CodelistGenerator

2025-07-06

Working with the OMOP CDM from R

Standarisation of the data format

The OMOP Common Data Model

Create a reference to `GiBleed` dataset

Eunomia is a synthetic OMOP database with ~2,600 individuals. It is freely available and you can create a cdm reference like:

library(DBI)
library(dplyr)
library(duckdb)
library(omopgenerics)
library(CDMConnector)
library(here)
library(CodelistGenerator)
library(CohortConstructor)

con <- dbConnect(duckdb(), 
                 dbdir = eunomiaDir())
cdm <- cdmFromCon(con = con, 
                  cdmSchema = "main", 
                  writeSchema = "main", 
                  cdmName = "Eunomia")
cdm

── # OMOP CDM reference (duckdb) of Eunomia ────────────────────────────────────────────────────────────────────────────

• omop tables: person, observation_period, visit_occurrence, visit_detail, condition_occurrence, drug_exposure,
procedure_occurrence, device_exposure, measurement, observation, death, note, note_nlp, specimen, fact_relationship,
location, care_site, provider, payer_plan_period, cost, drug_era, dose_era, condition_era, metadata, cdm_source,
concept, vocabulary, domain, concept_class, concept_relationship, relationship, concept_synonym, concept_ancestor,
source_to_concept_map, drug_strength

• cohort tables: -

• achilles tables: -

• other tables: -

Creating a reference to the OMOP common data model

Once we have created the our reference to the overall OMOP CDM, we can reference specific tables using the “$” operator or [[““]].

cdm$observation_period |> head(2)

# Source:   SQL [?? x 5]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
  observation_period_id person_id observation_period_start_date observation_period_end_date period_type_concept_id
                  <int>     <int> <date>                        <date>                                       <int>
1                     6         6 1963-12-31                    2007-02-06                                44814724
2                    13        13 2009-04-26                    2019-04-14                                44814724

cdm[["observation_period"]] |> head(2)

# Source:   SQL [?? x 5]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
  observation_period_id person_id observation_period_start_date observation_period_end_date period_type_concept_id
                  <int>     <int> <date>                        <date>                                       <int>
1                     6         6 1963-12-31                    2007-02-06                                44814724
2                    13        13 2009-04-26                    2019-04-14                                44814724

Behind the scenes

The omopgenerics package defines core classes and methods used by CDMConnector and analytic packages.

Having omopgenerics as a central dependency reduces code duplication and ensures consistency across packages (eg function inputs, error messages, and results objects).

omopgenerics::validateCdmArgument(cdm = cdm, 
                                  checkOverlapObservation = TRUE, 
                                  checkStartBeforeEndObservation = TRUE, 
                                  checkPlausibleObservationDates = TRUE)

`<cdm_reference>` object

class(cdm)

[1] "cdm_reference"

cdmName(cdm)

[1] "Eunomia"

cdmVersion(cdm)

[1] "5.3"

cdmSource(cdm)

This is a duckdb cdm source

`<cdm_table>` object

cdm$person

# Source:   table<person> [?? x 18]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
   person_id gender_concept_id year_of_birth month_of_birth day_of_birth birth_datetime      race_concept_id
       <int>             <int>         <int>          <int>        <int> <dttm>                        <int>
 1         6              8532          1963             12           31 1963-12-31 00:00:00            8516
 2       123              8507          1950              4           12 1950-04-12 00:00:00            8527
 3       129              8507          1974             10            7 1974-10-07 00:00:00            8527
 4        16              8532          1971             10           13 1971-10-13 00:00:00            8527
 5        65              8532          1967              3           31 1967-03-31 00:00:00            8516
 6        74              8532          1972              1            5 1972-01-05 00:00:00            8527
 7        42              8532          1909             11            2 1909-11-02 00:00:00            8527
 8       187              8507          1945              7           23 1945-07-23 00:00:00            8527
 9        18              8532          1965             11           17 1965-11-17 00:00:00            8527
10       111              8532          1975              5            2 1975-05-02 00:00:00            8527
# ℹ more rows
# ℹ 11 more variables: ethnicity_concept_id <int>, location_id <int>, provider_id <int>, care_site_id <int>,
#   person_source_value <chr>, gender_source_value <chr>, gender_source_concept_id <int>, race_source_value <chr>,
#   race_source_concept_id <int>, ethnicity_source_value <chr>, ethnicity_source_concept_id <int>

class(cdm$person)

