Generates a mock CDM (Common Data Model) object based on existing CDM structures and additional tables.

This function takes an existing CDM reference (which can be empty) and a list of additional named tables to create a more complete mock CDM object. It ensures that all provided observations fit within their respective observation periods and that all individual records are consistent with the entries in the person table. This is useful for creating reliable and realistic healthcare data simulations for development and testing within the OMOP CDM framework.

Usage

mockCdmFromTables(
  cdm = mockCdmReference(),
  tables = list(),
  maxObservationalPeriodEndDate = as.Date("01-01-2024", "%d-%m-%Y"),
  seed = NULL
)

Arguments

cdm: A local cdm_reference object used as the base structure to update.
tables: A named list of data frames representing additional tables to be integrated into the CDM. These tables can include both standard OMOP tables such as 'drug_exposure' or 'condition_occurrence', as well as cohort-specific tables that are not part of the standard OMOP model but are necessary for specific analyses. Each table should be named according to its intended table name in the CDM structure.
maxObservationalPeriodEndDate: A Date object specifying the latest allowable end date for the observation period. This value ensures that observation_period_end_date values do not exceed the current calendar date.
seed: An optional integer used to set the random seed for reproducibility. If NULL, the seed is not set.

Value

A modified cdm_reference object.

Examples

# \donttest{
library(omock)
library(dplyr)
#> 
#> Attaching package: ‘dplyr’
#> The following objects are masked from ‘package:stats’:
#> 
#>     filter, lag
#> The following objects are masked from ‘package:base’:
#> 
#>     intersect, setdiff, setequal, union

# Create a mock cohort table
cohort <- tibble(
  cohort_definition_id = c(1, 1, 2, 2, 1, 3, 3, 3, 1, 3),
  subject_id = c(1, 4, 2, 3, 5, 5, 4, 3, 3, 1),
  cohort_start_date = as.Date(c(
    "2020-04-01", "2021-06-01", "2022-05-22", "2010-01-01", "2019-08-01",
    "2019-04-07", "2021-01-01", "2008-02-02", "2009-09-09", "2021-01-01"
  )),
  cohort_end_date = cohort_start_date
)

# Generate a mock CDM from preexisting CDM structure and cohort table
cdm <- mockCdmFromTables(cdm = mockCdmReference(), tables = list(cohort = cohort))

# Access the newly integrated cohort table and the standard person table in the CDM
cdm$cohort |> glimpse()
#> Rows: 10
#> Columns: 4
#> $ cohort_definition_id <int> 1, 1, 2, 2, 1, 3, 3, 3, 1, 3
#> $ subject_id           <int> 1, 4, 2, 3, 5, 5, 4, 3, 3, 1
#> $ cohort_start_date    <date> 2020-04-01, 2021-06-01, 2022-05-22, 2010-01-01, 2…
#> $ cohort_end_date      <date> 2020-04-01, 2021-06-01, 2022-05-22, 2010-01-01, 2…
cdm$person |> glimpse()
#> Rows: 5
#> Columns: 18
#> $ person_id                   <int> 1, 2, 3, 4, 5
#> $ gender_concept_id           <int> 8532, 8507, 8532, 8507, 8532
#> $ year_of_birth               <int> 2001, 2015, 2001, 2007, 2012
#> $ month_of_birth              <int> 9, 1, 3, 5, 8
#> $ day_of_birth                <int> 21, 9, 6, 7, 29
#> $ birth_datetime              <dttm> 2001-09-21, 2015-01-09, 2001-03-06, 2007-0…
#> $ race_concept_id             <int> 38003603, 38003589, 38003591, 38003610, 38…
#> $ ethnicity_concept_id        <int> 38003563, 38003563, 38003564, 38003564, 38…
#> $ location_id                 <int> 0, 0, 0, 0, 0
#> $ person_source_value         <chr> "", "", "", "", ""
#> $ gender_source_value         <chr> "", "", "", "", ""
#> $ gender_source_concept_id    <int> 0, 0, 0, 0, 0
#> $ race_source_value           <chr> "", "", "", "", ""
#> $ race_source_concept_id      <int> 0, 0, 0, 0, 0
#> $ ethnicity_source_value      <chr> "", "", "", "", ""
#> $ ethnicity_source_concept_id <int> 0, 0, 0, 0, 0
#> $ provider_id                 <int> 0, 0, 0, 0, 0
#> $ care_site_id                <int> 0, 0, 0, 0, 0
# }