| pattern_id | amount_unit | numerator_unit | denominator_unit |
|---|---|---|---|
| 1 | international unit | NA | NA |
| 2 | microgram | NA | NA |
| 3 | milliequivalent | NA | NA |
| 4 | milligram | NA | NA |
| 5 | millimiter | NA | NA |
| 6 | NA | microgram | hour |
| 7 | NA | milligram | actuation |
| 8 | NA | microgram | hour |
| 9 | NA | milligram | hour |
DrugUtilisation contains functions to instantiate and characterize the cohorts used in a Drug Utilisation Study in the OMOP common data model. Main functionalities are:
Create DrugUtilisation cohorts
Add indications to this cohort
Add the dosage of a certain ingredient (subseted for a list of drugs)
Calculate the daily dose
Create Concept based cohorts
Read concepts from json files
Summarise the drug use in a certain cohort
Summarise the indications in a certain cohort
Summarise the patients characteristics in a certain cohort
Summarise the patients large scale characterics in a certain cohort
First, we need to create a cdm reference for the data we´ll be using. Here we´ll generate an example with simulated data, but to see how you would set this up for your database please consult the CDMConnector package connection examples.
The package also provides a functionality to generate a mockDrugUtilisation cdm reference:
To create a cohort we will need a conceptList, this can be read from json files:
Or we can build our own list using other packages (e.g. CodelistGenerator)
library(CodelistGenerator)
#> Warning: package 'CodelistGenerator' was built under R version 4.2.3
conceptList <- getDrugIngredientCodes(cdm, "acetaminophen")
conceptList
#> $acetaminophen
#> [1] 1125315 43135274 2905077 1125360To generate the cohort of drug use we will use generateDrugUtilisationCohortSet:
cdm <- generateDrugUtilisationCohortSet(
cdm = cdm,
name = "dus_cohort",
conceptSet = conceptList,
limit = "first",
priorObservation = 365,
gapEra = 30,
priorUseWashout = 0,
imputeDuration = "none",
durationRange = c(0, Inf)
)The generated cohort will have the GeneratedCohortSet as seen in CDMConnector
class(cdm[["dus_cohort"]])
#> [1] "cohort_table" "GeneratedCohortSet" "cdm_table"
#> [4] "tbl_duckdb_connection" "tbl_dbi" "tbl_sql"
#> [7] "tbl_lazy" "tbl"Cohort set:
library(CDMConnector)
library(dplyr)
#> Warning: package 'dplyr' was built under R version 4.2.3
#>
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#>
#> filter, lag
#> The following objects are masked from 'package:base':
#>
#> intersect, setdiff, setequal, union
settings(cdm[["dus_cohort"]]) %>% glimpse()
#> Rows: 1
#> Columns: 11
#> $ cohort_definition_id <int> 1
#> $ cohort_name <chr> "acetaminophen"
#> $ duration_range_min <chr> "0"
#> $ duration_range_max <chr> "Inf"
#> $ impute_duration <chr> "none"
#> $ gap_era <chr> "30"
#> $ prior_use_washout <chr> "0"
#> $ prior_observation <chr> "365"
#> $ cohort_date_range_start <chr> NA
#> $ cohort_date_range_end <chr> NA
#> $ limit <chr> "first"Cohort count:
cohortCount(cdm[["dus_cohort"]])
#> # A tibble: 1 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 1 35 35Cohort attrition:
attrition(cdm[["dus_cohort"]]) %>% glimpse()
#> Rows: 4
#> Columns: 7
#> $ cohort_definition_id <int> 1, 1, 1, 1
#> $ number_records <int> 71, 70, 41, 35
#> $ number_subjects <int> 62, 62, 35, 35
#> $ reason_id <int> 1, 2, 3, 4
#> $ reason <chr> "Initial qualifying events", "join exposures sepa…
#> $ excluded_records <int> 0, 1, 29, 6
#> $ excluded_subjects <int> 0, 0, 27, 0Indications will always be cohorts. An option that the package has is to create concept based cohorts using generateConceptCohortSet.
indications <- list(headache = 378253, influenza = 4266367)
cdm <- generateConceptCohortSet(cdm, indications, "indications_cohort")
cohortCount(cdm[["indications_cohort"]])
#> # A tibble: 2 × 3
#> cohort_definition_id number_records number_subjects
#> <int> <int> <int>
#> 1 1 52 52
#> 2 2 46 46Then we can add the indication using the function addIndication. That will add a new column for each indication gap and indication.
x <- cdm[["dus_cohort"]] %>%
addIndication(
cdm = cdm, indicationCohortName = "indications_cohort", indicationGap = c(0, 30, 365),
unknownIndicationTable = c("condition_occurrence")
)
#> Warning: The `cdm` argument of `addIndication()` is deprecated as of DrugUtilisation
#> 0.5.0.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
glimpse(x)
#> Rows: ??
