OMOP and CDMConnector

The OMOP Common Data Model

2025-06-26

Introduction: The OMOP Common Data Model

Every time that someone goes to the doctor and something happens the doctors write it into their records.
Each annotation of the doctor is translated into a code, combination of letters and numbers that refers to a condition. There exist several different codding languages: SNOMED, read codes, ICD10, ICD9, RxNorm, ATC, … It depends on the region, language, type of record and others which one is used. This makes that the same condition or drug can be coded in different ways.
A compilation of these records for a group of people is what we call the medical databases. Depending on the origin and purpose of these data there are different groups of databases: electronic health records, claims data, registries… This databases can be structured by several different tables.
The Observational Medical Outcomes Partnership (OMOP) Common Data Model (CDM) is an open community data standard, designed to standardise the structure and content of observational data and to enable efficient analyses that can produce reliable evidence.

Standarisation of the data format

Tables and relation in the OMOP Common Data Model

Mapping a database to the OMOP CDM

Mapping process

Mapping a database to the OMOP CDM

Mapping process

Mapping a database to the OMOP CDM

Mapping process

Standarisation of the vocabularies

From all the vocabularies OMOP CDM uses only a few as Standard: SNOMED for conditions, RxNorm for drugs, …

The process to obtain an standard code from non standard one is called mapping. We can find the mapping in the concept_relationship table.

Each one of the records in clinical data tables (condition_occurrence, drug_exposure, measurement, observation, …) will be coded by two codes:

Source concept: particular to each database, it is the original code.
Standard concept: equivalent code from the standard vocabulary.

Example of mapping

In concept relationship we can find different information such as:

Concept relationship

In particular, we have the Maps to and Mapped from relations that can help us to see the mapping between codes.

Example of mapping

Mapping process

Example of mapping

Mapping process

Example of mapping

Mapping process

More details

For more details on how the vocabularies work you can check: Vocabulary course in EHDEN academy

All details about OMOP CDM and more can be found in: the book of ohdsi.

Let’s start coding

These are the packages that we will use in this presentation:

library(omock)
library(DBI)
library(CDMConnector)
library(duckdb)
library(here)
library(dplyr)
library(visOmopResults)
library(omopgenerics)

You can click on the specific functions to see ?help and what package they come from:

cdmFromCon()

Create a mock reference

You can create a mock database using omock from one of the 24 available synthetic databases:

availableMockDatasets()

 [1] "GiBleed"                             "empty_cdm"                           "synpuf-1k_5.3"                      
 [4] "synpuf-1k_5.4"                       "synthea-allergies-10k"               "synthea-anemia-10k"                 
 [7] "synthea-breast_cancer-10k"           "synthea-contraceptives-10k"          "synthea-covid19-10k"                
[10] "synthea-covid19-200k"                "synthea-dermatitis-10k"              "synthea-heart-10k"                  
[13] "synthea-hiv-10k"                     "synthea-lung_cancer-10k"             "synthea-medications-10k"            
[16] "synthea-metabolic_syndrome-10k"      "synthea-opioid_addiction-10k"        "synthea-rheumatoid_arthritis-10k"   
[19] "synthea-snf-10k"                     "synthea-surgery-10k"                 "synthea-total_joint_replacement-10k"
[22] "synthea-veteran_prostate_cancer-10k" "synthea-veterans-10k"                "synthea-weight_loss-10k"

cdm <- mockCdmFromDataset(datasetName = "GiBleed")

ℹ Reading GiBleed tables.
ℹ Adding drug_strength table.

cdm


── # OMOP CDM reference (local) of GiBleed ─────────────────────────────────────────────────────────────────────────────
• omop tables: care_site, cdm_source, concept_ancestor, concept_class, concept_relationship, concept_synonym, concept,
condition_era, condition_occurrence, cost, death, device_exposure, domain, dose_era, drug_era, drug_exposure,
drug_strength, fact_relationship, location, measurement, metadata, note_nlp, note, observation_period, observation,
payer_plan_period, person, procedure_occurrence, provider, relationship, source_to_concept_map, specimen, visit_detail,
visit_occurrence, vocabulary
• cohort tables: -
• achilles tables: -
• other tables: -

`<cdm_reference>` object

class(cdm)

[1] "cdm_reference"

cdmName(cdm)

[1] "GiBleed"

cdmVersion(cdm)

[1] "5.3"

cdmSource(cdm)

This is a local cdm source

`<cdm_reference>` object

attributes(cdm)

$names
 [1] "care_site"             "cdm_source"            "concept_ancestor"      "concept_class"        
 [5] "concept_relationship"  "concept_synonym"       "concept"               "condition_era"        
 [9] "condition_occurrence"  "cost"                  "death"                 "device_exposure"      
[13] "domain"                "dose_era"              "drug_era"              "drug_exposure"        
[17] "drug_strength"         "fact_relationship"     "location"              "measurement"          
[21] "metadata"              "note_nlp"              "note"                  "observation_period"   
[25] "observation"           "payer_plan_period"     "person"                "procedure_occurrence" 
[29] "provider"              "relationship"          "source_to_concept_map" "specimen"             
[33] "visit_detail"          "visit_occurrence"      "vocabulary"           

$cdm_name
[1] "GiBleed"

$cdm_source

This is a local cdm source


$cdm_version
[1] "5.3"

$class
[1] "cdm_reference"

`<cdm_table>` object

cdm$person

# A tibble: 2,694 × 18
   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 2,684 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_df"     "tbl"        "data.frame"

`<cdm_table>` object

cdmReference(cdm$person)

── # OMOP CDM reference (local) of GiBleed ─────────────────────────────────────────────────────────────────────────────

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

• cohort tables: -

• achilles tables: -

• other tables: -

tableName(cdm$person)

[1] "person"

tableSource(cdm$person)

This is a local cdm source

`<cdm_table>` object

cdmName(cdm$person)

[1] "GiBleed"

cdmVersion(cdm$person)

[1] "5.3"

cdmSource(cdm$person)

This is a local cdm source

`<cdm_table>` object

attributes(cdm$person)

$class
[1] "omop_table" "cdm_table"  "tbl_df"     "tbl"        "data.frame"

$row.names
   [1]    1    2    3    4    5    6    7    8    9   10   11   12   13   14   15   16   17   18   19   20   21   22
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[1959] 1959 1960 1961 1962 1963 1964 1965 1966 1967 1968 1969 1970 1971 1972 1973 1974 1975 1976 1977 1978 1979 1980
[1981] 1981 1982 1983 1984 1985 1986 1987 1988 1989 1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002
[2003] 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018 2019 2020 2021 2022 2023 2024
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[2443] 2443 2444 2445 2446 2447 2448 2449 2450 2451 2452 2453 2454 2455 2456 2457 2458 2459 2460 2461 2462 2463 2464
[2465] 2465 2466 2467 2468 2469 2470 2471 2472 2473 2474 2475 2476 2477 2478 2479 2480 2481 2482 2483 2484 2485 2486
[2487] 2487 2488 2489 2490 2491 2492 2493 2494 2495 2496 2497 2498 2499 2500 2501 2502 2503 2504 2505 2506 2507 2508
[2509] 2509 2510 2511 2512 2513 2514 2515 2516 2517 2518 2519 2520 2521 2522 2523 2524 2525 2526 2527 2528 2529 2530
[2531] 2531 2532 2533 2534 2535 2536 2537 2538 2539 2540 2541 2542 2543 2544 2545 2546 2547 2548 2549 2550 2551 2552
[2553] 2553 2554 2555 2556 2557 2558 2559 2560 2561 2562 2563 2564 2565 2566 2567 2568 2569 2570 2571 2572 2573 2574
[2575] 2575 2576 2577 2578 2579 2580 2581 2582 2583 2584 2585 2586 2587 2588 2589 2590 2591 2592 2593 2594 2595 2596
[2597] 2597 2598 2599 2600 2601 2602 2603 2604 2605 2606 2607 2608 2609 2610 2611 2612 2613 2614 2615 2616 2617 2618
[2619] 2619 2620 2621 2622 2623 2624 2625 2626 2627 2628 2629 2630 2631 2632 2633 2634 2635 2636 2637 2638 2639 2640
[2641] 2641 2642 2643 2644 2645 2646 2647 2648 2649 2650 2651 2652 2653 2654 2655 2656 2657 2658 2659 2660 2661 2662
[2663] 2663 2664 2665 2666 2667 2668 2669 2670 2671 2672 2673 2674 2675 2676 2677 2678 2679 2680 2681 2682 2683 2684
[2685] 2685 2686 2687 2688 2689 2690 2691 2692 2693 2694