[1] "omop_table"            "cdm_table"             "tbl_duckdb_connection" "tbl_dbi"              
[5] "tbl_sql"               "tbl_lazy"              "tbl"

`<cdm_table>` object

cdmReference(cdm$person)

── # OMOP CDM reference (duckdb) of Eunomia ────────────────────────────────────────────────────────────────────────────

• omop tables: person, observation_period, visit_occurrence, visit_detail, condition_occurrence, drug_exposure,
procedure_occurrence, device_exposure, measurement, observation, death, note, note_nlp, specimen, fact_relationship,
location, care_site, provider, payer_plan_period, cost, drug_era, dose_era, condition_era, metadata, cdm_source,
concept, vocabulary, domain, concept_class, concept_relationship, relationship, concept_synonym, concept_ancestor,
source_to_concept_map, drug_strength

• cohort tables: -

• achilles tables: -

• other tables: -

tableName(cdm$person)

[1] "person"

tableSource(cdm$person)

This is a duckdb cdm source

`<cdm_table>` object

cdmName(cdm$person)

[1] "Eunomia"

cdmVersion(cdm$person)

[1] "5.3"

cdmSource(cdm$person)

This is a duckdb cdm source

Working with the cdm reference

We can use common dplyr operations to interact with the data in our cdm reference

cdm$condition_occurrence |> 
  count()

# Source:   SQL [?? x 1]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
      n
  <dbl>
1 65332

cdm$condition_occurrence |> 
  summarise(
    min_condition_start = min(condition_start_date, na.rm = TRUE),
    median_condition_start = median(condition_start_date, na.rm = TRUE),
    max_condition_start = max(condition_start_date, na.rm = TRUE))

# Source:   SQL [?? x 3]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
  min_condition_start median_condition_start max_condition_start
  <date>              <dttm>                 <date>             
1 1908-10-30          1986-12-09 00:00:00    2019-06-29

Dplyr R code to SQL

Behind the scenes our dplyr query is being translated into SQL.

cdm$condition_occurrence |> 
  count() |> 
  show_query()

<SQL>
SELECT COUNT(*) AS n
FROM condition_occurrence

cdm$condition_occurrence |> 
  summarise(
    min_condition_start = min(condition_start_date, na.rm = TRUE),
    median_condition_start = median(condition_start_date, na.rm = TRUE),
    max_condition_start = max(condition_start_date, na.rm = TRUE)) |> 
  show_query()

<SQL>
SELECT
  MIN(condition_start_date) AS min_condition_start,
  MEDIAN(condition_start_date) AS median_condition_start,
  MAX(condition_start_date) AS max_condition_start
FROM condition_occurrence

Your turn

Using a cdm reference you have connected to:

How many people are in the person table?
What is the minimum observation period start date?
What is the maximum observation period end date?

Solution

💡 Click to see solution

cdm$person |> 
  count()

# Source:   SQL [?? x 1]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
      n
  <dbl>
1  2694

cdm$observation_period |> 
  summarise(min_obs_period_start = min(observation_period_start_date, na.rm = TRUE))

# Source:   SQL [?? x 1]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
  min_obs_period_start
  <date>              
1 1908-09-22

cdm$observation_period |> 
  summarise(max_obs_period_start = max(observation_period_end_date, na.rm = TRUE))

# Source:   SQL [?? x 1]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
  max_obs_period_start
  <date>              
1 2019-07-03

CDM vocabulary tables

https://athena.ohdsi.org

CDM vocabulary tables

cdm$concept |> glimpse()

