#> Warning: The `groupNameCol` argument of `gtTable()` is deprecated as of visOmopResults
#> 0.3.0.
#> ℹ Please use the `groupColumn` argument instead.
#> This warning is displayed once every 8 hours.
#> Call `lifecycle::last_lifecycle_warnings()` to see where this warning was
#> generated.| amount | amount_unit | numerator | numerator_unit | denominator | denominator_unit | formula |
|---|---|---|---|---|---|---|
| - | - | number | microgram | number | hour | time based with denominator: if (denominator>24) {numerator * 24 / denominator} else {numerator} |
| - | - | number | milligram | number | hour | time based with denominator: if (denominator>24) {numerator * 24 / denominator} else {numerator} |
| - | - | number | unit | number | hour | time based with denominator: if (denominator>24) {numerator * 24 / denominator} else {numerator} |
| - | - | number | microgram | - | hour | time based no denominator: 24 * numerator |
| - | - | number | milligram | - | hour | time based no denominator: 24 * numerator |
| number | international unit | - | - | - | - | fixed amount formulation: quantity * amount / days exposed |
| number | microgram | - | - | - | - | fixed amount formulation: quantity * amount / days exposed |
| number | milliequivalent | - | - | - | - | fixed amount formulation: quantity * amount / days exposed |
| number | milligram | - | - | - | - | fixed amount formulation: quantity * amount / days exposed |
| number | milliliter | - | - | - | - | fixed amount formulation: quantity * amount / days exposed |
| number | unit | - | - | - | - | fixed amount formulation: quantity * amount / days exposed |
| - | - | number | international unit | number | milligram | concentration formulation: quantity * numerator / days exposed |
| - | - | number | international unit | number | milliliter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milliequivalent | number | milliliter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milligram | number | Actuation | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milligram | number | liter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milligram | number | milligram | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milligram | number | milliliter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milligram | number | square centimeter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milliliter | number | milligram | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milliliter | number | milliliter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | unit | number | Actuation | concentration formulation: quantity * numerator / days exposed |
| - | - | number | unit | number | milligram | concentration formulation: quantity * numerator / days exposed |
| - | - | number | unit | number | milliliter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | unit | number | square centimeter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | international unit | - | milligram | concentration formulation: quantity * numerator / days exposed |
| - | - | number | international unit | - | milliliter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | mega-international unit | - | milliliter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milliequivalent | - | milligram | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milliequivalent | - | milliliter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milligram | - | Actuation | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milligram | - | liter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milligram | - | milligram | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milligram | - | milliliter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milligram | - | square centimeter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milliliter | - | milligram | concentration formulation: quantity * numerator / days exposed |
| - | - | number | milliliter | - | milliliter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | unit | - | Actuation | concentration formulation: quantity * numerator / days exposed |
| - | - | number | unit | - | milligram | concentration formulation: quantity * numerator / days exposed |
| - | - | number | unit | - | milliliter | concentration formulation: quantity * numerator / days exposed |
| - | - | number | unit | - | square centimeter | concentration formulation: quantity * numerator / days exposed |
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)
conceptList <- getDrugIngredientCodes(cdm, "acetaminophen")
conceptList
#> $acetaminophen
#> [1] 1125315 1125360 2905077 43135274To 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)
#>
#> 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(
indicationCohortName = "indications_cohort", indicationGap = c(0, 30, 365),
unknownIndicationTable = c("condition_occurrence")
)
glimpse(x)
#> Rows: ??