$names
 [1] "person_id"                   "gender_concept_id"           "year_of_birth"              
 [4] "month_of_birth"              "day_of_birth"                "birth_datetime"             
 [7] "race_concept_id"             "ethnicity_concept_id"        "location_id"                
[10] "provider_id"                 "care_site_id"                "person_source_value"        
[13] "gender_source_value"         "gender_source_concept_id"    "race_source_value"          
[16] "race_source_concept_id"      "ethnicity_source_value"      "ethnicity_source_concept_id"

$tbl_source

This is a local cdm source


$tbl_name
[1] "person"

$cdm_reference


── # OMOP CDM reference (local) of GiBleed ─────────────────────────────────────────────────────────────────────────────
• omop tables: care_site, cdm_source, concept_ancestor, concept_class, concept_relationship, concept_synonym, concept,
condition_era, condition_occurrence, cost, death, device_exposure, domain, dose_era, drug_era, drug_exposure,
drug_strength, fact_relationship, location, measurement, metadata, note_nlp, note, observation_period, observation,
payer_plan_period, person, procedure_occurrence, provider, relationship, source_to_concept_map, specimen, visit_detail,
visit_occurrence, vocabulary
• cohort tables: -
• achilles tables: -
• other tables: -

Connecting to a database from R (the DBI package)

In general the OMOP datasets that we will use won’t be locally, and they will on a database.

Database connections from R can be made using the DBI package.

Connect to postgres:

library(RPostgres)
db <- dbConnect(
  Postgres(),
  dbname = "...",
  host = "...",
  user = "...",
  password = "..."
)

Connecting to a database from R (the DBI package)

Connect to Sql server:

library(odbc)
db <- dbConnect(
  odbc(),
  Driver   = "ODBC Driver 18 for SQL Server",
  Server   = "...",
  Database = "...",
  UID      = "...",
  PWD      = "...",
  TrustServerCertificate = "yes",
  Port     = "..."
)

In this CDMConnector article you can see how to connect to the different supported DBMS.

Databases organisation

Databases are organised by schemas (blueprint or plan that defines how the data will be organised and structured within the database).

In general, OMOP databases have two schemas:

cdm schema: it contains all the tables of the cdm. Usually we only will have reading permission for this schema.
write schema: it is a place where we can store tables (like cohorts). We need writing permissions to this schema.

Create a local database

duckdb is a package that allows us to create local databases.

# name of the dataset
datasetName <- "GiBleed"

# name of the database file
dbdir <- here(paste0(datasetName, ".duckdb"))

# create empty database
con <- dbConnect(drv = duckdb(dbdir = dbdir))

# create local reference
cdm <- mockCdmFromDataset(datasetName = datasetName)

# copy local reference to connection
insertCdmTo(cdm = cdm, to = dbSource(con = con, writeSchema = "main"))

# disconnect
dbDisconnect(conn = con)

Let’s create our first cdm reference

# connect to the database
dbdir <- here("GiBleed.duckdb")
con <- dbConnect(drv = duckdb(dbdir = dbdir))
cdm <- cdmFromCon(con = con, cdmSchema = "main", writeSchema = "main")
cdm

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

• 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: -

Access to tables of the cdm reference

cdm$person

# Source:   table<person> [?? x 18]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//home/runner/work/RealWorldEvidenceSummerSchool2025/RealWorldEvidenceSummerSchool2025/GiBleed.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>

Read tables in GiBleed

Once we read a table we can operate with it and for example count the number of rows of person table.

cdm$person |>
  count()

# Source:   SQL [?? x 1]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//home/runner/work/RealWorldEvidenceSummerSchool2025/RealWorldEvidenceSummerSchool2025/GiBleed.duckdb]
      n
  <dbl>
1  2694

Operation with tidyverse

If you are familiarised with tidyverse you can use any of the usual dplyr commands in you database tables.

cdm$drug_exposure |>
  group_by(drug_concept_id) |>
  summarise(number_persons = n_distinct(person_id)) |>
  collect() |>
  arrange(desc(number_persons))

# A tibble: 113 × 2
   drug_concept_id number_persons
             <int>          <dbl>
 1        40213227           2660
 2         1127433           2580
 3        40213160           2140
 4         1713671           2021
 5        19059056           1927
 6         1118084           1844
 7        40213296           1737
 8        40213306           1560
 9         1127078           1428
10        40229134           1393
# ℹ 103 more rows

Database name

When we have a cdm object we can check which is the name of that database using:

cdmName(cdm)

[1] "Synthea"

In some cases we want to give a database a name that we want, this can be done at the connection stage:

cdm <- cdmFromCon(
  con = con, cdmSchema = "main", writeSchema = "main", cdmName = "GiBleed"
)

cdmName(cdm)

[1] "GiBleed"

Create a new table

Let’s say I want to subset the condition_occurrence table to a certain rows and certain columns and save it so I can later access it.

temporary table (default):

cdm$my_saved_table <- cdm$condition_occurrence |>
  filter(condition_concept_id == 4112343) |>
  select(person_id, condition_start_date) |>
  compute()
listSourceTables(cdm)