Rows: ??
Columns: 10
Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
$ concept_id       <int> 35208414, 1118088, 40213201, 1557272, 4336464, 4295880, 3020630, 19129655, 44923712, 1569708,…
$ concept_name     <chr> "Gastrointestinal hemorrhage, unspecified", "celecoxib 200 MG Oral Capsule [Celebrex]", "pneu…
$ domain_id        <chr> "Condition", "Drug", "Drug", "Drug", "Procedure", "Procedure", "Measurement", "Drug", "Drug",…
$ vocabulary_id    <chr> "ICD10CM", "RxNorm", "CVX", "RxNorm", "SNOMED", "SNOMED", "LOINC", "RxNorm", "NDC", "ICD10CM"…
$ concept_class_id <chr> "4-char billing code", "Branded Drug", "CVX", "Ingredient", "Procedure", "Procedure", "Lab Te…
$ standard_concept <chr> NA, "S", "S", "S", "S", "S", "S", "S", NA, NA, "S", "S", "S", "S", "S", "S", NA, "S", "S", "S…
$ concept_code     <chr> "K92.2", "213469", "33", "46041", "232717009", "76601001", "2885-2", "789980", "00025152531",…
$ valid_start_date <date> 2007-01-01, 1970-01-01, 2008-12-01, 1970-01-01, 1970-01-01, 1970-01-01, 1970-01-01, 2008-03-…
$ valid_end_date   <date> 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-…
$ invalid_reason   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…

CDM vocabulary tables

cdm$condition_occurrence |>
  group_by(condition_concept_id) |>
  tally() |>
  left_join(
    cdm$concept |>
      select("concept_id", "concept_name"),
    by = c("condition_concept_id" = "concept_id")
  ) |>
  collect() |>
  arrange(desc(n))

# A tibble: 80 × 3
   condition_concept_id     n concept_name                            
                  <int> <dbl> <chr>                                   
 1             40481087 17268 Viral sinusitis                         
 2              4112343 10217 Acute viral pharyngitis                 
 3               260139  8184 Acute bronchitis                        
 4               372328  3605 Otitis media                            
 5                80180  2694 Osteoarthritis                          
 6                28060  2656 Streptococcal sore throat               
 7                81151  1915 Sprain of ankle                         
 8               378001  1013 Concussion with no loss of consciousness
 9              4283893  1001 Sinusitis                               
10              4294548   939 Acute bacterial sinusitis               
# ℹ 70 more rows

CDM vocabulary tables

cdm$concept_ancestor |> glimpse()

Rows: ??
Columns: 4
Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
$ ancestor_concept_id      <int> 4180628, 4179141, 21500574, 21505770, 21503967, 36203060, 36151386, 21502552, 4076562…
$ descendant_concept_id    <int> 313217, 4146173, 1118084, 1119510, 40162522, 40479422, 1119510, 1112807, 40769189, 19…
$ min_levels_of_separation <int> 5, 2, 4, 0, 5, 4, 0, 4, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 2, 0, 0, 2, 2, 0, 0, 2, 0, 0, 0…
$ max_levels_of_separation <int> 6, 2, 4, 0, 6, 4, 0, 4, 2, 2, 0, 0, 0, 0, 0, 0, 0, 0, 3, 0, 0, 3, 3, 0, 0, 3, 0, 0, 0…

CDM vocabulary tables

cdm$concept_relationship |> glimpse()

Rows: ??
Columns: 6
Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
$ concept_id_1     <int> 192671, 1118088, 1569708, 35208414, 35208414, 40162359, 44923712, 45011828
$ concept_id_2     <int> 35208414, 44923712, 35208414, 192671, 1569708, 45011828, 1118088, 40162359
$ relationship_id  <chr> "Mapped from", "Mapped from", "Subsumes", "Maps to", "Is a", "Mapped from", "Maps to", "Maps …
$ valid_start_date <date> 1970-01-01, 1970-01-01, 2016-03-25, 1970-01-01, 2016-03-25, 2009-08-03, 1970-01-01, 2009-08-0…
$ valid_end_date   <date> 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-31, 2099-12-3…
$ invalid_reason   <chr> NA, NA, NA, NA, NA, NA, NA, NA

