measurementCohort {CohortConstructor} | R Documentation |
Create measurement-based cohorts
Description
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
andvalueAsNumber
are NULL then no requirements on of the values associated with measurement records and usingmeasurementCohort()
will lead to the same result as usingconceptCohort()
(so long as all concepts are from the measurement domain).If one of
valueAsConcept
andvalueAsNumber
is not NULL then records will be required to have values that satisfy the requirement specified.If both
valueAsConcept
andvalueAsNumber
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,
table = c("measurement", "observation"),
inObservation = TRUE
)
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 |
valueAsNumber |
A list indicating the range of values and the unit they correspond to, as follows: list("unit_concept_id" = c(rangeValue1, rangeValue2)). If no name is supplied in the list, no requirement on unit concept id will be applied. If NULL, all entries independent of their value as number will be included. |
table |
Name of OMOP tables to search for records of the concepts provided. Options are "measurement" and/or "observation". |
inObservation |
If TRUE, only records in observation will be used. If FALSE, records before the start of observation period will be considered, with startdate the start of observation. |
Value
A cohort table
Examples
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)),
inObservation = TRUE
)
cdm$cohort
# You can also create multiple measurement cohorts, and include records
# outside the observation period.
cdm$cohort2 <- measurementCohort(
cdm = cdm,
name = "cohort2",
conceptSet = list("normal_blood_pressure" = c(4326744, 4298393, 45770407),
"high_blood_pressure" = c(4326744, 4298393, 45770407)),
valueAsConcept = c(4124457),
valueAsNumber = list("8876" = c(70, 120),
"8876" = c(121, 200)),
inObservation = FALSE
)
cdm$cohort2