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A cdm reference is a single R object that represents OMOP CDM data. The tables in the cdm reference may be in a database, but a cdm reference may also contain OMOP CDM tables that are in dataframes or tibbles, or in arrow. In the latter cases the cdm reference would typically be a subset of an original cdm reference that has been derived as part of a particular analysis.

omopgenerics provides a general class definition a cdm reference and a dataframe/ tibble implementation. For creating a cdm reference using a database, see the CDMConnector package (https://darwin-eu.github.io/CDMConnector/).

A cdm reference is a list of tables. These tables come in three types: standard OMOP CDM tables, cohort tables, and other auxiliary tables.

1) Standard OMOP CDM tables

There are multiple versions of the OMOP CDM. The list of tables included in version 5.3 are as follows.

library(omopgenerics)
#> 
#> Attaching package: 'omopgenerics'
#> The following object is masked from 'package:stats':
#> 
#>     filter
omopTables()
#>  [1] "person"                "observation_period"    "visit_occurrence"     
#>  [4] "visit_detail"          "condition_occurrence"  "drug_exposure"        
#>  [7] "procedure_occurrence"  "device_exposure"       "measurement"          
#> [10] "observation"           "death"                 "note"                 
#> [13] "note_nlp"              "specimen"              "fact_relationship"    
#> [16] "location"              "care_site"             "provider"             
#> [19] "payer_plan_period"     "cost"                  "drug_era"             
#> [22] "dose_era"              "condition_era"         "metadata"             
#> [25] "cdm_source"            "concept"               "vocabulary"           
#> [28] "domain"                "concept_class"         "concept_relationship" 
#> [31] "relationship"          "concept_synonym"       "concept_ancestor"     
#> [34] "source_to_concept_map" "drug_strength"         "cohort_definition"    
#> [37] "attribute_definition"  "concept_recommended"

The standard OMOP tables have required fields. We can check the required column of the person table, for example, like so

omopColumns(table = "person", version = "5.3")
#>  [1] "person_id"                   "gender_concept_id"          
#>  [3] "year_of_birth"               "month_of_birth"             
#>  [5] "day_of_birth"                "birth_datetime"             
#>  [7] "race_concept_id"             "ethnicity_concept_id"       
#>  [9] "location_id"                 "provider_id"                
#> [11] "care_site_id"                "person_source_value"        
#> [13] "gender_source_value"         "gender_source_concept_id"   
#> [15] "race_source_value"           "race_source_concept_id"     
#> [17] "ethnicity_source_value"      "ethnicity_source_concept_id"
omopColumns(table = "observation_period", version = "5.3")
#> [1] "observation_period_id"         "person_id"                    
#> [3] "observation_period_start_date" "observation_period_end_date"  
#> [5] "period_type_concept_id"

2) Cohort tables

Studies using the OMOP CDM often create study-specific cohort tables. We also consider these as part of the cdm reference once created. Each cohort table is associated with a specific class of its own, a generatedCohortSet, which is described more in a subsequent vignette. As with the standard OMOP CDM tables, cohort tables are expected to contain a specific set of fields (with no restriction placed on whether they include additional fields or not).

cohortColumns(table = "cohort", version = "5.3")
#> [1] "cohort_definition_id" "subject_id"           "cohort_start_date"   
#> [4] "cohort_end_date"
cohortColumns(table = "cohort_set", version = "5.3")
#> [1] "cohort_definition_id" "cohort_name"
cohortColumns(table = "cohort_attrition", version = "5.3")
#> [1] "cohort_definition_id" "number_records"       "number_subjects"     
#> [4] "reason_id"            "reason"               "excluded_records"    
#> [7] "excluded_subjects"

3) Achilles result tables

The Achilles R package provides descriptive statistics on an OMOP CDM database. The results from Achilles are stored in tables in the database. The following tables are created with the given columns.

achillesTables()
#> [1] "achilles_analysis"     "achilles_results"      "achilles_results_dist"
achillesColumns("achilles_analysis")
#> [1] "analysis_id"    "analysis_name"  "stratum_1_name" "stratum_2_name"
#> [5] "stratum_3_name" "stratum_4_name" "stratum_5_name" "is_default"    
#> [9] "category"
achillesColumns("achilles_results")
#> [1] "analysis_id" "stratum_1"   "stratum_2"   "stratum_3"   "stratum_4"  
#> [6] "stratum_5"   "count_value"
achillesColumns("achilles_results_dist")
#>  [1] "analysis_id"  "stratum_1"    "stratum_2"    "stratum_3"    "stratum_4"   
#>  [6] "stratum_5"    "count_value"  "min_value"    "max_value"    "avg_value"   
#> [11] "stdev_value"  "median_value" "p10_value"    "p25_value"    "p75_value"   
#> [16] "p90_value"

4) Other tables

Beyond the standard OMOP CDM tables and cohort tables, additional tables can be added to the cdm reference. These tables could, for example, be OMOP extension/ expansion tables or extra tables containing data required to perform a study but not normally included as part of the OMOP CDM. These tables could contain any set of fields.

General rules for a cdm reference

Any table to be part of a cdm object has to fulfill the following conditions:

  • All tables must share a common source (that is, a mix of tables in the database and in-memory is not permitted).

  • The name of the tables must be lower snake_case.

  • The name of the column names of each table must be lower snake_case.

  • The person and observation_period tables must be present.

  • The cdm reference must have an attribute “cdmName” that gives the name associated with the data contained there within.

Export metadata about the cdm reference

When the export method is applied to a cdm reference, metadata about that cdm will be written to a csv. The csv contains the following columns

Variable Description Datatype Required
result_type Always “Snapshot”. Identifies this result as a summary of a cdm reference. Character Yes
cdm_name The name of the data source. Character Yes
cdm_source_name Value of cdm source name taken from the cdm source table (if present in the cdm reference). Character No
cdm_description Value of cdm description taken from the cdm source table (if present in the cdm reference). Character No
cdm_documentation_reference Value of cdm documentation reference taken from the cdm source table (if present in the cdm reference). Character No
cdm_version The cdm version associated with the cdm reference. Character Yes
cdm_holder Value of cdm holder reference taken from the cdm source table (if present in the cdm reference). Character No
cdm_release_date Value of cdm release date taken from the cdm source table (if present in the cdm reference). Date No
vocabulary_version Version of the vocabulary being used taken from the concept table (if present in the cdm reference). Character No
person_count Number of records in the person table. Integer Yes
observation_period_count Number of records in the observation period table. Integer Yes
earliest_observation_period_start_date Earliest date in the observation period start date field from the observation period table. Date Yes
latest_observation_period_end_date Latest date in the observation period start date field from the observation period table. Date Yes
snapshot_date Date at which this snapshot was created. Date Yes