Create cohorts measurement based cohorts

measurementCohort() creates cohorts based on patient records contained in the measurement table. This function extends the conceptCohort() as it allows for measurement values associated with the records to be specified.

• If valueAsConcept and valueAsNumber are NULL then no requirements on of the values associated with measurement records and using measurementCohort() will lead to the same result as using conceptCohort() (so long as all concepts are from the measurement domain).

• If one of valueAsConcept and valueAsNumber is not NULL then records will be required to have values that satisfy the requirement specified.

• If both valueAsConcept and valueAsNumber are not NULL, records will be required to have values that fulfill either of the requirements

Usage

measurementCohort(
  cdm,
  conceptSet,
  name,
  valueAsConcept = NULL,
  valueAsNumber = NULL
)

Arguments

cdm

A cdm reference.

conceptSet

A conceptSet, which can either be a codelist or a conceptSetExpression.

name

Name of the new cohort table created in the cdm object.

valueAsConcept

A vector of cohort IDs used to filter measurements. Only measurements with these values in the value_as_concept_id column of the measurement table will be included. If NULL all entries independently of their value as concept will be considered.

valueAsNumber

A named list indicating the range of values and the unit they correspond to, as follows: list("unit_concept_id" = c(rangeValue1, rangeValue2)). If NULL, all entries independently of their value as number will be included.

Value

A cohort table

Examples

# \donttest{
library(CohortConstructor)
cdm <- mockCohortConstructor(con = NULL)
cdm$concept <- cdm$concept |>
  dplyr::union_all(
    dplyr::tibble(
      concept_id = c(4326744, 4298393, 45770407, 8876, 4124457),
      concept_name = c("Blood pressure", "Systemic blood pressure",
                       "Baseline blood pressure", "millimeter mercury column",
                       "Normal range"),
      domain_id = "Measurement",
      vocabulary_id = c("SNOMED", "SNOMED", "SNOMED", "UCUM", "SNOMED"),
      standard_concept = "S",
      concept_class_id = c("Observable Entity", "Observable Entity",
                           "Observable Entity", "Unit", "Qualifier Value"),
      concept_code = NA,
      valid_start_date = NA,
      valid_end_date = NA,
      invalid_reason = NA
    )
  )
cdm$measurement <- dplyr::tibble(
  measurement_id = 1:4,
  person_id = c(1, 1, 2, 3),
  measurement_concept_id = c(4326744, 4298393, 4298393, 45770407),
  measurement_date = as.Date(c("2000-07-01", "2000-12-11", "2002-09-08",
                                "2015-02-19")),
  measurement_type_concept_id = NA,
  value_as_number = c(100, 125, NA, NA),
  value_as_concept_id = c(0, 0, 0, 4124457),
  unit_concept_id = c(8876, 8876, 0, 0)
)
cdm <- CDMConnector::copyCdmTo(
  con = DBI::dbConnect(duckdb::duckdb()),
  cdm = cdm, schema = "main")

cdm$cohort <- measurementCohort(
  cdm = cdm,
  name = "cohort",
  conceptSet = list("normal_blood_pressure" = c(4326744, 4298393, 45770407)),
  valueAsConcept = c(4124457),
  valueAsNumber = list("8876" = c(70, 120))
)
#> Warning: ! `codelist` contains numeric values, they are casted to integers.
#> ℹ Getting records in observation.
#> ℹ Applying measurement requirements.
#> ℹ Creating cohort attributes.
#> Warning: ! 1 column in cohort do not match expected column type:
#> • `subject_id` is numeric but expected integer
#> ✔ Cohort cohort created.

cdm$cohort
#> # Source:   table<main.cohort> [2 x 4]
#> # Database: DuckDB v1.0.0 [unknown@Linux 6.5.0-1025-azure:R 4.4.1/:memory:]
#>   cohort_definition_id subject_id cohort_start_date cohort_end_date
#>                  <int>      <dbl> <date>            <date>         
#> 1                    1          1 2000-07-01        2000-07-01     
#> 2                    1          3 2015-02-19        2015-02-19     
# }