#> Columns: 16
#> Database: DuckDB v0.10.0 [martics@Windows 10 x64:R 4.2.3/:memory:]
#> $ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ subject_id <int> 42, 87, 95, 35, 54, 62, 96, 26, 91, 8, 46…
#> $ cohort_start_date <date> 2002-09-15, 2020-10-02, 2001-06-13, 2005…
#> $ cohort_end_date <date> 2007-12-19, 2021-01-02, 2010-01-28, 2006…
#> $ 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, 1, 1, 1,…
#> $ indication_gap_30_unknown <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_headache <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_365_none <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,…
#> $ indication_gap_365_unknown <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,…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 v0.10.0 [martics@Windows 10 x64:R 4.2.3/:memory:]
#> $ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ subject_id <int> 42, 87, 95, 35, 54, 62, 96, 26, 91, 8, 46…
#> $ cohort_start_date <date> 2002-09-15, 2020-10-02, 2001-06-13, 2005…
#> $ cohort_end_date <date> 2007-12-19, 2021-01-02, 2010-01-28, 2006…
#> $ 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, 1, 1, 1,…
#> $ indication_gap_30_unknown <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_headache <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_365_none <dbl> 1, 1, 1, 1, 1, 1, 1, 0, 1, 1, 1, 1, 1, 0,…
#> $ indication_gap_365_unknown <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1,…
#> $ 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)
#> ℹ The following estimates will be computed:
#> • indication_gap_0_headache: count, percentage
#> • indication_gap_0_influenza: count, percentage
#> • indication_gap_0_none: count, percentage
#> • indication_gap_0_unknown: count, percentage
#> • indication_gap_30_headache: count, percentage
#> • indication_gap_30_influenza: count, percentage
#> • indication_gap_30_none: count, percentage
#> • indication_gap_30_unknown: count, percentage
#> • indication_gap_365_influenza: count, percentage
#> • indication_gap_365_headache: count, percentage
#> • indication_gap_365_none: count, percentage
#> • indication_gap_365_unknown: count, percentage
#> • indication_gap_0: count, percentage
#> • indication_gap_30: count, percentage
#> • indication_gap_365: count, percentage
#> → Start summary of data, at 2024-05-14 00:54:02
#>
#> ✔ Summary finished, at 2024-05-14 00:54:02
#> # A tibble: 42 × 13
#> result_id cdm_name group_name group_level strata_name strata_level
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1 DUS MOCK cohort_name acetaminophen overall overall
#> 2 1 DUS MOCK cohort_name acetaminophen overall overall
#> 3 1 DUS MOCK cohort_name acetaminophen overall overall
#> 4 1 DUS MOCK cohort_name acetaminophen overall overall
#> 5 1 DUS MOCK cohort_name acetaminophen overall overall
#> 6 1 DUS MOCK cohort_name acetaminophen overall overall
#> 7 1 DUS MOCK cohort_name acetaminophen overall overall
#> 8 1 DUS MOCK cohort_name acetaminophen overall overall
#> 9 1 DUS MOCK cohort_name acetaminophen overall overall
#> 10 1 DUS MOCK cohort_name acetaminophen overall overall
#> # ℹ 32 more rows
#> # ℹ 7 more variables: 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()
#> ℹ The following estimates will be computed:
#> • indication_gap_0_headache: count, percentage
#> • indication_gap_0_influenza: count, percentage
#> • indication_gap_0_none: count, percentage
#> • indication_gap_0_unknown: count, percentage
#> • indication_gap_30_headache: count, percentage
#> • indication_gap_30_influenza: count, percentage
#> • indication_gap_30_none: count, percentage
#> • indication_gap_30_unknown: count, percentage
#> • indication_gap_365_influenza: count, percentage
#> • indication_gap_365_headache: count, percentage
#> • indication_gap_365_none: count, percentage
#> • indication_gap_365_unknown: count, percentage
#> • indication_gap_0: count, percentage
#> • indication_gap_30: count, percentage
#> • indication_gap_365: count, percentage
#> → Start summary of data, at 2024-05-14 00:54:02
#>