#> Columns: 16
#> Database: DuckDB 0.7.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ subject_id <int> 81, 47, 96, 75, 91, 93, 46, 88, 8, 54, 9,…
#> $ cohort_start_date <date> 1995-09-13, 1963-12-22, 2011-05-25, 2010…
#> $ cohort_end_date <date> 1997-11-17, 1965-09-16, 2013-01-23, 2011…
#> $ indication_gap_0_headache <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_0_influenza <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_0_none <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ indication_gap_0_unknown <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_30_headache <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_30_influenza <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_30_none <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,…
#> $ indication_gap_30_unknown <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
#> $ indication_gap_365_headache <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_365_influenza <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_365_none <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,…
#> $ indication_gap_365_unknown <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…We can combine the indications in a single column using the indicationToStrata() function. This column can be used as stratification of the results if needed:
x <- x %>% indicationToStrata(keep = TRUE)
glimpse(x)
#> Rows: ??
#> Columns: 19
#> Database: DuckDB 0.7.1 [martics@Windows 10 x64:R 4.2.1/:memory:]
#> $ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ subject_id <int> 81, 47, 96, 75, 91, 93, 46, 88, 8, 54, 9,…
#> $ cohort_start_date <date> 1995-09-13, 1963-12-22, 2011-05-25, 2010…
#> $ cohort_end_date <date> 1997-11-17, 1965-09-16, 2013-01-23, 2011…
#> $ indication_gap_0_headache <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_0_influenza <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_0_none <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ indication_gap_0_unknown <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_30_headache <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_30_influenza <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_30_none <dbl> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 0, 1, 1,…
#> $ indication_gap_30_unknown <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
#> $ indication_gap_365_headache <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_365_influenza <dbl> 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_365_none <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 0, 1, 1,…
#> $ indication_gap_365_unknown <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0,…
#> $ indication_gap_0 <chr> "None", "None", "None", "None", "None", "…
#> $ indication_gap_30 <chr> "None", "None", "None", "None", "None", "…
#> $ indication_gap_365 <chr> "None", "None", "None", "None", "None", "…We can summarise the indication results using the summariseIndication function:
summariseIndication(x, cdm)
#> # A tibble: 42 × 15
#> cdm_name result_type package_name package_version group_name group_level
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 2 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 3 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 4 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 5 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 6 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 7 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 8 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 9 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 10 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> # ℹ 32 more rows
#> # ℹ 9 more variables: strata_name <chr>, strata_level <chr>,
#> # variable_name <chr>, variable_level <chr>, estimate_name <chr>,
#> # estimate_type <chr>, estimate_value <chr>, additional_name <chr>,
#> # additional_level <chr>summariseIndication(x, cdm) %>% glimpse()
#> Rows: 42
#> Columns: 15
#> $ cdm_name <chr> "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS …
#> $ result_type <chr> "summarised_indication", "summarised_indication", "su…
#> $ package_name <chr> "DrugUtilisation", "DrugUtilisation", "DrugUtilisatio…
#> $ package_version <chr> "0.5.0", "0.5.0", "0.5.0", "0.5.0", "0.5.0", "0.5.0",…
#> $ group_name <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_…
#> $ group_level <chr> "acetaminophen", "acetaminophen", "acetaminophen", "a…
#> $ strata_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name <chr> "number subjects", "number records", "Indication on i…
#> $ variable_level <chr> NA, NA, "Headache", "Headache", "Influenza", "Influen…
#> $ estimate_name <chr> "count", "count", "count", "percentage", "count", "pe…
#> $ estimate_type <chr> "numeric", "numeric", "numeric", "percentage", "numer…
#> $ estimate_value <chr> "35", "35", "0", "0", "0", "0", "35", "100", "0", "0"…
#> $ additional_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ additional_level <chr> "overall", "overall", "overall", "overall", "overall"…All summarise functions have the option to add strata. Strata will always point to preexisting columns. Here we can see an example where we create a age_group and sex columns using PatientProfiles and then we use it as strata
library(PatientProfiles)
x <- x %>%
addAge(cdm, ageGroup = list(c(0, 19), c(20, 39), c(40, 59), c(60, 79), c(80, 150))) %>%
addSex(cdm)
#> Warning: The `cdm` argument of `addSex()` is deprecated as of PatientProfiles 0.6.0.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> Warning: The `cdm` argument of `addAge()` is deprecated as of PatientProfiles 0.6.0.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
summariseIndication(x, cdm, strata = list("age_group", "sex", c("age_group", "sex")))
#> # A tibble: 466 × 15
#> cdm_name result_type package_name package_version group_name group_level
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 2 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 3 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 4 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 5 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 6 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 7 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 8 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 9 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> 10 DUS MOCK summarised_indi… DrugUtilisa… 0.5.0 cohort_na… acetaminop…
#> # ℹ 456 more rows
#> # ℹ 9 more variables: strata_name <chr>, strata_level <chr>,
#> # variable_name <chr>, variable_level <chr>, estimate_name <chr>,
#> # estimate_type <chr>, estimate_value <chr>, additional_name <chr>,
#> # additional_level <chr>summariseIndication(x, cdm, strata = list("age_group", "sex", c("age_group", "sex"))) %>% glimpse()
#> Rows: 466
#> Columns: 15
#> $ cdm_name <chr> "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS …
#> $ result_type <chr> "summarised_indication", "summarised_indication", "su…
#> $ package_name <chr> "DrugUtilisation", "DrugUtilisation", "DrugUtilisatio…
#> $ package_version <chr> "0.5.0", "0.5.0", "0.5.0", "0.5.0", "0.5.0", "0.5.0",…
#> $ group_name <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_…
#> $ group_level <chr> "acetaminophen", "acetaminophen", "acetaminophen", "a…
#> $ strata_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ strata_level <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name <chr> "number subjects", "number records", "Indication on i…
#> $ variable_level <chr> NA, NA, "Headache", "Headache", "Influenza", "Influen…
#> $ estimate_name <chr> "count", "count", "count", "percentage", "count", "pe…
#> $ estimate_type <chr> "numeric", "numeric", "numeric", "percentage", "numer…
#> $ estimate_value <chr> "35", "35", "0", "0", "0", "0", "35", "100", "0", "0"…
#> $ additional_name <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ additional_level <chr> "overall", "overall", "overall", "overall", "overall"…We can compute daily dose for a certain ingredient from a subset of drug_exposure or the whole drug exposure (can be very computationally expensive).