  [1] "care_site"                                  "cdm_source"                                
  [3] "concept"                                    "concept_ancestor"                          
  [5] "concept_class"                              "concept_relationship"                      
  [7] "concept_synonym"                            "condition_era"                             
  [9] "condition_occurrence"                       "cost"                                      
 [11] "death"                                      "device_exposure"                           
 [13] "domain"                                     "dose_era"                                  
 [15] "drug_era"                                   "drug_exposure"                             
 [17] "drug_strength"                              "fact_relationship"                         
 [19] "location"                                   "measurement"                               
 [21] "metadata"                                   "my_study_age_cohort"                       
 [23] "my_study_age_cohort_attrition"              "my_study_age_cohort_codelist"              
 [25] "my_study_age_cohort_set"                    "my_study_aspirin"                          
 [27] "my_study_aspirin_attrition"                 "my_study_aspirin_codelist"                 
 [29] "my_study_aspirin_last"                      "my_study_aspirin_last_attrition"           
 [31] "my_study_aspirin_last_codelist"             "my_study_aspirin_last_set"                 
 [33] "my_study_aspirin_set"                       "my_study_aspirin_strata"                   
 [35] "my_study_aspirin_strata_attrition"          "my_study_aspirin_strata_codelist"          
 [37] "my_study_aspirin_strata_set"                "my_study_diclofenac"                       
 [39] "my_study_diclofenac_attrition"              "my_study_diclofenac_codelist"              
 [41] "my_study_diclofenac_set"                    "my_study_ibuprofen"                        
 [43] "my_study_ibuprofen_attrition"               "my_study_ibuprofen_codelist"               
 [45] "my_study_ibuprofen_death"                   "my_study_ibuprofen_death_attrition"        
 [47] "my_study_ibuprofen_death_codelist"          "my_study_ibuprofen_death_set"              
 [49] "my_study_ibuprofen_set"                     "my_study_ibuprofen_strata"                 
 [51] "my_study_ibuprofen_strata_attrition"        "my_study_ibuprofen_strata_codelist"        
 [53] "my_study_ibuprofen_strata_set"              "my_study_ibuprofen_years"                  
 [55] "my_study_ibuprofen_years_attrition"         "my_study_ibuprofen_years_codelist"         
 [57] "my_study_ibuprofen_years_set"               "my_study_intersection"                     
 [59] "my_study_intersection_attrition"            "my_study_intersection_codelist"            
 [61] "my_study_intersection_set"                  "my_study_medications"                      
 [63] "my_study_medications_attrition"             "my_study_medications_codelist"             
 [65] "my_study_medications_collapsed"             "my_study_medications_collapsed_attrition"  
 [67] "my_study_medications_collapsed_codelist"    "my_study_medications_collapsed_set"        
 [69] "my_study_medications_requirement"           "my_study_medications_requirement_attrition"
 [71] "my_study_medications_requirement_codelist"  "my_study_medications_requirement_set"      
 [73] "my_study_medications_set"                   "my_study_medications_trimmed"              
 [75] "my_study_medications_trimmed_attrition"     "my_study_medications_trimmed_codelist"     
 [77] "my_study_medications_trimmed_set"           "my_study_mi"                               
 [79] "my_study_mi_attrition"                      "my_study_mi_codelist"                      
 [81] "my_study_mi_set"                            "my_study_og_028_1750928000"                
 [83] "my_study_og_029_1750928000"                 "my_study_og_029_1750928000_attrition"      
 [85] "my_study_og_029_1750928000_codelist"        "my_study_og_029_1750928000_set"            
 [87] "my_study_og_057_1750928015"                 "my_study_og_058_1750928015"                
 [89] "my_study_og_058_1750928015_attrition"       "my_study_og_058_1750928015_codelist"       
 [91] "my_study_og_058_1750928015_set"             "my_study_og_077_1750928021"                
 [93] "my_study_og_078_1750928021"                 "my_study_og_078_1750928021_attrition"      
 [95] "my_study_og_078_1750928021_codelist"        "my_study_og_078_1750928021_set"            
 [97] "my_study_og_097_1750928028"                 "my_study_og_098_1750928028"                
 [99] "my_study_og_098_1750928028_attrition"       "my_study_og_098_1750928028_codelist"       
[101] "my_study_og_098_1750928028_set"             "my_study_og_117_1750928033"                
[103] "my_study_og_118_1750928033"                 "my_study_og_118_1750928033_attrition"      
[105] "my_study_og_118_1750928033_codelist"        "my_study_og_118_1750928033_set"            
[107] "my_study_og_137_1750928038"                 "my_study_og_138_1750928038"                
[109] "my_study_og_138_1750928038_attrition"       "my_study_og_138_1750928038_codelist"       
[111] "my_study_og_138_1750928038_set"             "my_study_og_176_1750928057"                
[113] "my_study_og_177_1750928057"                 "my_study_og_177_1750928057_attrition"      
[115] "my_study_og_177_1750928057_codelist"        "my_study_og_177_1750928057_set"            
[117] "my_study_og_196_1750928061"                 "my_study_og_197_1750928061"                
[119] "my_study_og_197_1750928061_attrition"       "my_study_og_197_1750928061_codelist"       
[121] "my_study_og_197_1750928061_set"             "my_study_statins"                          
[123] "my_study_statins_attrition"                 "my_study_statins_codelist"                 
[125] "my_study_statins_set"                       "note"                                      
[127] "note_nlp"                                   "observation"                               
[129] "observation_period"                         "payer_plan_period"                         
[131] "person"                                     "procedure_occurrence"                      
[133] "provider"                                   "relationship"                              
[135] "source_to_concept_map"                      "specimen"                                  
[137] "test_conditions"                            "test_conditions_attrition"                 
[139] "test_conditions_codelist"                   "test_conditions_set"                       
[141] "test_medications"                           "test_medications_attrition"                
[143] "test_medications_codelist"                  "test_medications_set"                      
[145] "test_new_sinusitis"                         "test_new_sinusitis_attrition"              
[147] "test_new_sinusitis_codelist"                "test_new_sinusitis_set"                    
[149] "test_sinusitis"                             "test_sinusitis_attrition"                  
[151] "test_sinusitis_codelist"                    "test_sinusitis_set"                        
[153] "visit_detail"                               "visit_occurrence"                          
[155] "vocabulary"                                 "og_001_1750928373"

Create a new table

permanent table:

cdm$my_saved_table <- cdm$condition_occurrence |>
  filter(condition_concept_id == 4112343) |>
  select(person_id, condition_start_date) |>
  compute(name = "my_saved_table")
listSourceTables(cdm)

