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#> 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>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.
Health stats visible at Monitor.