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: 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

Table with the field value obtained from the intersection

Examples

# \donttest{
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"
  )
#> Warning: ! `codelist` cast to integers.
#> # A query:  ?? x 5
#> # Database: DuckDB 1.5.4 [unknown@Linux 6.17.0-1020-azure:R 4.6.1/:memory:]
#>    cohort_definition_id subject_id cohort_start_date cohort_end_date
#>                   <int>      <int> <date>            <date>         
#>  1                    1          1 1955-12-22        1965-09-28     
#>  2                    2          5 1984-03-25        1991-03-26     
#>  3                    1          9 1939-01-10        1940-07-17     
#>  4                    2          3 1966-01-19        1978-06-11     
#>  5                    2          4 1970-07-03        1971-04-06     
#>  6                    3          2 1940-01-20        1943-09-07     
#>  7                    2          7 1971-01-17        1976-01-30     
#>  8                    2          8 1958-10-26        1966-06-16     
#>  9                    3          6 1969-08-21        1970-04-13     
#> 10                    3         10 1974-03-26        1976-10-01     
#> # ℹ 1 more variable: drug_type_concept_id_acetaminophen_0_to_inf <chr>

# }