  [1] "care_site"                                  "cdm_source"                                
  [3] "concept"                                    "concept_ancestor"                          
  [5] "concept_class"                              "concept_relationship"                      
  [7] "concept_synonym"                            "condition_era"                             
  [9] "condition_occurrence"                       "cost"                                      
 [11] "death"                                      "device_exposure"                           
 [13] "domain"                                     "dose_era"                                  
 [15] "drug_era"                                   "drug_exposure"                             
 [17] "drug_strength"                              "fact_relationship"                         
 [19] "location"                                   "measurement"                               
 [21] "metadata"                                   "my_saved_table"                            
 [23] "my_study_age_cohort"                        "my_study_age_cohort_attrition"             
 [25] "my_study_age_cohort_codelist"               "my_study_age_cohort_set"                   
 [27] "my_study_aspirin"                           "my_study_aspirin_attrition"                
 [29] "my_study_aspirin_codelist"                  "my_study_aspirin_last"                     
 [31] "my_study_aspirin_last_attrition"            "my_study_aspirin_last_codelist"            
 [33] "my_study_aspirin_last_set"                  "my_study_aspirin_set"                      
 [35] "my_study_aspirin_strata"                    "my_study_aspirin_strata_attrition"         
 [37] "my_study_aspirin_strata_codelist"           "my_study_aspirin_strata_set"               
 [39] "my_study_diclofenac"                        "my_study_diclofenac_attrition"             
 [41] "my_study_diclofenac_codelist"               "my_study_diclofenac_set"                   
 [43] "my_study_ibuprofen"                         "my_study_ibuprofen_attrition"              
 [45] "my_study_ibuprofen_codelist"                "my_study_ibuprofen_death"                  
 [47] "my_study_ibuprofen_death_attrition"         "my_study_ibuprofen_death_codelist"         
 [49] "my_study_ibuprofen_death_set"               "my_study_ibuprofen_set"                    
 [51] "my_study_ibuprofen_strata"                  "my_study_ibuprofen_strata_attrition"       
 [53] "my_study_ibuprofen_strata_codelist"         "my_study_ibuprofen_strata_set"             
 [55] "my_study_ibuprofen_years"                   "my_study_ibuprofen_years_attrition"        
 [57] "my_study_ibuprofen_years_codelist"          "my_study_ibuprofen_years_set"              
 [59] "my_study_intersection"                      "my_study_intersection_attrition"           
 [61] "my_study_intersection_codelist"             "my_study_intersection_set"                 
 [63] "my_study_medications"                       "my_study_medications_attrition"            
 [65] "my_study_medications_codelist"              "my_study_medications_collapsed"            
 [67] "my_study_medications_collapsed_attrition"   "my_study_medications_collapsed_codelist"   
 [69] "my_study_medications_collapsed_set"         "my_study_medications_requirement"          
 [71] "my_study_medications_requirement_attrition" "my_study_medications_requirement_codelist" 
 [73] "my_study_medications_requirement_set"       "my_study_medications_set"                  
 [75] "my_study_medications_trimmed"               "my_study_medications_trimmed_attrition"    
 [77] "my_study_medications_trimmed_codelist"      "my_study_medications_trimmed_set"          
 [79] "my_study_mi"                                "my_study_mi_attrition"                     
 [81] "my_study_mi_codelist"                       "my_study_mi_set"                           
 [83] "my_study_og_028_1750928000"                 "my_study_og_029_1750928000"                
 [85] "my_study_og_029_1750928000_attrition"       "my_study_og_029_1750928000_codelist"       
 [87] "my_study_og_029_1750928000_set"             "my_study_og_057_1750928015"                
 [89] "my_study_og_058_1750928015"                 "my_study_og_058_1750928015_attrition"      
 [91] "my_study_og_058_1750928015_codelist"        "my_study_og_058_1750928015_set"            
 [93] "my_study_og_077_1750928021"                 "my_study_og_078_1750928021"                
 [95] "my_study_og_078_1750928021_attrition"       "my_study_og_078_1750928021_codelist"       
 [97] "my_study_og_078_1750928021_set"             "my_study_og_097_1750928028"                
 [99] "my_study_og_098_1750928028"                 "my_study_og_098_1750928028_attrition"      
[101] "my_study_og_098_1750928028_codelist"        "my_study_og_098_1750928028_set"            
[103] "my_study_og_117_1750928033"                 "my_study_og_118_1750928033"                
[105] "my_study_og_118_1750928033_attrition"       "my_study_og_118_1750928033_codelist"       
[107] "my_study_og_118_1750928033_set"             "my_study_og_137_1750928038"                
[109] "my_study_og_138_1750928038"                 "my_study_og_138_1750928038_attrition"      
[111] "my_study_og_138_1750928038_codelist"        "my_study_og_138_1750928038_set"            
[113] "my_study_og_176_1750928057"                 "my_study_og_177_1750928057"                
[115] "my_study_og_177_1750928057_attrition"       "my_study_og_177_1750928057_codelist"       
[117] "my_study_og_177_1750928057_set"             "my_study_og_196_1750928061"                
[119] "my_study_og_197_1750928061"                 "my_study_og_197_1750928061_attrition"      
[121] "my_study_og_197_1750928061_codelist"        "my_study_og_197_1750928061_set"            
[123] "my_study_statins"                           "my_study_statins_attrition"                
[125] "my_study_statins_codelist"                  "my_study_statins_set"                      
[127] "note"                                       "note_nlp"                                  
[129] "observation"                                "observation_period"                        
[131] "payer_plan_period"                          "person"                                    
[133] "procedure_occurrence"                       "provider"                                  
[135] "relationship"                               "source_to_concept_map"                     
[137] "specimen"                                   "test_conditions"                           
[139] "test_conditions_attrition"                  "test_conditions_codelist"                  
[141] "test_conditions_set"                        "test_medications"                          
[143] "test_medications_attrition"                 "test_medications_codelist"                 
[145] "test_medications_set"                       "test_new_sinusitis"                        
[147] "test_new_sinusitis_attrition"               "test_new_sinusitis_codelist"               
[149] "test_new_sinusitis_set"                     "test_sinusitis"                            
[151] "test_sinusitis_attrition"                   "test_sinusitis_codelist"                   
[153] "test_sinusitis_set"                         "visit_detail"                              
[155] "visit_occurrence"                           "vocabulary"                                
[157] "og_001_1750928373"

Create a new table

cdm

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

• 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: my_saved_table

cdm$my_saved_table

# Source:   table<my_saved_table> [?? x 2]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//home/runner/work/RealWorldEvidenceSummerSchool2025/RealWorldEvidenceSummerSchool2025/GiBleed.duckdb]
   person_id condition_start_date
       <int> <date>              
 1       263 2015-10-02          
 2       439 1990-03-20          
 3       449 1999-12-12          
 4       515 1961-11-14          
 5        17 1963-12-02          
 6        30 1993-03-19          
 7        90 1970-01-15          
 8       116 1959-06-11          
 9       137 2005-11-15          
10       176 1986-10-08          
# ℹ more rows

Drop an existing table

To drop an existing table:

Eliminate the table from the cdm object.
Eliminate the table from the database.

cdm <- dropSourceTable(cdm = cdm, name = "my_saved_table")
cdm

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

• 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: -

Drop an existing table

listSourceTables(cdm)

  [1] "care_site"                                  "cdm_source"                                
  [3] "concept"                                    "concept_ancestor"                          
  [5] "concept_class"                              "concept_relationship"                      
  [7] "concept_synonym"                            "condition_era"                             
  [9] "condition_occurrence"                       "cost"                                      
 [11] "death"                                      "device_exposure"                           
 [13] "domain"                                     "dose_era"                                  
 [15] "drug_era"                                   "drug_exposure"                             
 [17] "drug_strength"                              "fact_relationship"                         
 [19] "location"                                   "measurement"                               
 [21] "metadata"                                   "my_study_age_cohort"                       
 [23] "my_study_age_cohort_attrition"              "my_study_age_cohort_codelist"              
 [25] "my_study_age_cohort_set"                    "my_study_aspirin"                          
 [27] "my_study_aspirin_attrition"                 "my_study_aspirin_codelist"                 
 [29] "my_study_aspirin_last"                      "my_study_aspirin_last_attrition"           
 [31] "my_study_aspirin_last_codelist"             "my_study_aspirin_last_set"                 
 [33] "my_study_aspirin_set"                       "my_study_aspirin_strata"                   
 [35] "my_study_aspirin_strata_attrition"          "my_study_aspirin_strata_codelist"          
 [37] "my_study_aspirin_strata_set"                "my_study_diclofenac"                       
 [39] "my_study_diclofenac_attrition"              "my_study_diclofenac_codelist"              
 [41] "my_study_diclofenac_set"                    "my_study_ibuprofen"                        
 [43] "my_study_ibuprofen_attrition"               "my_study_ibuprofen_codelist"               
 [45] "my_study_ibuprofen_death"                   "my_study_ibuprofen_death_attrition"        
 [47] "my_study_ibuprofen_death_codelist"          "my_study_ibuprofen_death_set"              
 [49] "my_study_ibuprofen_set"                     "my_study_ibuprofen_strata"                 
 [51] "my_study_ibuprofen_strata_attrition"        "my_study_ibuprofen_strata_codelist"        
 [53] "my_study_ibuprofen_strata_set"              "my_study_ibuprofen_years"                  
 [55] "my_study_ibuprofen_years_attrition"         "my_study_ibuprofen_years_codelist"         
 [57] "my_study_ibuprofen_years_set"               "my_study_intersection"                     
 [59] "my_study_intersection_attrition"            "my_study_intersection_codelist"            
 [61] "my_study_intersection_set"                  "my_study_medications"                      
 [63] "my_study_medications_attrition"             "my_study_medications_codelist"             
 [65] "my_study_medications_collapsed"             "my_study_medications_collapsed_attrition"  
 [67] "my_study_medications_collapsed_codelist"    "my_study_medications_collapsed_set"        
 [69] "my_study_medications_requirement"           "my_study_medications_requirement_attrition"
 [71] "my_study_medications_requirement_codelist"  "my_study_medications_requirement_set"      
 [73] "my_study_medications_set"                   "my_study_medications_trimmed"              
 [75] "my_study_medications_trimmed_attrition"     "my_study_medications_trimmed_codelist"     
 [77] "my_study_medications_trimmed_set"           "my_study_mi"                               
 [79] "my_study_mi_attrition"                      "my_study_mi_codelist"                      
 [81] "my_study_mi_set"                            "my_study_og_028_1750928000"                
 [83] "my_study_og_029_1750928000"                 "my_study_og_029_1750928000_attrition"      
 [85] "my_study_og_029_1750928000_codelist"        "my_study_og_029_1750928000_set"            
 [87] "my_study_og_057_1750928015"                 "my_study_og_058_1750928015"                
 [89] "my_study_og_058_1750928015_attrition"       "my_study_og_058_1750928015_codelist"       
 [91] "my_study_og_058_1750928015_set"             "my_study_og_077_1750928021"                
 [93] "my_study_og_078_1750928021"                 "my_study_og_078_1750928021_attrition"      
 [95] "my_study_og_078_1750928021_codelist"        "my_study_og_078_1750928021_set"            
 [97] "my_study_og_097_1750928028"                 "my_study_og_098_1750928028"                
 [99] "my_study_og_098_1750928028_attrition"       "my_study_og_098_1750928028_codelist"       
[101] "my_study_og_098_1750928028_set"             "my_study_og_117_1750928033"                
[103] "my_study_og_118_1750928033"                 "my_study_og_118_1750928033_attrition"      
[105] "my_study_og_118_1750928033_codelist"        "my_study_og_118_1750928033_set"            
[107] "my_study_og_137_1750928038"                 "my_study_og_138_1750928038"                
[109] "my_study_og_138_1750928038_attrition"       "my_study_og_138_1750928038_codelist"       
[111] "my_study_og_138_1750928038_set"             "my_study_og_176_1750928057"                
[113] "my_study_og_177_1750928057"                 "my_study_og_177_1750928057_attrition"      
[115] "my_study_og_177_1750928057_codelist"        "my_study_og_177_1750928057_set"            
[117] "my_study_og_196_1750928061"                 "my_study_og_197_1750928061"                
[119] "my_study_og_197_1750928061_attrition"       "my_study_og_197_1750928061_codelist"       
[121] "my_study_og_197_1750928061_set"             "my_study_statins"                          
[123] "my_study_statins_attrition"                 "my_study_statins_codelist"                 
[125] "my_study_statins_set"                       "note"                                      
[127] "note_nlp"                                   "observation"                               
[129] "observation_period"                         "payer_plan_period"                         
[131] "person"                                     "procedure_occurrence"                      
[133] "provider"                                   "relationship"                              
[135] "source_to_concept_map"                      "specimen"                                  
[137] "test_conditions"                            "test_conditions_attrition"                 
[139] "test_conditions_codelist"                   "test_conditions_set"                       
[141] "test_medications"                           "test_medications_attrition"                
[143] "test_medications_codelist"                  "test_medications_set"                      
[145] "test_new_sinusitis"                         "test_new_sinusitis_attrition"              
[147] "test_new_sinusitis_codelist"                "test_new_sinusitis_set"                    
[149] "test_sinusitis"                             "test_sinusitis_attrition"                  
[151] "test_sinusitis_codelist"                    "test_sinusitis_set"                        
[153] "visit_detail"                               "visit_occurrence"                          
[155] "vocabulary"                                 "og_001_1750928373"

