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.
# CRAN version
install.packages("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.
cdm <- TestGenerator::patientsCDM(pathJson = NULL,
testName = "test",
cdmVersion = "5.3")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")
#> â Unit Test Definition Created Successfully: 'test'
cdm <- TestGenerator::patientsCDM(pathJson = outputPath,
testName = "test",
cdmVersion = "5.3")
#> Note: method with signature 'DBIConnection#Id' chosen for function 'dbExistsTable',
#> target signature 'duckdb_connection#Id'.
#> "duckdb_connection#ANY" would also be valid
#> ! cdm name not specified and could not be inferred from the cdm source table
#> â Patients pushed to blank CDM successfully
cdm[["person"]] %>% glimpse()
#> Rows: ??
#> Columns: 18
#> Database: DuckDB v1.0.0 [root@Darwin 24.1.0:R 4.4.1//private/var/folders/wm/s6fjrtt53ld72z03p47nkdvr0000gn/T/RtmpXvHHUw/file18235526d6af4.duckdb]
#> $ person_id <int> 1, 2, 3, 4, 5, 6, 7, 8
#> $ gender_concept_id <int> 8532, 8507, 8532, 8507, 8532, 8507, 8532, â¦
#> $ year_of_birth <int> 1980, 1990, 2000, 1980, 1990, 2000, 1980, â¦
#> $ 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, NAThe 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::generateCohortSet(cdm,
cohort_set,
name = "test_cohorts")
#> â¹ Generating 3 cohorts
#> â¹ Generating cohort (1/3) - diazepamâ Generating cohort (1/3) - diazepam [351ms]
#> â¹ Generating cohort (2/3) - hospitalisationâ Generating cohort (2/3) - hospitalisation [272ms]
#> â¹ Generating cohort (3/3) - icu_visitâ Generating cohort (3/3) - icu_visit [132ms]
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: fillRetroSearch is an open source project built by @garambo | Open a GitHub Issue
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