CohortConstructor (version 0.6.1)

measurementCohort: Create measurement-based cohorts

Description

measurementCohort() creates cohorts based on patient records from the measurement or observation tables. It extends the function conceptCohort() by allowing users to specify measurement values associated with those records.

This function supports both concept-based and value-based filtering:

• Either valueAsConcept or valueAsNumber must be provided.

• If one of them is specified (not NULL), only records that satisfy the other filter will be included.

• If both are provided, records that meet either filter will be included.

Usage

measurementCohort(
  cdm,
  conceptSet,
  name,
  valueAsConcept = NULL,
  valueAsNumber = NULL,
  table = NULL,
  useRecordsBeforeObservation = FALSE,
  useSourceFields = FALSE,
  subsetCohort = NULL,
  subsetCohortId = NULL
)

Value

A cohort table.

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 named list defining cohorts based on measurement values as concept IDs. Each element name defines the name of cohort to create, and its value is a vector of concept IDs used to filter measurements by value_as_concept_id. If NULL, all records will be included regardless of value_as_concept_id.

For instance, to create two bmi cohorts from a bmi conceptSet we can do the following: valueAsConcept = list(high_bmi = c(4328749, 35819253), low_bmi = c(4267416, 45881666))

See more examples in the function examples.

valueAsNumber

A named list defining cohorts based on numeric measurement ranges. Each list element should contain one or more numeric vectors of length two, specifying the allowed range(s) for the measurement value. If the numeric vector is named, the name should correspond to the unit_concept_id that will be used for that range.

For example, the following creates a cohort named "low_weight" based on measurements recorded in kilograms (unit_concept_id = 9529) and stones (unit_concept_id = 21498905): valueAsNumber = list("low_weight" = list("9529" = c(30, 40), "21498905" = c(4.7, 6.3)))

See the examples below for how to define multiple cohorts based on different measurement filters.

table

Character vector specifying which OMOP tables to use. Accepts "measurement", "observation", or both.

useRecordsBeforeObservation

If FALSE, only records in observation will be used. If TRUE, records before the start of observation period will be considered, with cohort start date set as the start date of the individuals next observation period (as cohort records must be within observation).

useSourceFields

If TRUE, the source concept_id fields will also be used when identifying relevant clinical records. If FALSE, only the standard concept_id fields will be used.

subsetCohort

A character refering to a cohort table containing individuals for whom cohorts will be generated. Only individuals in this table will appear in the generated cohort.

subsetCohortId

Optional. Specifies cohort IDs from the subsetCohort table to include. If none are provided, all cohorts from the subsetCohort are included.

Examples

# \donttest{
library(CohortConstructor)
library(omock)
library(dplyr)

cdm <- mockVocabularyTables(concept = 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_character_,
  valid_start_date = as.Date(NA_character_),
  valid_end_date = as.Date(NA_character_),
  invalid_reason = NA_character_
)) |>
  mockCdmFromTables(tables = list(
    measurement = tibble(
      measurement_id = 1:4L,
      person_id = c(1L, 1L, 2L, 3L),
      measurement_concept_id = c(4326744L, 4298393L, 4298393L, 45770407L),
      measurement_date = as.Date(c("2000-07-01", "2000-12-11", "2002-09-08", "2015-02-19")),
      measurement_type_concept_id = 0L,
      value_as_number = c(100, 125, NA, NA),
      value_as_concept_id = c(0L, 0L, 0L, 4124457L),
      unit_concept_id = c(8876L, 8876L, 0L, 0L)
   )
 ))

# create one cohort of blood pressure measurements indicating normal levels
cdm$cohort <- measurementCohort(
  cdm = cdm,
  name = "cohort",
  conceptSet = list("blood_pressure" = c(4326744, 4298393, 45770407)),
  valueAsConcept = list("normal_blood_preassure" = c(4124457)),
  valueAsNumber = list("normal_blood_preassure" = list("8876" = c(70, 120))),
  useRecordsBeforeObservation = FALSE
)

cdm$cohort

# create two cohorts of blood preassure measurements, one with results
# indicating normal blood pressure and the other inidcating high

cdm$cohort2 <- measurementCohort(
  cdm = cdm,
  name = "cohort2",
  conceptSet = list("blood_pressure" = c(4326744, 4298393, 45770407)),
  valueAsConcept = list(
    "normal_blood_pressure" = 4124457,
    "high_blood_pressure" = 4328749
  ),
  valueAsNumber = list(
    "normal_blood_pressure" = list("8876" = c(70, 120)),
    "high_blood_pressure" = list("8876" = c(121, 200))
  ),
  useRecordsBeforeObservation = TRUE
)

cdm$cohort2 |> settings()

# }