Drop an existing table

Let’s drop also the other table that we created:

cdm <- dropSourceTable(cdm = cdm, name = starts_with("og_"))
cdm

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

• 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: -

Drop an existing table

listSourceTables(cdm)

  [1] "care_site"                                  "cdm_source"                                
  [3] "concept"                                    "concept_ancestor"                          
  [5] "concept_class"                              "concept_relationship"                      
  [7] "concept_synonym"                            "condition_era"                             
  [9] "condition_occurrence"                       "cost"                                      
 [11] "death"                                      "device_exposure"                           
 [13] "domain"                                     "dose_era"                                  
 [15] "drug_era"                                   "drug_exposure"                             
 [17] "drug_strength"                              "fact_relationship"                         
 [19] "location"                                   "measurement"                               
 [21] "metadata"                                   "my_study_age_cohort"                       
 [23] "my_study_age_cohort_attrition"              "my_study_age_cohort_codelist"              
 [25] "my_study_age_cohort_set"                    "my_study_aspirin"                          
 [27] "my_study_aspirin_attrition"                 "my_study_aspirin_codelist"                 
 [29] "my_study_aspirin_last"                      "my_study_aspirin_last_attrition"           
 [31] "my_study_aspirin_last_codelist"             "my_study_aspirin_last_set"                 
 [33] "my_study_aspirin_set"                       "my_study_aspirin_strata"                   
 [35] "my_study_aspirin_strata_attrition"          "my_study_aspirin_strata_codelist"          
 [37] "my_study_aspirin_strata_set"                "my_study_diclofenac"                       
 [39] "my_study_diclofenac_attrition"              "my_study_diclofenac_codelist"              
 [41] "my_study_diclofenac_set"                    "my_study_ibuprofen"                        
 [43] "my_study_ibuprofen_attrition"               "my_study_ibuprofen_codelist"               
 [45] "my_study_ibuprofen_death"                   "my_study_ibuprofen_death_attrition"        
 [47] "my_study_ibuprofen_death_codelist"          "my_study_ibuprofen_death_set"              
 [49] "my_study_ibuprofen_set"                     "my_study_ibuprofen_strata"                 
 [51] "my_study_ibuprofen_strata_attrition"        "my_study_ibuprofen_strata_codelist"        
 [53] "my_study_ibuprofen_strata_set"              "my_study_ibuprofen_years"                  
 [55] "my_study_ibuprofen_years_attrition"         "my_study_ibuprofen_years_codelist"         
 [57] "my_study_ibuprofen_years_set"               "my_study_intersection"                     
 [59] "my_study_intersection_attrition"            "my_study_intersection_codelist"            
 [61] "my_study_intersection_set"                  "my_study_medications"                      
 [63] "my_study_medications_attrition"             "my_study_medications_codelist"             
 [65] "my_study_medications_collapsed"             "my_study_medications_collapsed_attrition"  
 [67] "my_study_medications_collapsed_codelist"    "my_study_medications_collapsed_set"        
 [69] "my_study_medications_requirement"           "my_study_medications_requirement_attrition"
 [71] "my_study_medications_requirement_codelist"  "my_study_medications_requirement_set"      
 [73] "my_study_medications_set"                   "my_study_medications_trimmed"              
 [75] "my_study_medications_trimmed_attrition"     "my_study_medications_trimmed_codelist"     
 [77] "my_study_medications_trimmed_set"           "my_study_mi"                               
 [79] "my_study_mi_attrition"                      "my_study_mi_codelist"                      
 [81] "my_study_mi_set"                            "my_study_og_028_1750928000"                
 [83] "my_study_og_029_1750928000"                 "my_study_og_029_1750928000_attrition"      
 [85] "my_study_og_029_1750928000_codelist"        "my_study_og_029_1750928000_set"            
 [87] "my_study_og_057_1750928015"                 "my_study_og_058_1750928015"                
 [89] "my_study_og_058_1750928015_attrition"       "my_study_og_058_1750928015_codelist"       
 [91] "my_study_og_058_1750928015_set"             "my_study_og_077_1750928021"                
 [93] "my_study_og_078_1750928021"                 "my_study_og_078_1750928021_attrition"      
 [95] "my_study_og_078_1750928021_codelist"        "my_study_og_078_1750928021_set"            
 [97] "my_study_og_097_1750928028"                 "my_study_og_098_1750928028"                
 [99] "my_study_og_098_1750928028_attrition"       "my_study_og_098_1750928028_codelist"       
[101] "my_study_og_098_1750928028_set"             "my_study_og_117_1750928033"                
[103] "my_study_og_118_1750928033"                 "my_study_og_118_1750928033_attrition"      
[105] "my_study_og_118_1750928033_codelist"        "my_study_og_118_1750928033_set"            
[107] "my_study_og_137_1750928038"                 "my_study_og_138_1750928038"                
[109] "my_study_og_138_1750928038_attrition"       "my_study_og_138_1750928038_codelist"       
[111] "my_study_og_138_1750928038_set"             "my_study_og_176_1750928057"                
[113] "my_study_og_177_1750928057"                 "my_study_og_177_1750928057_attrition"      
[115] "my_study_og_177_1750928057_codelist"        "my_study_og_177_1750928057_set"            
[117] "my_study_og_196_1750928061"                 "my_study_og_197_1750928061"                
[119] "my_study_og_197_1750928061_attrition"       "my_study_og_197_1750928061_codelist"       
[121] "my_study_og_197_1750928061_set"             "my_study_statins"                          
[123] "my_study_statins_attrition"                 "my_study_statins_codelist"                 
[125] "my_study_statins_set"                       "note"                                      
[127] "note_nlp"                                   "observation"                               
[129] "observation_period"                         "payer_plan_period"                         
[131] "person"                                     "procedure_occurrence"                      
[133] "provider"                                   "relationship"                              
[135] "source_to_concept_map"                      "specimen"                                  
[137] "test_conditions"                            "test_conditions_attrition"                 
[139] "test_conditions_codelist"                   "test_conditions_set"                       
[141] "test_medications"                           "test_medications_attrition"                
[143] "test_medications_codelist"                  "test_medications_set"                      
[145] "test_new_sinusitis"                         "test_new_sinusitis_attrition"              
[147] "test_new_sinusitis_codelist"                "test_new_sinusitis_set"                    
[149] "test_sinusitis"                             "test_sinusitis_attrition"                  
[151] "test_sinusitis_codelist"                    "test_sinusitis_set"                        
[153] "visit_detail"                               "visit_occurrence"                          
[155] "vocabulary"

