In this vignette, we demonstrate how to use the
addIndications()
function to establish a binary indicator
between the drug utilisation cohort and another concept-based
cohort.
The DrugUtilisation package is designed to work with data in the OMOP CDM format, so our first step is to create a reference to the data using the DBI and CDMConnector packages. The connection to a Postgres database would look like:
library(DrugUtilisation)
library(DBI)
library(duckdb)
library(CDMConnector)
library(CodelistGenerator)
library(dplyr)
library(PatientProfiles)
con <- DBI::dbConnect(duckdb::duckdb(), eunomia_dir())
cdm <- CDMConnector::cdm_from_con(
con = con,
cdm_schema = "main",
write_schema = "main"
)
We will use Acetaminophen as our example drug to construct our drug utilisation cohort. To begin, we’ll employ the CodelistGenerator package to generate a concept list associated with Acetaminophen.
conceptList <- CodelistGenerator::getDrugIngredientCodes(cdm, "acetaminophen")
conceptList
#> $acetaminophen
#> [1] 1125315 1127078 1127433 40229134 40231925 40162522 19133768
Next, we can create a drug utilisation cohort by using the
conceptList
with the
generateDrugUtilisationCohortSet()
function. For a better
understanding of the arguments and functionalities of
generateDrugUtilisationCohortSet()
, please refer to the
Use DrugUtilisation to create a cohort vignette.
cdm <- generateDrugUtilisationCohortSet(
cdm = cdm,
name = "acetaminophen_users",
conceptSet = conceptList,
limit = "All",
gapEra = 30,
priorUseWashout = 0
)
Next we going to create our indications cohort to indicate patients
with sinusitis and bronchitis. This can be done by using
generateConceptCohortSet()
.
indications <-
list(
sinusitis = c(257012, 4294548, 40481087),
bronchitis = c(260139, 258780)
)
cdm <-
generateConceptCohortSet(cdm, name = "indications_cohort", indications)
cohortCount(cdm[["indications_cohort"]]) %>%
left_join(
settings(cdm[["indications_cohort"]]) %>%
select(cohort_definition_id, cohort_name),
by = "cohort_definition_id"
)
#> # A tibble: 2 × 4
#> cohort_definition_id number_records number_subjects cohort_name
#> <int> <int> <int> <chr>
#> 1 1 2688 2688 sinusitis
#> 2 2 2546 2546 bronchitis
Then to add indication to the drug utilisation cohort we can simple
use the addIndication()
function. To do that, we only need
the drug utilisation cohort and the indicationCohortName
.
However, other arguments can be specified using the additional
parameters. An example is provided below.
cdm[["acetaminophen_users"]] %>%
addIndication(
cdm = cdm,
indicationCohortName = "indications_cohort",
indicationGap = c(0, 30, 365),
unknownIndicationTable = c("condition_occurrence")
)
#> # Source: table<og_044_1717442351> [?? x 16]
#> # Database: DuckDB v0.10.0 [martics@Windows 10 x64:R 4.2.3/C:\Users\martics\AppData\Local\Temp\RtmpQPoPtg\file4838333c2bd.duckdb]
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> <int> <int> <date> <date>
#> 1 1 35 2014-07-26 2014-08-02
#> 2 1 96 1942-02-26 1942-02-26
#> 3 1 96 1962-05-22 1962-05-29
#> 4 1 189 1961-11-05 1961-11-19
#> 5 1 457 1998-07-04 1998-08-10
#> 6 1 512 1960-02-05 1960-02-19
#> 7 1 710 1961-10-15 1961-10-29
#> 8 1 806 1967-03-06 1967-03-20
#> 9 1 859 1992-11-11 1992-11-18
#> 10 1 865 1974-03-31 1974-04-14
#> # ℹ more rows
#> # ℹ 12 more variables: indication_gap_0_bronchitis <dbl>,
#> # indication_gap_0_sinusitis <dbl>, indication_gap_0_none <dbl>,
#> # indication_gap_0_unknown <dbl>, indication_gap_30_bronchitis <dbl>,
#> # indication_gap_30_sinusitis <dbl>, indication_gap_30_none <dbl>,
#> # indication_gap_30_unknown <dbl>, indication_gap_365_bronchitis <dbl>,
#> # indication_gap_365_sinusitis <dbl>, indication_gap_365_none <dbl>, …
Use indicationGap
to specify the indication gaps, which
are defined as the gap between the event and the indication.
Additionally, you can use unknownIndicationTable
to specify
the tables to look for unknown indication.
To create a summary table of the indications cohort, you can use the
summariseIndication()
function.
cdm[["acetaminophen_users"]] %>%
addIndication(
cdm = cdm,
indicationCohortName = "indications_cohort",
indicationGap = c(0, 30, 365),
unknownIndicationTable = c("condition_occurrence")
) %>%
summariseIndication(cdm) %>%
select("variable_name", "estimate_name", "estimate_value")
#> # A tibble: 26 × 3
#> variable_name estimate_name estimate_value
#> <chr> <chr> <chr>
#> 1 number records count 13860
#> 2 number subjects count 2679
#> 3 Indication on index date count 2518
#> 4 Indication on index date percentage 18.1673881673882
#> 5 Indication on index date count <NA>
#> 6 Indication on index date percentage <NA>
#> 7 Indication on index date count 163
#> 8 Indication on index date percentage 1.17604617604618
#> 9 Indication on index date count 11178
#> 10 Indication on index date percentage 80.6493506493507
#> # ℹ 16 more rows
You can also summarise the indications by using the
strata
argument in the summariseIndication()
function. In the example below, it is summarized by
ageGroup
and sex
.
cdm[["acetaminophen_users"]] %>%
addDemographics(ageGroup = list(c(0, 19), c(20, 150))) %>%
addIndication(
cdm = cdm,
indicationCohortName = "indications_cohort",
indicationGap = c(0),
unknownIndicationTable = c("condition_occurrence")
) %>%
summariseIndication(
cdm,
strata = list("age" = "age_group", "sex" = "sex")) %>%
select("variable_name", "estimate_name", "estimate_value","strata_name")
#> # A tibble: 50 × 4
#> variable_name estimate_name estimate_value strata_name
#> <chr> <chr> <chr> <chr>
#> 1 number records count 13860 overall
#> 2 number subjects count 2679 overall
#> 3 Indication on index date count 2518 overall
#> 4 Indication on index date percentage 18.1673881673882 overall
#> 5 Indication on index date count <NA> overall
#> 6 Indication on index date percentage <NA> overall
#> 7 Indication on index date count 163 overall
#> 8 Indication on index date percentage 1.17604617604618 overall
#> 9 Indication on index date count 11178 overall
#> 10 Indication on index date percentage 80.6493506493507 overall
#> # ℹ 40 more rows