Generates a mock measurement table and integrates it into an existing CDM object.

This function simulates measurement records for individuals within a specified cohort. It creates a realistic dataset by generating measurement records based on the specified number of records per person. Each measurement record is correctly associated with an individual within valid observation periods, ensuring the integrity of the data.

Usage

mockMeasurement(cdm, recordPerson = 1, seed = NULL)

Arguments

cdm: A `cdm_reference` object that must already include 'person' and 'observation_period' tables. This object serves as the base CDM structure where the measurement data will be added. The 'person' and 'observation_period' tables must be populated as they are necessary for generating accurate measurement records.
recordPerson: An integer specifying the expected number of measurement records to generate per person. This parameter allows for the simulation of varying frequencies of health measurements among individuals in the cohort, reflecting real-world variability in patient monitoring and diagnostic testing.
seed: An optional integer used to set the seed for random number generation, ensuring reproducibility of the generated data. If provided, this seed enables the function to produce consistent results each time it is run with the same inputs. If 'NULL', the seed is not set, which can lead to different outputs on each run.

Value

Returns the modified `cdm` object with the new 'measurement' table added. This table includes the simulated measurement data for each person, ensuring that each record is correctly linked to individuals in the 'person' table and falls within valid observation periods.

Examples

library(omock)

# Create a mock CDM reference and add measurement records
cdm <- mockCdmReference() |>
  mockPerson() |>
  mockObservationPeriod() |>
  mockMeasurement(recordPerson = 5)

# View the generated measurement data
print(cdm$measurement)
#> # A tibble: 50 × 20
#>    measurement_concept_id person_id measurement_date measurement_id
#>  *                  <int>     <int> <date>                    <int>
#>  1                3020630         1 1991-03-28                    1
#>  2                3020630         2 1983-06-02                    2
#>  3                3020630         7 1988-10-05                    3
#>  4                3020630         6 1991-04-12                    4
#>  5                3020630         4 1979-01-20                    5
#>  6                3020630         9 2009-07-05                    6
#>  7                3020630         4 1977-07-08                    7
#>  8                3020630        10 2009-06-13                    8
#>  9                3020630         3 2005-10-13                    9
#> 10                3020630         1 1990-10-27                   10
#> # ℹ 40 more rows
#> # ℹ 16 more variables: measurement_type_concept_id <int>,
#> #   measurement_datetime <dttm>, measurement_time <chr>,
#> #   operator_concept_id <int>, value_as_number <dbl>,
#> #   value_as_concept_id <int>, unit_concept_id <int>, range_low <dbl>,
#> #   range_high <dbl>, provider_id <int>, visit_occurrence_id <int>,
#> #   visit_detail_id <int>, measurement_source_value <chr>, …