DrugUtilisation

WARNING: This package is under development.

• addDailyDose function works for the following patterns in the drug_strength table:

Package overview

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

Example

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:

library(DrugUtilisation)
cdm <- mockDrugUtilisation(numberIndividual = 100)

Create a cohort of drug use

To create a cohort we will need a conceptList, this can be read from json files:

conceptList <- readConceptList(here::here("Concepts"), cdm)

Or we can build our own list using other packages (e.g. CodelistGenerator)

library(CodelistGenerator)
conceptList <- getDrugIngredientCodes(cdm, "acetaminophen")
conceptList
#> $`Ingredient: acetaminophen (1125315)`
#> [1]  1125315  1125360  2905077 43135274

To 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)
)

Cohort attributes

The generated cohort will have the GeneratedCohortSet as seen in CDMConnector

class(cdm[["dus_cohort"]])
#> [1] "GeneratedCohortSet"    "tbl_duckdb_connection" "tbl_dbi"              
#> [4] "tbl_sql"               "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
cohortSet(cdm[["dus_cohort"]]) %>% glimpse()
#> Rows: 1
#> Columns: 11
#> $ cohort_definition_id    <int> 1
#> $ cohort_name             <chr> "Ingredient: acetaminophen (1125315)"
#> $ limit                   <chr> "first"
#> $ prior_observation       <chr> "365"
#> $ gap_era                 <chr> "30"
#> $ prior_use_washout       <chr> "0"
#> $ cohort_date_range_start <chr> NA
#> $ cohort_date_range_end   <chr> NA
#> $ impute_duration         <chr> "none"
#> $ duration_range_min      <chr> "0"
#> $ duration_range_max      <chr> "Inf"

Cohort count:

cohortCount(cdm[["dus_cohort"]])
#> # A tibble: 1 × 3
#>   cohort_definition_id number_records number_subjects
#>                  <int>          <dbl>           <dbl>
#> 1                    1             35              35

Cohort attrition:

cohortAttrition(cdm[["dus_cohort"]]) %>% glimpse()
#> Rows: 8
#> Columns: 7
#> $ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1
#> $ number_records       <dbl> 90, 90, 70, 70, 41, 41, 41, 35
#> $ number_subjects      <dbl> 62, 62, 62, 62, 35, 35, 35, 35
#> $ reason_id            <dbl> 1, 2, 3, 4, 5, 6, 7, 8
#> $ reason               <chr> "Qualifying initial records", "Duration imputatio…
#> $ excluded_records     <dbl> 0, 0, 20, 0, 29, 0, 0, 6
#> $ excluded_subjects    <dbl> 0, 0, 0, 0, 27, 0, 0, 0

Indication

Indications 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>          <dbl>           <dbl>
#> 1                    2             46              46
#> 2                    1             52              52

Then 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")
  )
glimpse(x)
#> Rows: ??
#> Columns: 16
#> Database: DuckDB 0.9.0 [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                   <dbl> 7, 48, 57, 77, 3, 58, 73, 23, 40, 45, 51,…
#> $ cohort_start_date            <date> 1969-09-09, 1972-04-14, 2020-04-17, 1967…
#> $ cohort_end_date              <date> 1970-02-14, 1972-12-18, 2020-11-19, 1967…
#> $ indication_gap_0_influenza   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_0_headache    <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_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, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ indication_gap_365_headache  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
#> $ indication_gap_365_influenza <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_365_none      <dbl> 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 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_unknown    <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_365_unknown   <dbl> 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 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 0.9.0 [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                   <dbl> 7, 48, 57, 77, 3, 58, 73, 23, 40, 45, 51,…
#> $ cohort_start_date            <date> 1969-09-09, 1972-04-14, 2020-04-17, 1967…
#> $ cohort_end_date              <date> 1970-02-14, 1972-12-18, 2020-11-19, 1967…
#> $ indication_gap_0_influenza   <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_0_headache    <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_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, 0, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
#> $ indication_gap_365_headache  <dbl> 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 1, 0, 0, 0,…
#> $ indication_gap_365_influenza <dbl> 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_365_none      <dbl> 1, 1, 0, 0, 1, 1, 0, 1, 1, 1, 0, 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_unknown    <dbl> 0, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_365_unknown   <dbl> 0, 0, 0, 1, 0, 0, 1, 0, 0, 0, 0, 0, 0, 0,…
#> $ indication_gap_0             <chr> "None", "None", "None", "None", "None", "…
#> $ indication_gap_30            <chr> "None", "None", "None", "Unknown", "None"…
#> $ indication_gap_365           <chr> "None", "None", "Influenza", "Unknown", "…

table(x %>% pull("indication_gap_365"))
#> 
#>  Headache Influenza      None   Unknown 
#>         2         3        27         3