Insert a table

Let’s say we have a local tibble and we want to insert it in the cdm:

cdm <- insertTable(cdm = cdm, name = "my_test_table", table = cars)
cdm

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

• 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: my_test_table

Insert a table

listSourceTables(cdm)

  [1] "care_site"                                  "cdm_source"                                
  [3] "concept"                                    "concept_ancestor"                          
  [5] "concept_class"                              "concept_relationship"                      
  [7] "concept_synonym"                            "condition_era"                             
  [9] "condition_occurrence"                       "cost"                                      
 [11] "death"                                      "device_exposure"                           
 [13] "domain"                                     "dose_era"                                  
 [15] "drug_era"                                   "drug_exposure"                             
 [17] "drug_strength"                              "fact_relationship"                         
 [19] "location"                                   "measurement"                               
 [21] "metadata"                                   "my_study_age_cohort"                       
 [23] "my_study_age_cohort_attrition"              "my_study_age_cohort_codelist"              
 [25] "my_study_age_cohort_set"                    "my_study_aspirin"                          
 [27] "my_study_aspirin_attrition"                 "my_study_aspirin_codelist"                 
 [29] "my_study_aspirin_last"                      "my_study_aspirin_last_attrition"           
 [31] "my_study_aspirin_last_codelist"             "my_study_aspirin_last_set"                 
 [33] "my_study_aspirin_set"                       "my_study_aspirin_strata"                   
 [35] "my_study_aspirin_strata_attrition"          "my_study_aspirin_strata_codelist"          
 [37] "my_study_aspirin_strata_set"                "my_study_diclofenac"                       
 [39] "my_study_diclofenac_attrition"              "my_study_diclofenac_codelist"              
 [41] "my_study_diclofenac_set"                    "my_study_ibuprofen"                        
 [43] "my_study_ibuprofen_attrition"               "my_study_ibuprofen_codelist"               
 [45] "my_study_ibuprofen_death"                   "my_study_ibuprofen_death_attrition"        
 [47] "my_study_ibuprofen_death_codelist"          "my_study_ibuprofen_death_set"              
 [49] "my_study_ibuprofen_set"                     "my_study_ibuprofen_strata"                 
 [51] "my_study_ibuprofen_strata_attrition"        "my_study_ibuprofen_strata_codelist"        
 [53] "my_study_ibuprofen_strata_set"              "my_study_ibuprofen_years"                  
 [55] "my_study_ibuprofen_years_attrition"         "my_study_ibuprofen_years_codelist"         
 [57] "my_study_ibuprofen_years_set"               "my_study_intersection"                     
 [59] "my_study_intersection_attrition"            "my_study_intersection_codelist"            
 [61] "my_study_intersection_set"                  "my_study_medications"                      
 [63] "my_study_medications_attrition"             "my_study_medications_codelist"             
 [65] "my_study_medications_collapsed"             "my_study_medications_collapsed_attrition"  
 [67] "my_study_medications_collapsed_codelist"    "my_study_medications_collapsed_set"        
 [69] "my_study_medications_requirement"           "my_study_medications_requirement_attrition"
 [71] "my_study_medications_requirement_codelist"  "my_study_medications_requirement_set"      
 [73] "my_study_medications_set"                   "my_study_medications_trimmed"              
 [75] "my_study_medications_trimmed_attrition"     "my_study_medications_trimmed_codelist"     
 [77] "my_study_medications_trimmed_set"           "my_study_mi"                               
 [79] "my_study_mi_attrition"                      "my_study_mi_codelist"                      
 [81] "my_study_mi_set"                            "my_study_og_028_1750928000"                
 [83] "my_study_og_029_1750928000"                 "my_study_og_029_1750928000_attrition"      
 [85] "my_study_og_029_1750928000_codelist"        "my_study_og_029_1750928000_set"            
 [87] "my_study_og_057_1750928015"                 "my_study_og_058_1750928015"                
 [89] "my_study_og_058_1750928015_attrition"       "my_study_og_058_1750928015_codelist"       
 [91] "my_study_og_058_1750928015_set"             "my_study_og_077_1750928021"                
 [93] "my_study_og_078_1750928021"                 "my_study_og_078_1750928021_attrition"      
 [95] "my_study_og_078_1750928021_codelist"        "my_study_og_078_1750928021_set"            
 [97] "my_study_og_097_1750928028"                 "my_study_og_098_1750928028"                
 [99] "my_study_og_098_1750928028_attrition"       "my_study_og_098_1750928028_codelist"       
[101] "my_study_og_098_1750928028_set"             "my_study_og_117_1750928033"                
[103] "my_study_og_118_1750928033"                 "my_study_og_118_1750928033_attrition"      
[105] "my_study_og_118_1750928033_codelist"        "my_study_og_118_1750928033_set"            
[107] "my_study_og_137_1750928038"                 "my_study_og_138_1750928038"                
[109] "my_study_og_138_1750928038_attrition"       "my_study_og_138_1750928038_codelist"       
[111] "my_study_og_138_1750928038_set"             "my_study_og_176_1750928057"                
[113] "my_study_og_177_1750928057"                 "my_study_og_177_1750928057_attrition"      
[115] "my_study_og_177_1750928057_codelist"        "my_study_og_177_1750928057_set"            
[117] "my_study_og_196_1750928061"                 "my_study_og_197_1750928061"                
[119] "my_study_og_197_1750928061_attrition"       "my_study_og_197_1750928061_codelist"       
[121] "my_study_og_197_1750928061_set"             "my_study_statins"                          
[123] "my_study_statins_attrition"                 "my_study_statins_codelist"                 
[125] "my_study_statins_set"                       "my_test_table"                             
[127] "note"                                       "note_nlp"                                  
[129] "observation"                                "observation_period"                        
[131] "payer_plan_period"                          "person"                                    
[133] "procedure_occurrence"                       "provider"                                  
[135] "relationship"                               "source_to_concept_map"                     
[137] "specimen"                                   "test_conditions"                           
[139] "test_conditions_attrition"                  "test_conditions_codelist"                  
[141] "test_conditions_set"                        "test_medications"                          
[143] "test_medications_attrition"                 "test_medications_codelist"                 
[145] "test_medications_set"                       "test_new_sinusitis"                        
[147] "test_new_sinusitis_attrition"               "test_new_sinusitis_codelist"               
[149] "test_new_sinusitis_set"                     "test_sinusitis"                            
[151] "test_sinusitis_attrition"                   "test_sinusitis_codelist"                   
[153] "test_sinusitis_set"                         "visit_detail"                              
[155] "visit_occurrence"                           "vocabulary"

