A sample version of an OMOP compliant input file for the Unmapped Concepts data quality module. This exact file is also included as a CSV in the package if the user wishes to use it, or the structure can be copied to produce a custom list of checks.
Format
ecp_input_omop
A dataframe or CSV file with 9 columns
- check_id
A short string "code" used to identify the specific check (ex: de, de_rx)
- cohort_definition
The definition of the specific cohort used to identify patients with the concept of interest
- cohort_schema
The schema where the cohort_table is kept. Use
cdmto use the pre-configuredcdm_schema,resultto use the preconfiguredresults_schema, or input the exact name of the schema.- cohort_table
The name of the CDM or pre-computed results table where the cohort has been stored
- schema
The schema where the data is kept. Use
cdmto use the pre-configuredcdm_schema,resultto use the preconfiguredresults_schema, or input the exact name of the schema.- table
The name of the CDM or pre-computed results table where the relevant data is kept
- concept_field
The *_concept_id field with the concepts that make up the valueset (ex: drug_concept_id, drug_source_concept_id)
- conceptset_name
The string name of the concept set that will identify the concepts of interest, as it appears in the predefined file_subdirectory
- filter_logic
OPTIONAL The logic that should be applied to the provided table in order to tailor the tables to the desired check assessment (ex: if you only want to identify prescriptions for antihypertensives)
Details
If you choose to use this sample file, please be sure to
set your file_subdirectory
to system.file("sample_conceptsets", package = "ndq") so
the functions know where to access the associated concept sets. You
also have the option to download the sample concept sets to a local
directory and point to that location.