#> ✔ Summary finished, at 2024-05-14 00:54:02
#> Rows: 42
#> Columns: 13
#> $ result_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ cdm_name <chr> "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS …
#> $ 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 records", "number subjects", "Indication on i…
#> $ variable_level <chr> NA, NA, "Headache", "Headache", "Influenza", "Influen…
#> $ estimate_name <chr> "count", "count", "count", "percentage", "count", "pe…
#> $ estimate_type <chr> "integer", "integer", "integer", "percentage", "integ…
#> $ 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(ageGroup = list(c(0, 19), c(20, 39), c(40, 59), c(60, 79), c(80, 150))) %>%
addSex()
summariseIndication(x, cdm, strata = list("age_group", "sex", c("age_group", "sex")))
#> ℹ The following estimates will be computed:
#> • indication_gap_0_headache: count, percentage
#> • indication_gap_0_influenza: count, percentage
#> • indication_gap_0_none: count, percentage
#> • indication_gap_0_unknown: count, percentage
#> • indication_gap_30_headache: count, percentage
#> • indication_gap_30_influenza: count, percentage
#> • indication_gap_30_none: count, percentage
#> • indication_gap_30_unknown: count, percentage
#> • indication_gap_365_influenza: count, percentage
#> • indication_gap_365_headache: count, percentage
#> • indication_gap_365_none: count, percentage
#> • indication_gap_365_unknown: count, percentage
#> • indication_gap_0: count, percentage
#> • indication_gap_30: count, percentage
#> • indication_gap_365: count, percentage
#> → Start summary of data, at 2024-05-14 00:54:04
#>
#> ✔ Summary finished, at 2024-05-14 00:54:04
#> # A tibble: 430 × 13
#> result_id cdm_name group_name group_level strata_name strata_level
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1 DUS MOCK cohort_name acetaminophen overall overall
#> 2 1 DUS MOCK cohort_name acetaminophen overall overall
#> 3 1 DUS MOCK cohort_name acetaminophen overall overall
#> 4 1 DUS MOCK cohort_name acetaminophen overall overall
#> 5 1 DUS MOCK cohort_name acetaminophen overall overall
#> 6 1 DUS MOCK cohort_name acetaminophen overall overall
#> 7 1 DUS MOCK cohort_name acetaminophen overall overall
#> 8 1 DUS MOCK cohort_name acetaminophen overall overall
#> 9 1 DUS MOCK cohort_name acetaminophen overall overall
#> 10 1 DUS MOCK cohort_name acetaminophen overall overall
#> # ℹ 420 more rows
#> # ℹ 7 more variables: 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()
#> ℹ The following estimates will be computed:
#> • indication_gap_0_headache: count, percentage
#> • indication_gap_0_influenza: count, percentage
#> • indication_gap_0_none: count, percentage
#> • indication_gap_0_unknown: count, percentage
#> • indication_gap_30_headache: count, percentage
#> • indication_gap_30_influenza: count, percentage
#> • indication_gap_30_none: count, percentage
#> • indication_gap_30_unknown: count, percentage
#> • indication_gap_365_influenza: count, percentage
#> • indication_gap_365_headache: count, percentage
#> • indication_gap_365_none: count, percentage
#> • indication_gap_365_unknown: count, percentage
#> • indication_gap_0: count, percentage
#> • indication_gap_30: count, percentage
#> • indication_gap_365: count, percentage
#> → Start summary of data, at 2024-05-14 00:54:04
#>
#> ✔ Summary finished, at 2024-05-14 00:54:05
#> Rows: 430
#> Columns: 13
#> $ result_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ cdm_name <chr> "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS …
#> $ 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 records", "number subjects", "Indication on i…
#> $ variable_level <chr> NA, NA, "Headache", "Headache", "Influenza", "Influen…
#> $ estimate_name <chr> "count", "count", "count", "percentage", "count", "pe…
#> $ estimate_type <chr> "integer", "integer", "integer", "percentage", "integ…
#> $ 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).
cdm[["drug_exposure"]] %>%
addDailyDose(ingredientConceptId = 1125315) %>%
glimpse()
#> Rows: ??