Summarise the indication

We can summarise the indication results using the summariseIndication function:

summariseIndication(x, cdm)
#> # A tibble: 40 × 11
#>    group_name  group_level      strata_name strata_level variable variable_level
#>    <chr>       <chr>            <chr>       <chr>        <chr>    <chr>         
#>  1 cohort_name Ingredient: ace… Overall     Overall      number … <NA>          
#>  2 cohort_name Ingredient: ace… Overall     Overall      number … <NA>          
#>  3 cohort_name Ingredient: ace… Overall     Overall      Indicat… Influenza     
#>  4 cohort_name Ingredient: ace… Overall     Overall      Indicat… Influenza     
#>  5 cohort_name Ingredient: ace… Overall     Overall      Indicat… Headache      
#>  6 cohort_name Ingredient: ace… Overall     Overall      Indicat… Headache      
#>  7 cohort_name Ingredient: ace… Overall     Overall      Indicat… None          
#>  8 cohort_name Ingredient: ace… Overall     Overall      Indicat… None          
#>  9 cohort_name Ingredient: ace… Overall     Overall      Indicat… Headache      
#> 10 cohort_name Ingredient: ace… Overall     Overall      Indicat… Headache      
#> # ℹ 30 more rows
#> # ℹ 5 more variables: variable_type <chr>, estimate_type <chr>, estimate <chr>,
#> #   cdm_name <chr>, result_type <chr>

summariseIndication(x, cdm) %>% glimpse()
#> Rows: 40
#> Columns: 11
#> $ group_name     <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_na…
#> $ group_level    <chr> "Ingredient: acetaminophen (1125315)", "Ingredient: ace…
#> $ strata_name    <chr> "Overall", "Overall", "Overall", "Overall", "Overall", …
#> $ strata_level   <chr> "Overall", "Overall", "Overall", "Overall", "Overall", …
#> $ variable       <chr> "number subjects", "number records", "Indication on ind…
#> $ variable_level <chr> NA, NA, "Influenza", "Influenza", "Headache", "Headache…
#> $ variable_type  <chr> NA, NA, "binary", "binary", "binary", "binary", "binary…
#> $ estimate_type  <chr> "count", "count", "count", "percentage", "count", "perc…
#> $ estimate       <chr> "35", "35", "0", "0", "0", "0", "35", "100", "0", "0", …
#> $ cdm_name       <chr> "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS MO…
#> $ result_type    <chr> "Summary indication", "Summary indication", "Summary in…

Add strata

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)
summariseIndication(x, cdm, strata = list("age_group", "sex", c("age_group", "sex")))
#> # A tibble: 440 × 11
#>    group_name  group_level      strata_name strata_level variable variable_level
#>    <chr>       <chr>            <chr>       <chr>        <chr>    <chr>         
#>  1 cohort_name Ingredient: ace… Overall     Overall      number … <NA>          
#>  2 cohort_name Ingredient: ace… Overall     Overall      number … <NA>          
#>  3 cohort_name Ingredient: ace… Overall     Overall      Indicat… Influenza     
#>  4 cohort_name Ingredient: ace… Overall     Overall      Indicat… Influenza     
#>  5 cohort_name Ingredient: ace… Overall     Overall      Indicat… Headache      
#>  6 cohort_name Ingredient: ace… Overall     Overall      Indicat… Headache      
#>  7 cohort_name Ingredient: ace… Overall     Overall      Indicat… None          
#>  8 cohort_name Ingredient: ace… Overall     Overall      Indicat… None          
#>  9 cohort_name Ingredient: ace… Overall     Overall      Indicat… Headache      
#> 10 cohort_name Ingredient: ace… Overall     Overall      Indicat… Headache      
#> # ℹ 430 more rows
#> # ℹ 5 more variables: variable_type <chr>, estimate_type <chr>, estimate <chr>,
#> #   cdm_name <chr>, result_type <chr>

summariseIndication(x, cdm, strata = list("age_group", "sex", c("age_group", "sex"))) %>% glimpse()
#> Rows: 440
#> Columns: 11
#> $ group_name     <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_na…
#> $ group_level    <chr> "Ingredient: acetaminophen (1125315)", "Ingredient: ace…
#> $ strata_name    <chr> "Overall", "Overall", "Overall", "Overall", "Overall", …
#> $ strata_level   <chr> "Overall", "Overall", "Overall", "Overall", "Overall", …
#> $ variable       <chr> "number subjects", "number records", "Indication on ind…
#> $ variable_level <chr> NA, NA, "Influenza", "Influenza", "Headache", "Headache…
#> $ variable_type  <chr> NA, NA, "binary", "binary", "binary", "binary", "binary…
#> $ estimate_type  <chr> "count", "count", "count", "percentage", "count", "perc…
#> $ estimate       <chr> "35", "35", "0", "0", "0", "0", "35", "100", "0", "0", …
#> $ cdm_name       <chr> "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS MOCK", "DUS MO…
#> $ result_type    <chr> "Summary indication", "Summary indication", "Summary in…