Currently you can evaluate the coverage of daily dose for a conceptList or overall using dailyDoseCoverage. You can restrict to a certain ingredient, otherwise the result will be stratified by ingredient.
You can add columns related to the drug use using addDrugUse. You always have to provide a reference ingredient.
#x <- x %>%
# addDrugUse(
# cdm = cdm,
# ingredientConceptId = 1125315,
# initialDailyDose = TRUE,
# numberExposures = TRUE,
# duration = TRUE,
# cumulativeDose = TRUE,
# numberEras = TRUE
# )You can summarise the drug use using summariseDrugUse function
You can summarise the patient characteristics with summariseCharacteristics function:
summariseCharacteristics(
x, cdm, ageGroup = list(c(0, 24), c(25, 49), c(50, 74), c(75, 150)),
tableIntersect = list(
"Visits" = list(
tableName = "visit_occurrence", value = "count", window = c(-365, 0)
)
),
cohortIntersect = list(
"Indications" = list(
targetCohortTable = "indications_cohort", value = "flag",
window = c(-365, 0)
)
)
)
#> Warning: The `cdm` argument of `summariseCharacteristics()` is deprecated as of
#> PatientProfiles 0.6.0.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.
#> ℹ adding demographics columns
#> ℹ adding table intersect columns for table: visit_occurrence
#> ℹ adding cohort intersect columns for table: indications_cohort
#> ℹ summarising data
#> ✔ summariseCharacteristics finished!
#> # A tibble: 66 × 15
#> cdm_name result_type package_name package_version group_name group_level
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 DUS MOCK summarised_char… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 2 DUS MOCK summarised_char… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 3 DUS MOCK summarised_char… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 4 DUS MOCK summarised_char… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 5 DUS MOCK summarised_char… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 6 DUS MOCK summarised_char… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 7 DUS MOCK summarised_char… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 8 DUS MOCK summarised_char… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 9 DUS MOCK summarised_char… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 10 DUS MOCK summarised_char… PatientProf… 0.6.0 cohort_na… acetaminop…
#> # ℹ 56 more rows
#> # ℹ 9 more variables: strata_name <chr>, strata_level <chr>,
#> # variable_name <chr>, variable_level <chr>, estimate_name <chr>,
#> # estimate_type <chr>, estimate_value <chr>, additional_name <chr>,
#> # additional_level <chr>You can summarise the patient characteristics with summariseLargeScaleCharacteristics function:
summariseLargeScaleCharacteristics(
cohort = x,
window = list(c(-Inf, Inf)),
eventInWindow = "condition_occurrence",
episodeInWindow = "drug_exposure"
)
#> # A tibble: 26 × 15
#> cdm_name result_type package_name package_version group_name group_level
#> <chr> <chr> <chr> <chr> <chr> <chr>
#> 1 DUS MOCK summarised_larg… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 2 DUS MOCK summarised_larg… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 3 DUS MOCK summarised_larg… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 4 DUS MOCK summarised_larg… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 5 DUS MOCK summarised_larg… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 6 DUS MOCK summarised_larg… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 7 DUS MOCK summarised_larg… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 8 DUS MOCK summarised_larg… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 9 DUS MOCK summarised_larg… PatientProf… 0.6.0 cohort_na… acetaminop…
#> 10 DUS MOCK summarised_larg… PatientProf… 0.6.0 cohort_na… acetaminop…
#> # ℹ 16 more rows
#> # ℹ 9 more variables: strata_name <chr>, strata_level <chr>,
#> # variable_name <chr>, variable_level <chr>, estimate_name <chr>,
#> # estimate_type <chr>, estimate_value <chr>, additional_name <chr>,
#> # additional_level <chr>