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Does my cohort picked the correct number patients? Am I calculating an intersection in the right way? Is that the expected value for treatment duration? It just takes one incorrect parameter to get incoherent results in a pharmacoepidemiological study, and it is very challenging to test calculations on huge and complex databases.

That is why TestGenerator is useful to push a micro sample of around 10 patients to unit test a study on the OMOP-CDM. It includes tools to create a blank CDM with a complete vocabulary and check if the code is doing what we expect.

This package is based on the unit testing written for the Eramus MC Ranitidine Study.


To install the development version of TestGenerator:

# Development version


The user should provide an Excel file (link to sample) with a micro population of around 10 patients for testing purposes. That can include any table from the OMOP-CDM.

readPatients() will read the Excel file, and saves the data in a JSON file. This is useful if the user wants to create more than one Unit Test Definitions.

  filePath = "~/pathto/testPatients.xlsx",
  testName = "test",
  outputPath = "inst/testCases"

patientCDM() pushes one of those Unit Test Definitions into a blank CDM reference with a complete version of the vocabulary.

cdm <- TestGenerator::patientsCDM(
  pathJson = "inst/testCases", 
  testName = "test")

Now the user has a CDM reference with a complete vocabulary and just 10 patients to unit test functions of a particular study.

#> Unit Test Definition created successfully: test
#> Patients pushed to blank CDM successfully
#> # OMOP CDM reference (tbl_duckdb_connection)
#> Tables: person, observation_period, visit_occurrence, visit_detail, condition_occurrence, drug_exposure, procedure_occurrence, device_exposure, measurement, observation, death, note, note_nlp, specimen, fact_relationship, location, care_site, provider, payer_plan_period, cost, drug_era, dose_era, condition_era, metadata, cdm_source, concept, vocabulary, domain, concept_class, concept_relationship, relationship, concept_synonym, concept_ancestor, source_to_concept_map, drug_strength, cohort_definition, attribute_definition

#> # Source:   table<person> [?? x 18]
#> # Database: DuckDB v0.9.1 [cbarboza@Windows 10 x64:R 4.3.1/C:\Users\cbarboza\AppData\Local\Temp\RtmpUBR77a\file697c4ada43e2.duckdb]
#>    person_id gender_concept_id year_of_birth month_of_birth day_of_birth
#>        <int>             <int>         <int>          <int>        <int>
#>  1         1              8532          1980             NA           NA
#>  2         2              8507          1980             NA           NA
#>  3         3              8532          1965             NA           NA
#>  4         4              8532          2010             NA           NA
#>  5         5              8532          1936             NA           NA
#>  6         6              8532          1970             NA           NA
#>  7         7              8532          1988             NA           NA
#>  8         8              8507          1998             NA           NA
#>  9         9              8507          1990             NA           NA
#> 10        10              8532          1945             NA           NA
#> # ℹ more rows
#> # ℹ 13 more variables: birth_datetime <dttm>, race_concept_id <int>,
#> #   ethnicity_concept_id <int>, location_id <int>, provider_id <int>,
#> #   care_site_id <int>, person_source_value <chr>, gender_source_value <chr>,
#> #   gender_source_concept_id <int>, race_source_value <chr>,
#> #   race_source_concept_id <int>, ethnicity_source_value <chr>,
#> #   ethnicity_source_concept_id <int>