

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

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

Query to add demographic characteristics at a certain date
addDemographics

Compute demographic characteristics at a certain date
addDeathFlag

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

Compute number of intersect with an omop table.
addTableIntersectDate

Compute date of intersect with an omop table.
addInObservation

Indicate if a certain record is within the observation period
addInObservationQuery

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

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

Function to disconnect from the mock
mockPatientProfiles

It creates a mock database for testing PatientProfiles package
addTableIntersectFlag

Compute a flag intersect with an omop table.
addConceptIntersectFlag

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

Query to add the sex of the individuals
addDateOfBirth

Add a column with the individual birth date
addFutureObservation

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

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

Compute the sex of the individuals
startDateColumn

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

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

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.
filterInObservation

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

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.
addTableIntersectDays

Compute time to intersect with an omop table.
addObservationPeriodIdQuery

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

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

Objects exported from other packages
benchmarkPatientProfiles

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

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

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

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

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

Add cohort name for each cohort_definition_id
addCohortIntersectFlag

It creates columns to indicate the presence of cohorts
PatientProfiles-package

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

Date of cohorts that are present in a certain window
addCdmName

Add cdm name
addAge

Compute the age of the individuals at a certain date
addCohortIntersectDays

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

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

Categorize a numeric variable
addCohortIntersectCount

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

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

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

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

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.