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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 small sample of 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 in very specific cases.
This package is based on the unit testing written for the Eramus MC Ranitidine Study.
To install TestGenerator:
The user can provide an Excel file (link to sample) or a set of CSV files that represent tables of the OMOP-CDM, with a micro population of just 8 patients for testing purposes.
readPatients()
will read either Excel or CSVs, and then saves the data in a JSON file. This is useful if the user wants to create more than one Unit Test Definitions. If the parameter outputPath
is NULL
The files are saved in the testthat/testCases
folder of the package. Alterna
TestGenerator::readPatients(filePath = "~/pathto/testPatients.xlsx",
testName = "test",
outputPath = NULL,
cdmVersion = "5.3")
Alternatively, the user can use the functions readPatients.xl
or readPatients.csv
directly.
TestGenerator::readPatients.xl(filePath = "~/pathto/testPatients.xlsx",
testName = "test",
outputPath = NULL,
cdmVersion = "5.3")
TestGenerator::readPatients.csv(filePath = "~/pathto/csv/files",
testName = "test",
outputPath = NULL,
cdmVersion = "5.3",
reduceLargeIds = FALSE)
patientCDM()
pushes one of those Unit Test Definitions into a blank CDM reference with a complete version of the vocabulary. If the pathJSON
parameter is NULL
, TestGenerator
will look for the JSON test files in the testthat/testCases
folder.
Now the user has a CDM reference with a complete vocabulary and just 8 patients.
filePath <- system.file("extdata/icu_sample_population.xlsx",
package = "TestGenerator")
outputPath <- file.path(tempdir(), "test")
dir.create(outputPath)
TestGenerator::readPatients(filePath = filePath,
testName = "test",
outputPath = outputPath,
cdmVersion = "5.3")
#> ✔ Unit Test Definition Created Successfully: 'test'
cdm <- TestGenerator::patientsCDM(pathJson = outputPath,
testName = "test",
cdmVersion = "5.3")
#> ! cdm name not specified and could not be inferred from the cdm source table
#> ✔ Patients pushed to blank CDM successfully
cdm[["person"]] %>% glimpse()
#> Rows: ??
#> Columns: 18
#> Database: DuckDB v0.9.1 [cbarboza@Windows 10 x64:R 4.3.1/C:\Users\cbarboza\AppData\Local\Temp\Rtmp44K6CT\file6a812027396.duckdb]
#> $ person_id <int> 1, 2, 3, 4, 5, 6, 7, 8
#> $ gender_concept_id <int> 8532, 8507, 8532, 8507, 8532, 8507, 8532, …
#> $ year_of_birth <int> 1980, 1990, 2000, 1980, 1990, 2000, 1980, …
#> $ month_of_birth <int> NA, NA, NA, NA, NA, NA, NA, NA
#> $ day_of_birth <int> NA, NA, NA, NA, NA, NA, NA, NA
#> $ birth_datetime <dttm> NA, NA, NA, NA, NA, NA, NA, NA
#> $ race_concept_id <int> 0, 0, 0, 0, 0, 0, 0, 0
#> $ ethnicity_concept_id <int> 0, 0, 0, 0, 0, 0, 0, 0
#> $ location_id <int> 0, 0, 0, 0, 0, 0, 0, 0
#> $ provider_id <int> 0, 0, 0, 0, 0, 0, 0, 0
#> $ care_site_id <int> 0, 0, 0, 0, 0, 0, 0, 0
#> $ person_source_value <chr> "0", "0", "0", "0", "0", "0", "0", "0"
#> $ gender_source_value <chr> "M", "F", "M", "F", "M", "F", "M", "F"
#> $ gender_source_concept_id <int> NA, NA, NA, NA, NA, NA, NA, NA
#> $ race_source_value <chr> NA, NA, NA, NA, NA, NA, NA, NA
#> $ race_source_concept_id <int> NA, NA, NA, NA, NA, NA, NA, NA
#> $ ethnicity_source_value <chr> NA, NA, NA, NA, NA, NA, NA, NA
#> $ ethnicity_source_concept_id <int> NA, NA, NA, NA, NA, NA, NA, NA
The reference can be used to create a cohort and create unit tests.
test_cohorts <- system.file("extdata",
"test_cohorts",
package = "TestGenerator")
cohort_set <- CDMConnector::readCohortSet(test_cohorts)
cdm <- CDMConnector::generate_cohort_set(cdm,
cohort_set,
name = "test_cohorts")
#> ℹ Generating 3 cohorts
#> ℹ Generating cohort (1/3) - diazepam✔ Generating cohort (1/3) - diazepam [345ms]
#> ℹ Generating cohort (2/3) - hospitalisation✔ Generating cohort (2/3) - hospitalisation [309ms]
#> ℹ Generating cohort (3/3) - icu_visit✔ Generating cohort (3/3) - icu_visit [191ms]
cohortAttrition <- CDMConnector::attrition(cdm[["test_cohorts"]])
excluded_records <- cohortAttrition %>%
pull(excluded_records) %>%
sum()
expect_equal(excluded_records, 0)
With graphCohort()
it is possible to visualise the timeline for particular patient.
diazepam <- cdm[["test_cohorts"]] %>%
filter(cohort_definition_id == 1) %>%
collect()
hospitalisation <- cdm[["test_cohorts"]] %>%
filter(cohort_definition_id == 2) %>%
collect()
icu_visit <- cdm[["test_cohorts"]] %>%
filter(cohort_definition_id == 3) %>%
collect()
TestGenerator::graphCohort(subject_id = 4, list("diazepam" = diazepam,
"hospitalisation" = hospitalisation,
"icu_visit" = icu_visit))
#> Warning in geom_segment(aes(x = cohort_start_date, y = cohort, xend =
#> cohort_end_date, : Ignoring unknown aesthetics: fill
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.