Daily dose

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()

Coverage

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.

#dailyDoseCoverage(cdm = cdm, sample = NULL, ingredient = NULL, conceptList = NULL) 

DrugUse

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
#  )

Summarise the drug use

You can summarise the drug use using summariseDrugUse function

#summariseDrugUse(x, cdm)

Summarise patient characteristics

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)
    )
  )
)
#> # A tibble: 46 × 11
#>    cdm_name result_type group_name group_level strata_name strata_level variable
#>    <chr>    <chr>       <chr>      <chr>       <chr>       <chr>        <chr>   
#>  1 DUS MOCK Summary ch… cohort_na… Ingredient… Overall     Overall      Number …
#>  2 DUS MOCK Summary ch… cohort_na… Ingredient… Overall     Overall      Number …
#>  3 DUS MOCK Summary ch… cohort_na… Ingredient… Overall     Overall      Cohort …
#>  4 DUS MOCK Summary ch… cohort_na… Ingredient… Overall     Overall      Cohort …
#>  5 DUS MOCK Summary ch… cohort_na… Ingredient… Overall     Overall      Cohort …
#>  6 DUS MOCK Summary ch… cohort_na… Ingredient… Overall     Overall      Cohort …
#>  7 DUS MOCK Summary ch… cohort_na… Ingredient… Overall     Overall      Cohort …
#>  8 DUS MOCK Summary ch… cohort_na… Ingredient… Overall     Overall      Cohort …
#>  9 DUS MOCK Summary ch… cohort_na… Ingredient… Overall     Overall      Cohort …
#> 10 DUS MOCK Summary ch… cohort_na… Ingredient… Overall     Overall      Cohort …
#> # ℹ 36 more rows
#> # ℹ 4 more variables: variable_level <chr>, variable_type <chr>,
#> #   estimate_type <chr>, estimate <chr>

Summarise patients large scale characteristics

You can summarise the patient characteristics with summariseLargeScaleCharacteristics function:

summariseLargeScaleCharacteristics(
  cohort = x, 
  window = list(c(-Inf, Inf)), 
  eventInWindow = "condition_occurrence", 
  episodeInWindow = "drug_exposure"
)
#> Warning in checkNewName(ageName, x): age already exists in x, it was renamed to
#> age_1
#> Warning in checkNewName(sexName, x): sex already exists in x, it was renamed to
#> sex_1
#> # A tibble: 26 × 14
#>    result_type          cdm_name group_name group_level strata_name strata_level
#>    <chr>                <chr>    <chr>      <chr>       <chr>       <chr>       
#>  1 Summarised Large Sc… DUS MOCK Cohort na… Ingredient… Overall     Overall     
#>  2 Summarised Large Sc… DUS MOCK Cohort na… Ingredient… Overall     Overall     
#>  3 Summarised Large Sc… DUS MOCK Cohort na… Ingredient… Overall     Overall     
#>  4 Summarised Large Sc… DUS MOCK Cohort na… Ingredient… Overall     Overall     
#>  5 Summarised Large Sc… DUS MOCK Cohort na… Ingredient… Overall     Overall     
#>  6 Summarised Large Sc… DUS MOCK Cohort na… Ingredient… Overall     Overall     
#>  7 Summarised Large Sc… DUS MOCK Cohort na… Ingredient… Overall     Overall     
#>  8 Summarised Large Sc… DUS MOCK Cohort na… Ingredient… Overall     Overall     
#>  9 Summarised Large Sc… DUS MOCK Cohort na… Ingredient… Overall     Overall     
#> 10 Summarised Large Sc… DUS MOCK Cohort na… Ingredient… Overall     Overall     
#> # ℹ 16 more rows
#> # ℹ 8 more variables: table_name <chr>, type <chr>, analysis <chr>,
#> #   concept <dbl>, variable <chr>, variable_level <chr>, estimate_type <chr>,
#> #   estimate <chr>