cdm$my_test_table

# Source:   table<my_test_table> [?? x 2]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//home/runner/work/RealWorldEvidenceSummerSchool2025/RealWorldEvidenceSummerSchool2025/GiBleed.duckdb]
   speed  dist
   <dbl> <dbl>
 1     4     2
 2     4    10
 3     7     4
 4     7    22
 5     8    16
 6     9    10
 7    10    18
 8    10    26
 9    10    34
10    11    17
# ℹ more rows

Use a prefix

It is VERY IMPORTANT that when we create the cdm object we use a prefix:

cdm <- cdmFromCon(
  con = con, 
  cdmSchema = "main", 
  writeSchema = "main", 
  writePrefix = "my_prefix_"
)
cdm

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

• 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: -

Use a prefix

Now when we create a new table the prefix will be automatically added:

cdm <- insertTable(cdm = cdm, name = "my_test_table", table = cars)
cdm

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

• 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: my_test_table

Use a prefix

listSourceTables(cdm = cdm)

[1] "my_test_table"

cdm$my_test_table

# Source:   table<my_prefix_my_test_table> [?? x 2]
# Database: DuckDB v1.3.1 [unknown@Linux 6.11.0-1015-azure:R 4.5.1//home/runner/work/RealWorldEvidenceSummerSchool2025/RealWorldEvidenceSummerSchool2025/GiBleed.duckdb]
   speed  dist
   <dbl> <dbl>
 1     4     2
 2     4    10
 3     7     4
 4     7    22
 5     8    16
 6     9    10
 7    10    18
 8    10    26
 9    10    34
10    11    17
# ℹ more rows

Use a prefix

DO NOT use the prefix to drop tables, you only care about the prefix at the connection stage!

cdm <- dropSourceTable(cdm = cdm, name = "my_prefix_my_test_table")
listSourceTables(cdm = cdm)

[1] "my_test_table"

Use a prefix

cdm

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

• 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: my_test_table

Use a prefix

DO NOT use the prefix to drop tables, you only care about the prefix at the connection stage!

cdm <- dropSourceTable(cdm = cdm, name = "my_test_table")
listSourceTables(cdm = cdm)

character(0)

Use a prefix

cdm

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

• 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: -

Consistency rules

We use compute() to compute the result into a temporary (temporary = TRUE) or permanent (temporary = FALSE) table.

If it is a temporary table we can assign assign it to where I want for example:

cdm$my_custom_name <- cdm$person |> 
  compute()

If it is a permanent table we can only assign it to the same name:

error:

cdm$my_custom_name <- cdm$person |> 
  compute(name = "not_my_custom_name")

Error in `[[<-`:
! You can't assign a table named not_my_custom_name to my_custom_name. Please use compute to change table name.

no error:

cdm$my_custom_name <- cdm$person |> 
  compute(name = "my_custom_name")

Consistency rules

Omop names are reserved words, e.g. we can not assign a table that is not the person table to cdm$person.

cdm$person <- cdm$drug_exposure |> 
  compute(name = "person", temporary = FALSE)

Error in `newOmopTable()`:
! gender_concept_id, year_of_birth, race_concept_id and ethnicity_concept_id are not present in table person

cdm$drug_exposure <- cdm$drug_exposure |> 
  rename("my_id" = "person_id") |> 
  compute(name = "drug_exposure", temporary = FALSE)

Error in `newOmopTable()`:
! person_id is not present in table drug_exposure

Result model

The output of our analyses has been standardised to the <summarised_result> object.

x <- mockSummarisedResult()
x

# A tibble: 126 × 13
   result_id cdm_name group_name  group_level strata_name       strata_level  variable_name variable_level estimate_name
       <int> <chr>    <chr>       <chr>       <chr>             <chr>         <chr>         <chr>          <chr>        
 1         1 mock     cohort_name cohort1     overall           overall       number subje… <NA>           count        
 2         1 mock     cohort_name cohort1     age_group &&& sex <40 &&& Male  number subje… <NA>           count        
 3         1 mock     cohort_name cohort1     age_group &&& sex >=40 &&& Male number subje… <NA>           count        
 4         1 mock     cohort_name cohort1     age_group &&& sex <40 &&& Fema… number subje… <NA>           count        
 5         1 mock     cohort_name cohort1     age_group &&& sex >=40 &&& Fem… number subje… <NA>           count        
 6         1 mock     cohort_name cohort1     sex               Male          number subje… <NA>           count        
 7         1 mock     cohort_name cohort1     sex               Female        number subje… <NA>           count        
 8         1 mock     cohort_name cohort1     age_group         <40           number subje… <NA>           count        
 9         1 mock     cohort_name cohort1     age_group         >=40          number subje… <NA>           count        
10         1 mock     cohort_name cohort2     overall           overall       number subje… <NA>           count        
# ℹ 116 more rows
# ℹ 4 more variables: estimate_type <chr>, estimate_value <chr>, additional_name <chr>, additional_level <chr>

class(x)

[1] "summarised_result" "omop_result"       "tbl_df"            "tbl"               "data.frame"

`<summarised_result>`

The summarised result object contains 13 columns:

glimpse(x)

Rows: 126
Columns: 13
$ result_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, 1, …
$ cdm_name         <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock…
$ group_name       <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "co…
$ group_level      <chr> "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "coho…
$ strata_name      <chr> "overall", "age_group &&& sex", "age_group &&& sex", "age_group &&& sex", "age_group &&& sex"…
$ strata_level     <chr> "overall", "<40 &&& Male", ">=40 &&& Male", "<40 &&& Female", ">=40 &&& Female", "Male", "Fem…
$ variable_name    <chr> "number subjects", "number subjects", "number subjects", "number subjects", "number subjects"…
$ variable_level   <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…
$ estimate_name    <chr> "count", "count", "count", "count", "count", "count", "count", "count", "count", "count", "co…
$ estimate_type    <chr> "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "inte…
$ estimate_value   <chr> "2655087", "3721239", "5728534", "9082078", "2016819", "8983897", "9446753", "6607978", "6291…
$ additional_name  <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ additional_level <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…

`<summarised_result>`

And have some associated settings

settings(x)

# A tibble: 1 × 8
  result_id result_type            package_name   package_version group       strata           additional min_cell_count
      <int> <chr>                  <chr>          <chr>           <chr>       <chr>            <chr>      <chr>         
1         1 mock_summarised_result visOmopResults 1.1.1           cohort_name age_group &&& s… ""         0

tidy the result object

x |>
  splitGroup() |>
  glimpse()