#> Columns: 9
#> Database: DuckDB v0.10.0 [martics@Windows 10 x64:R 4.2.3/:memory:]
#> $ drug_exposure_id <int> 8, 12, 17, 23, 24, 27, 34, 35, 40, 49, 50, 59…
#> $ person_id <int> 3, 4, 7, 8, 9, 9, 12, 13, 14, 19, 19, 23, 25,…
#> $ drug_concept_id <dbl> 43135274, 1125360, 1125360, 2905077, 1125360,…
#> $ drug_exposure_start_date <date> 1996-09-13, 2004-12-26, 1969-09-09, 2019-09-…
#> $ drug_exposure_end_date <date> 2008-07-15, 2006-04-15, 1970-02-14, 2020-12-…
#> $ drug_type_concept_id <dbl> 38000177, 38000177, 38000177, 38000177, 38000…
#> $ quantity <dbl> 50, 100, 80, 10, 25, 35, 10, 90, 45, 80, 40, …
#> $ daily_dose <dbl> 4.625347, 105.042017, 251.572327, 202.105263,…
#> $ unit <chr> "milligram", "milligram", "milligram", "milli…Currently you can evaluate the coverage of daily dose for a conceptList or overall using dailyDoseCoverage. You should restrict to a certain ingredient.
dailyDoseCoverage(cdm = cdm, ingredient = 1125315)
#> ℹ The following estimates will be computed:
#> • daily_dose: count_missing, percentage_missing, mean, sd, min, q05, q25,
#> median, q75, q95, max
#> ! Table is collected to memory as not all requested estimates are supported on
#> the database side
#> → Start summary of data, at 2024-05-14 00:54:06
#>
#> ✔ Summary finished, at 2024-05-14 00:54:06
#> # A tibble: 84 × 13
#> result_id cdm_name group_name group_level strata_name strata_level
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1 DUS MOCK ingredient_name acetaminophen overall overall
#> 2 1 DUS MOCK ingredient_name acetaminophen overall overall
#> 3 1 DUS MOCK ingredient_name acetaminophen overall overall
#> 4 1 DUS MOCK ingredient_name acetaminophen overall overall
#> 5 1 DUS MOCK ingredient_name acetaminophen overall overall
#> 6 1 DUS MOCK ingredient_name acetaminophen overall overall
#> 7 1 DUS MOCK ingredient_name acetaminophen overall overall
#> 8 1 DUS MOCK ingredient_name acetaminophen overall overall
#> 9 1 DUS MOCK ingredient_name acetaminophen overall overall
#> 10 1 DUS MOCK ingredient_name acetaminophen overall overall
#> # ℹ 74 more rows
#> # ℹ 7 more variables: variable_name <chr>, variable_level <chr>,
#> # estimate_name <chr>, estimate_type <chr>, estimate_value <chr>,
#> # additional_name <chr>, additional_level <chr>You can add columns related to the drug use using addDrugUse. You always have to provide a reference ingredient.
x <- x %>%
addDrugUse(
ingredientConceptId = 1125315,
dose = TRUE,
quantity = TRUE,
duration = TRUE
)You can summarise the drug use using summariseDrugUse function
summariseDrugUse(x)
#> ! names of group will be ignored
#> ℹ The following estimates will be computed:
#> • number_exposures: min, q05, q25, median, q75, q95, max, mean, sd,
#> count_missing, percentage_missing
#> • duration: min, q05, q25, median, q75, q95, max, mean, sd, count_missing,
#> percentage_missing
#> • cumulative_quantity: min, q05, q25, median, q75, q95, max, mean, sd,
#> count_missing, percentage_missing
#> • number_eras: min, q05, q25, median, q75, q95, max, mean, sd, count_missing,
#> percentage_missing
#> • initial_quantity: min, q05, q25, median, q75, q95, max, mean, sd,
#> count_missing, percentage_missing
#> • impute_daily_dose_percentage: min, q05, q25, median, q75, q95, max, mean, sd,
#> count_missing, percentage_missing
#> • impute_duration_percentage: min, q05, q25, median, q75, q95, max, mean, sd,
#> count_missing, percentage_missing
#> • initial_daily_dose_milligram: min, q05, q25, median, q75, q95, max, mean, sd,
#> count_missing, percentage_missing
#> • cumulative_dose_milligram: min, q05, q25, median, q75, q95, max, mean, sd,
