Add indications to Drug Utilisation Cohort
Martí Català, Mike Du, Yuchen Guo, Kim López-Güell, and Edward Burn
2024-02-10
addIndications-example.RmdIntroduction
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"
)Create a drug utilisation cohort
We will use Acetaminophen as our example drug to construct our drug utilisation cohort. To begin, we’ll employ the “codelistGenerator” to generate a concept list associated with Acetaminophen.
# using CodelistGenerator
conceptList <- CodelistGenerator::getDrugIngredientCodes(cdm, "acetaminophen")
conceptList
#> $acetaminophen
#> [1] 1125315 1127078 1127433 40229134 40231925 40162522 19133768Next, 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
)Create a indication cohort
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(
cohortSet(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 bronchitisAdd indications
Then to add indication to the drug Utilisation cohort we can simple use the addIndication function. An example is provided below. The function have argument to specify the indication gaps. The indication gaps is defined as the gap between the event and the indication. It also allow user to specify the tables to look for unknown indication.
cdm[["acetaminophen_users"]] %>%
addIndication(
cdm = cdm,
indicationCohortName = "indications_cohort",
indicationGap = c(0, 30, 365),
unknownIndicationTable = c("condition_occurrence")
)
#> # Source: table<dbplyr_014> [?? x 16]
#> # Database: DuckDB v0.9.2 [unknown@Linux 6.2.0-1019-azure:R 4.3.2//tmp/Rtmpyqx0LW/file1e4070f06ca4.duckdb]
#> cohort_definition_id subject_id cohort_start_date cohort_end_date
#> <int> <int> <date> <date>
#> 1 1 7 1974-08-12 1974-09-11
#> 2 1 57 1982-01-01 1982-01-08
#> 3 1 80 1937-07-09 1937-07-16
#> 4 1 86 1952-07-05 1952-07-19
#> 5 1 86 1977-12-14 1977-12-28
#> 6 1 144 1980-10-21 1980-11-04
#> 7 1 144 1984-01-21 1984-01-28
#> 8 1 187 1980-01-09 1980-01-30
#> 9 1 187 1999-04-21 1999-05-26
#> 10 1 187 2001-04-15 2001-05-13
#> # 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_sinusitis <dbl>,
#> # indication_gap_30_bronchitis <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>, …Summarise Indications
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 subjects count 2679
#> 2 number records count 13860
#> 3 Indication on index date count 2518
#> 4 Indication on index date percentage 18.1673881673882
#> 5 Indication on index date count <5
#> 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 rowsYou can also summarize 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 subjects count 2679 overall
#> 2 number records count 13860 overall
#> 3 Indication on index date count 2518 overall
#> 4 Indication on index date percentage 18.1673881673882 overall
#> 5 Indication on index date count <5 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