CDM vocabulary tables

cdm$concept_synonym |> glimpse()

Rows: ??
Columns: 3
Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
$ concept_id           <int> 964261, 1322184, 441267, 1718412, 4336464, 4102123, 4237458, 4280726, 4330583, 3014576, 4…
$ concept_synonym_name <chr> "cyanocobalamin 5000 MCG/ML Injectable Solution", "clopidogrel", "Cystic fibrosis (disord…
$ language_concept_id  <int> 4180186, 4180186, 4180186, 4180186, 4180186, 4180186, 4180186, 4180186, 4180186, 4180186,…

Exploring vocabulary tables using CodelistGenerator

Vocabulary version

Search results will be specific to the version of the vocabulary being used

getVocabVersion(cdm = cdm)

[1] "v5.0 18-JAN-19"

Available vocabularies

What vocabularies are available?

getVocabularies(cdm = cdm)

[1] "CVX"     "Gender"  "ICD10CM" "LOINC"   "NDC"     "None"    "RxNorm"  "SNOMED"  "Visit"

Available domains

What domains are present?

getDomains(cdm = cdm)

[1] "Drug"        "Measurement" "Procedure"   "Condition"   "Observation" "Visit"       "Gender"

Concept classes

What concept classes are present?

getConceptClassId(
  cdm = cdm,
  standardConcept = "Standard",
  domain = "Drug"
)

[1] "Branded Drug"        "Branded Drug Comp"   "Branded Pack"        "Clinical Drug"       "Clinical Drug Comp" 
[6] "CVX"                 "Ingredient"          "Quant Branded Drug"  "Quant Clinical Drug"

getConceptClassId(
  cdm = cdm,
  standardConcept = "Standard",
  domain = "Condition"
)

[1] "Clinical Finding"

Your turn

Using a cdm reference you have connected to:

What is the vocabulary version of the cdm?
How many concepts are in your concept table?
What domains are available?

Solution

💡 Click to see solution

getVocabVersion(cdm = cdm)

[1] "v5.0 18-JAN-19"

cdm$concept |>
  tally()

# Source:   SQL [?? x 1]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
      n
  <dbl>
1   444

getDomains(cdm = cdm)

[1] "Drug"        "Procedure"   "Condition"   "Measurement" "Observation" "Visit"       "Gender"

Vocabulary based codelists using CodelistGenerator

Vocabulary-based codelists using CodelistGenerator

We can use drug hierarchies and relationships to create vocabulary-based codelists.

Drug ingredients

ingredients <- getDrugIngredientCodes(cdm = cdm, 
                                      name = c("warfarin", "morphine", 
                                               "verapamil", "atorvastatin",
                                               "nitroglycerin", "simvastatin",
                                               "acetaminophen"),
                                      nameStyle = "{concept_name}")
ingredients


- acetaminophen (7 codes)
- atorvastatin (2 codes)
- morphine (2 codes)
- nitroglycerin (2 codes)
- simvastatin (2 codes)
- verapamil (2 codes)
along with 1 more codelists

ingredients$warfarin

[1]  1310149 40163554

cdm$concept |>
  filter(concept_id %in% c(1310149, 40163554))

# Source:   SQL [?? x 10]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
  concept_id concept_name        domain_id vocabulary_id concept_class_id standard_concept concept_code valid_start_date
       <int> <chr>               <chr>     <chr>         <chr>            <chr>            <chr>        <date>          
1    1310149 Warfarin            Drug      RxNorm        Ingredient       S                11289        1970-01-01      
2   40163554 Warfarin Sodium 5 … Drug      RxNorm        Clinical Drug    S                855332       2009-08-02      
# ℹ 2 more variables: valid_end_date <date>, invalid_reason <chr>

