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: 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 added columns with overlap information.

Examples

# \donttest{
library(PatientProfiles)
library(dplyr)

cdm <- mockPatientProfiles(source = "duckdb")
#> duckdb keeps downloaded extensions and secrets in a temporary directory:
#> ℹ /tmp/RtmpB2T0r9/duckdb
#> This is removed when the R session ends.
#> • Extensions are re-downloaded each session.
#> • Secrets are lost.
#> ℹ Run duckdb(shared_home = TRUE) (or create ~/.duckdb) to keep them (suitable for most users).
#> ℹ Run duckdb(shared_home = FALSE) to accept the temporary directory (and silence this message).
#> ℹ See ?duckdb_storage for details and alternatives.

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

cdm$cohort1 |>
  addCohortIntersectFlag(
    targetCohortTable = "cohort2"
  )
#> # A query:  ?? x 7
#> # Database: DuckDB 1.5.5 [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                    2          6 1952-08-01        1965-06-15     
#>  2                    1          7 1912-04-22        1921-06-29     
#>  3                    1          3 1984-02-05        1985-10-29     
#>  4                    2          4 1988-09-20        1992-11-03     
#>  5                    3         10 1947-01-21        1947-04-18     
#>  6                    1          9 1950-08-14        1951-02-10     
#>  7                    1          5 1912-06-23        1920-04-13     
#>  8                    1          8 1982-12-10        1985-02-05     
#>  9                    3          2 1967-10-12        1975-01-21     
#> 10                    3          1 1922-07-04        1939-05-11     
#> # ℹ 3 more variables: cohort_1_0_to_inf <dbl>, cohort_2_0_to_inf <dbl>,
#> #   cohort_3_0_to_inf <dbl>

# }