#> count_missing, percentage_missing
#> ! Table is collected to memory as not all requested estimates are supported on
#> the database side
#> → Start summary of data, at 2024-05-14 00:54:13
#>
#> ✔ Summary finished, at 2024-05-14 00:54:13
#> # A tibble: 101 × 13
#> result_id cdm_name group_name group_level strata_name strata_level
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1 DUS MOCK cohort_name acetaminophen overall overall
#> 2 1 DUS MOCK cohort_name acetaminophen overall overall
#> 3 1 DUS MOCK cohort_name acetaminophen overall overall
#> 4 1 DUS MOCK cohort_name acetaminophen overall overall
#> 5 1 DUS MOCK cohort_name acetaminophen overall overall
#> 6 1 DUS MOCK cohort_name acetaminophen overall overall
#> 7 1 DUS MOCK cohort_name acetaminophen overall overall
#> 8 1 DUS MOCK cohort_name acetaminophen overall overall
#> 9 1 DUS MOCK cohort_name acetaminophen overall overall
#> 10 1 DUS MOCK cohort_name acetaminophen overall overall
#> # ℹ 91 more rows
#> # ℹ 7 more variables: 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 summariseCharacteristics function:
x |>
summariseCharacteristics(
ageGroup = list(c(0, 24), c(25, 49), c(50, 74), c(75, 150)),
tableIntersectCount = list(
"Visits" = list(
tableName = "visit_occurrence", window = c(-365, 0)
)
),
cohortIntersectFlag = list(
"Indications" = list(
targetCohortTable = "indications_cohort", window = c(-365, 0)
)
)
)
#> ℹ adding demographics columns
#> ℹ adding tableIntersectCount 1/1
#> ℹ adding cohortIntersectFlag 1/1
#> ℹ summarising data
#>
#> ✔ summariseCharacteristics finished!
#> # A tibble: 54 × 13
#> result_id cdm_name group_name group_level strata_name strata_level
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1 DUS MOCK cohort_name acetaminophen overall overall
#> 2 1 DUS MOCK cohort_name acetaminophen overall overall
#> 3 1 DUS MOCK cohort_name acetaminophen overall overall
#> 4 1 DUS MOCK cohort_name acetaminophen overall overall
#> 5 1 DUS MOCK cohort_name acetaminophen overall overall
#> 6 1 DUS MOCK cohort_name acetaminophen overall overall
#> 7 1 DUS MOCK cohort_name acetaminophen overall overall
#> 8 1 DUS MOCK cohort_name acetaminophen overall overall
#> 9 1 DUS MOCK cohort_name acetaminophen overall overall
#> 10 1 DUS MOCK cohort_name acetaminophen overall overall
#> # ℹ 44 more rows
#> # ℹ 7 more variables: 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:
x |>
summariseLargeScaleCharacteristics(
window = list(c(-Inf, Inf)),
eventInWindow = "condition_occurrence",
episodeInWindow = "drug_exposure"
)
#> ℹ Summarising large scale characteristics
#> - getting characteristics from table condition_occurrence (1 of 2) - getting
#> characteristics from table drug_exposure (2 of 2)
#> # A tibble: 26 × 13
#> result_id cdm_name group_name group_level strata_name strata_level
#> <int> <chr> <chr> <chr> <chr> <chr>
#> 1 1 DUS MOCK cohort_name acetaminophen overall overall
#> 2 1 DUS MOCK cohort_name acetaminophen overall overall
#> 3 1 DUS MOCK cohort_name acetaminophen overall overall
#> 4 1 DUS MOCK cohort_name acetaminophen overall overall
#> 5 1 DUS MOCK cohort_name acetaminophen overall overall
#> 6 1 DUS MOCK cohort_name acetaminophen overall overall
#> 7 2 DUS MOCK cohort_name acetaminophen overall overall
#> 8 2 DUS MOCK cohort_name acetaminophen overall overall
#> 9 2 DUS MOCK cohort_name acetaminophen overall overall
#> 10 2 DUS MOCK cohort_name acetaminophen overall overall
#> # ℹ 16 more rows
#> # ℹ 7 more variables: variable_name <chr>, variable_level <chr>,
#> # estimate_name <chr>, estimate_type <chr>, estimate_value <chr>,
#> # additional_name <chr>, additional_level <chr>