Drug ingredients

combinations <- getDrugIngredientCodes(cdm = cdm, 
                                      name = c("acetaminophen"),
                                      ingredientRange = c(2, Inf),
                                      nameStyle = "{concept_name}")
combinations


- acetaminophen (4 codes)

combinations$acetaminophen

[1] 40229134 40231925 40162522 19133768

cdm$concept |>
  filter(concept_id %in% c(40229134, 40231925, 40162522, 19133768)) |> 
  pull(concept_name)

[1] "Acetaminophen 21.7 MG/ML / Dextromethorphan Hydrobromide 1 MG/ML / doxylamine succinate 0.417 MG/ML Oral Solution"
[2] "Acetaminophen 325 MG / Oxycodone Hydrochloride 5 MG Oral Tablet"                                                  
[3] "Acetaminophen 325 MG / Hydrocodone Bitartrate 7.5 MG Oral Tablet"                                                 
[4] "Acetaminophen 750 MG / Hydrocodone Bitartrate 7.5 MG Oral Tablet"

Your turn

Using Eunomia data:

Get codes for memantine using getDrugIngredientCodes. How many codes do you include?
Get codes for memantine, restricting to only concepts with it as the only ingredient. Do the codes you include change?

Solution

💡 Click to see solution

memantine_codes <- getDrugIngredientCodes(cdm = cdm, name = "memantine")
memantine_codes


- 6719_memantine (3 codes)

memantine_monotherapy_codes <- getDrugIngredientCodes(cdm = cdm, name = "memantine",
                                                      ingredientRange = c(1, 1))
memantine_monotherapy_codes


- 6719_memantine (2 codes)

Systematic search using CodelistGenerator

CodelistGenerator is used to create a candidate set of codes for helping to define patient cohorts in data mapped to the OMOP common data model.

A little like the process for a systematic review, the idea is that for a specified search strategy, CodelistGenerator will identify a set of concepts that may be relevant, with these then being screened to remove any irrelevant codes.

Codes for asthma

asthma_codes <- getCandidateCodes(
  cdm = cdm,
  keywords = "asthma",
  domains = "Condition"
)
asthma_codes |> glimpse()

Rows: 2
Columns: 6
$ concept_id       <int> 4051466, 317009
$ found_from       <chr> "From initial search", "From initial search"
$ concept_name     <chr> "Childhood asthma", "Asthma"
$ domain_id        <chr> "Condition", "Condition"
$ vocabulary_id    <chr> "SNOMED", "SNOMED"
$ standard_concept <chr> "S", "S"

asthma_cs <- newCodelist(list("asthma" = asthma_codes$concept_id))
asthma_cs


- asthma (2 codes)

Your turn

Using Eunomia data:

Search for codes for sinusitis recorded in the condition domain
Do you identify any more codes if you also search in the observation domain as well as the condition domain

Solution

💡 Click to see solution

sinusitis_codes <- getCandidateCodes(
  cdm = cdm,
  keywords = "sinusitis",
  domains = "Condition"
)
nrow(sinusitis_codes)

[1] 4

sinusitis_codes_2 <- getCandidateCodes(
  cdm = cdm,
  keywords = "sinusitis",
  domains = c("Condition", "Observation")
)
nrow(sinusitis_codes_2)

[1] 4

Codelists to cohorts

medication_codes <- getDrugIngredientCodes(cdm = cdm, 
                                      name = c("warfarin", "morphine", 
                                               "verapamil", "atorvastatin",
                                               "nitroglycerin", "simvastatin",
                                               "acetaminophen"),
                                      nameStyle = "{concept_name}")

cdm$meds <- conceptCohort(cdm = cdm, 
                          conceptSet = medication_codes,
                          exit = "event_end_date",
                          name = "meds") |> 
  collapseCohorts(gap = 30) 

cdm$meds

# Source:   table<meds> [?? x 4]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
   cohort_definition_id subject_id cohort_start_date cohort_end_date
                  <int>      <int> <date>            <date>         
 1                    1         74 1973-09-28        1973-10-12     
 2                    1        129 1994-08-25        1994-09-08     
 3                    1        220 1990-01-12        1990-01-26     
 4                    1        452 1965-12-31        1966-01-14     
 5                    1        669 1998-06-15        1998-07-06     
 6                    1       1091 1998-07-27        1998-08-10     
 7                    1       1134 1955-06-08        1955-06-22     
 8                    1       1135 1981-10-27        1981-11-26     
 9                    1       1144 1972-06-09        1972-06-23     
10                    1       1144 1987-10-04        1987-10-11     
# ℹ more rows

Codelists to cohorts

sinusitis_codes <- getCandidateCodes(
  cdm = cdm,
  keywords = "sinusitis",
  domains = "Condition"
)

cdm$sinusitis <- conceptCohort(cdm = cdm, 
                          conceptSet = list(sinusitis = sinusitis_codes$concept_id),
                          exit = "event_end_date",
                          name = "sinusitis") |> 
  requireIsFirstEntry()

cdm$sinusitis

# Source:   table<sinusitis> [?? x 4]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
   cohort_definition_id subject_id cohort_start_date cohort_end_date
                  <int>      <int> <date>            <date>         
 1                    1          1 1967-05-30        1967-06-20     
 2                    1         23 1991-08-08        1991-08-22     
 3                    1         69 1952-04-20        1952-10-19     
 4                    1         74 1979-11-30        1979-12-14     
 5                    1         81 1956-08-27        1956-09-03     
 6                    1        188 1967-08-03        1967-08-10     
 7                    1        189 1972-06-02        1972-06-09     
 8                    1        213 1968-12-30        1969-01-06     
 9                    1        219 1973-02-09        1973-02-16     
10                    1        227 1972-03-20        1972-03-27     
# ℹ more rows

`<cohort_table>` object

settings(cdm$meds)

# A tibble: 7 × 4
  cohort_definition_id cohort_name   cdm_version vocabulary_version
                 <int> <chr>         <chr>       <chr>             
1                    1 acetaminophen 5.3         v5.0 18-JAN-19    
2                    2 atorvastatin  5.3         v5.0 18-JAN-19    
3                    3 morphine      5.3         v5.0 18-JAN-19    
4                    4 nitroglycerin 5.3         v5.0 18-JAN-19    
5                    5 simvastatin   5.3         v5.0 18-JAN-19    
6                    6 verapamil     5.3         v5.0 18-JAN-19    
7                    7 warfarin      5.3         v5.0 18-JAN-19

settings(cdm$sinusitis)

# A tibble: 1 × 4
  cohort_definition_id cohort_name cdm_version vocabulary_version
                 <int> <chr>       <chr>       <chr>             
1                    1 sinusitis   5.3         v5.0 18-JAN-19

`<cohort_table>` object

cohortCount(cdm$meds)

# A tibble: 7 × 3
  cohort_definition_id number_records number_subjects
                 <int>          <int>           <int>
1                    1          13860            2679
2                    2             57              54
3                    3             35              35
4                    4            208             192
5                    5            182             182
6                    6            137             137
7                    7            137             137

cohortCount(cdm$sinusitis)

# A tibble: 1 × 3
  cohort_definition_id number_records number_subjects
                 <int>          <int>           <int>
1                    1           2689            2689

`<cohort_table>` object

attrition(cdm$meds)

# A tibble: 49 × 7
   cohort_definition_id number_records number_subjects reason_id reason               excluded_records excluded_subjects
                  <int>          <int>           <int>     <int> <chr>                           <int>             <int>
 1                    1          14205            2679         1 Initial qualifying …                0                 0
 2                    1          14205            2679         2 Record start <= rec…                0                 0
 3                    1          14205            2679         3 Record in observati…                0                 0
 4                    1          14205            2679         4 Non-missing sex                     0                 0
 5                    1          14205            2679         5 Non-missing year of…                0                 0
 6                    1          13908            2679         6 Merge overlapping r…              297                 0
 7                    1          13860            2679         7 Collapse cohort wit…               48                 0
 8                    2             57              54         1 Initial qualifying …                0                 0
 9                    2             57              54         2 Record start <= rec…                0                 0
10                    2             57              54         3 Record in observati…                0                 0
# ℹ 39 more rows

attrition(cdm$sinusitis)

# A tibble: 7 × 7
  cohort_definition_id number_records number_subjects reason_id reason                excluded_records excluded_subjects
                 <int>          <int>           <int>     <int> <chr>                            <int>             <int>
1                    1          20033            2689         1 Initial qualifying e…                0                 0
2                    1          20033            2689         2 Record start <= reco…                0                 0
3                    1          20033            2689         3 Record in observation                0                 0
4                    1          20033            2689         4 Non-missing sex                      0                 0
5                    1          20033            2689         5 Non-missing year of …                0                 0
6                    1          19147            2689         6 Merge overlapping re…              886                 0
7                    1           2689            2689         7 Restricted to first …            16458                 0

`<cohort_table>` object

cohortCodelist(cdm$meds, 1)


- acetaminophen (7 codes)

cohortCodelist(cdm$sinusitis, 1)


- sinusitis (4 codes)

Your turn

Using Eunomia data:

Create a cohort of people taking aspirin, collapse records less than 30 days, and require is first ever exposure

Solution

💡 Click to see solution

aspirin_codes <- getDrugIngredientCodes(cdm = cdm, name = "aspirin")
cdm$aspirin_cohort <- conceptCohort(cdm = cdm, conceptSet = aspirin_codes, 
                                    name = "aspirin_cohort") |> 
  collapseCohorts(gap = 30) |> 
  requireIsFirstEntry()

cdm$aspirin_cohort |> 
  glimpse()

Rows: ??
Columns: 4
Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//tmp/RtmpkGLTdC/file2a5f12d5ca24.duckdb]
$ cohort_definition_id <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1,…
$ subject_id           <int> 1, 69, 81, 189, 227, 244, 246, 254, 310, 332, 352, 362, 388, 432, 450, 480, 505, 580, 616…
$ cohort_start_date    <date> 1970-12-03, 1947-11-05, 1963-09-11, 1970-01-08, 1974-04-04, 1926-05-07, 1961-11-15, 1992…
$ cohort_end_date      <date> 1970-12-31, 1947-11-19, 1963-12-10, 1970-02-07, 1974-04-18, 1926-05-21, 1962-01-14, 1992…

CodelistGenerator

Working with the OMOP CDM from R

Standarisation of the data format

Create a reference to GiBleed dataset

Creating a reference to the OMOP common data model

Behind the scenes

<cdm_reference> object

<cdm_table> object

<cdm_table> object

<cdm_table> object

Working with the cdm reference

Dplyr R code to SQL

Your turn

Solution

CDM vocabulary tables

CDM vocabulary tables

CDM vocabulary tables

CDM vocabulary tables

CDM vocabulary tables

CDM vocabulary tables

CDM vocabulary tables

Exploring vocabulary tables using CodelistGenerator

Vocabulary version

Available vocabularies

Available domains

Concept classes

Your turn

Solution

Vocabulary based codelists using CodelistGenerator

Vocabulary-based codelists using CodelistGenerator

Drug ingredients

Drug ingredients

Your turn

Solution

Systematic search using CodelistGenerator

Systematic search using CodelistGenerator

Codes for asthma

Your turn

Solution

Codelists to cohorts

Codelists to cohorts

Codelists to cohorts

<cohort_table> object

<cohort_table> object

<cohort_table> object

<cohort_table> object

Your turn

Solution

Create a reference to `GiBleed` dataset

`<cdm_reference>` object

`<cdm_table>` object

`<cdm_table>` object

`<cdm_table>` object

`<cohort_table>` object

`<cohort_table>` object

`<cohort_table>` object

`<cohort_table>` object