Rows: 126
Columns: 12
$ result_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, 1, …
$ cdm_name         <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock…
$ cohort_name      <chr> "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "coho…
$ strata_name      <chr> "overall", "age_group &&& sex", "age_group &&& sex", "age_group &&& sex", "age_group &&& sex"…
$ strata_level     <chr> "overall", "<40 &&& Male", ">=40 &&& Male", "<40 &&& Female", ">=40 &&& Female", "Male", "Fem…
$ variable_name    <chr> "number subjects", "number subjects", "number subjects", "number subjects", "number subjects"…
$ variable_level   <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…
$ estimate_name    <chr> "count", "count", "count", "count", "count", "count", "count", "count", "count", "count", "co…
$ estimate_type    <chr> "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "inte…
$ estimate_value   <chr> "2655087", "3721239", "5728534", "9082078", "2016819", "8983897", "9446753", "6607978", "6291…
$ additional_name  <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ additional_level <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…

tidy the result object

x |>
  splitStrata() |>
  glimpse()

Rows: 126
Columns: 13
$ result_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, 1, …
$ cdm_name         <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock…
$ group_name       <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "co…
$ group_level      <chr> "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "coho…
$ age_group        <chr> "overall", "<40", ">=40", "<40", ">=40", "overall", "overall", "<40", ">=40", "overall", "<40…
$ sex              <chr> "overall", "Male", "Male", "Female", "Female", "Male", "Female", "overall", "overall", "overa…
$ variable_name    <chr> "number subjects", "number subjects", "number subjects", "number subjects", "number subjects"…
$ variable_level   <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…
$ estimate_name    <chr> "count", "count", "count", "count", "count", "count", "count", "count", "count", "count", "co…
$ estimate_type    <chr> "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "inte…
$ estimate_value   <chr> "2655087", "3721239", "5728534", "9082078", "2016819", "8983897", "9446753", "6607978", "6291…
$ additional_name  <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ additional_level <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…

tidy the result object

x |>
  splitAdditional() |>
  glimpse()

Rows: 126
Columns: 11
$ result_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, 1, 1,…
$ cdm_name       <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock",…
$ group_name     <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "coho…
$ group_level    <chr> "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort…
$ strata_name    <chr> "overall", "age_group &&& sex", "age_group &&& sex", "age_group &&& sex", "age_group &&& sex", …
$ strata_level   <chr> "overall", "<40 &&& Male", ">=40 &&& Male", "<40 &&& Female", ">=40 &&& Female", "Male", "Femal…
$ variable_name  <chr> "number subjects", "number subjects", "number subjects", "number subjects", "number subjects", …
$ variable_level <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ estimate_name  <chr> "count", "count", "count", "count", "count", "count", "count", "count", "count", "count", "coun…
$ estimate_type  <chr> "integer", "integer", "integer", "integer", "integer", "integer", "integer", "integer", "intege…
$ estimate_value <chr> "2655087", "3721239", "5728534", "9082078", "2016819", "8983897", "9446753", "6607978", "629114…

tidy the result object

x |>
  pivotEstimates() |>
  glimpse()

Rows: 72
Columns: 14
$ result_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, 1, …
$ cdm_name         <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock…
$ group_name       <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "co…
$ group_level      <chr> "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "coho…
$ strata_name      <chr> "overall", "age_group &&& sex", "age_group &&& sex", "age_group &&& sex", "age_group &&& sex"…
$ strata_level     <chr> "overall", "<40 &&& Male", ">=40 &&& Male", "<40 &&& Female", ">=40 &&& Female", "Male", "Fem…
$ variable_name    <chr> "number subjects", "number subjects", "number subjects", "number subjects", "number subjects"…
$ variable_level   <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…
$ additional_name  <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ additional_level <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ count            <int> 2655087, 3721239, 5728534, 9082078, 2016819, 8983897, 9446753, 6607978, 6291140, 617863, 2059…
$ mean             <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 38.003518, 77.744522,…
$ sd               <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 7.942399, 1.079436, 7…
$ percentage       <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, N…

tidy the result object

x |>
  tidy() |>
  glimpse()

Rows: 72
Columns: 10
$ cdm_name       <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock",…
$ cohort_name    <chr> "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort1", "cohort…
$ age_group      <chr> "overall", "<40", ">=40", "<40", ">=40", "overall", "overall", "<40", ">=40", "overall", "<40",…
$ sex            <chr> "overall", "Male", "Male", "Female", "Female", "Male", "Female", "overall", "overall", "overall…
$ variable_name  <chr> "number subjects", "number subjects", "number subjects", "number subjects", "number subjects", …
$ variable_level <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…
$ count          <int> 2655087, 3721239, 5728534, 9082078, 2016819, 8983897, 9446753, 6607978, 6291140, 617863, 205974…
$ mean           <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 38.003518, 77.744522, 9…
$ sd             <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, 7.942399, 1.079436, 7.2…
$ percentage     <dbl> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA,…

filtering the result object

x |>
  filter(strata_name == "overall") |>
  glimpse()

Rows: 14
Columns: 13
$ result_id        <int> 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1
$ cdm_name         <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock…
$ group_name       <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "co…
$ group_level      <chr> "cohort1", "cohort2", "cohort1", "cohort2", "cohort1", "cohort2", "cohort1", "cohort2", "coho…
$ strata_name      <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ strata_level     <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ variable_name    <chr> "number subjects", "number subjects", "age", "age", "age", "age", "Medications", "Medications…
$ variable_level   <chr> NA, NA, NA, NA, NA, NA, "Amoxiciline", "Amoxiciline", "Amoxiciline", "Amoxiciline", "Ibuprofe…
$ estimate_name    <chr> "count", "count", "mean", "mean", "sd", "sd", "count", "count", "percentage", "percentage", "…
$ estimate_type    <chr> "integer", "integer", "numeric", "numeric", "numeric", "numeric", "integer", "integer", "perc…
$ estimate_value   <chr> "2655087", "617863", "38.0035179434344", "38.2387957070023", "7.9423986072652", "7.8935623168…
$ additional_name  <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ additional_level <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…

filtering the result object

x |>
  filterStrata(sex == "Female") |>
  glimpse()

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, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, …
$ cdm_name         <chr> "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock", "mock…
$ group_name       <chr> "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "cohort_name", "co…
$ group_level      <chr> "cohort1", "cohort1", "cohort1", "cohort2", "cohort2", "cohort2", "cohort1", "cohort1", "coho…
$ strata_name      <chr> "age_group &&& sex", "age_group &&& sex", "sex", "age_group &&& sex", "age_group &&& sex", "s…
$ strata_level     <chr> "<40 &&& Female", ">=40 &&& Female", "Female", "<40 &&& Female", ">=40 &&& Female", "Female",…
$ variable_name    <chr> "number subjects", "number subjects", "number subjects", "number subjects", "number subjects"…
$ variable_level   <chr> NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, NA, "Amoxiciline", "Amoxi…
$ estimate_name    <chr> "count", "count", "count", "count", "count", "count", "mean", "mean", "mean", "mean", "mean",…
$ estimate_type    <chr> "integer", "integer", "integer", "integer", "integer", "integer", "numeric", "numeric", "nume…
$ estimate_value   <chr> "9082078", "2016819", "9446753", "6870228", "3841037", "4976992", "21.2142521282658", "65.167…
$ additional_name  <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…
$ additional_level <chr> "overall", "overall", "overall", "overall", "overall", "overall", "overall", "overall", "over…

Other objects and classes

<codelist>, <codelist_with_details>, <conceptSetExpression>
<cohort_table>
<achilles_table>

omock

👉 Packages website
👉 CRAN link
👉 Manual

📧 mike.du@ndorms.ox.ac.uk

omopgenerics

👉 Packages website
👉 CRAN link
👉 Manual

📧 marti.catalasabate@ndorms.ox.ac.uk

visOmopResults

👉 Packages website
👉 CRAN link
👉 Manual

📧 nuria.mercadebesora@ndorms.ox.ac.uk

CDMConnector

👉 Packages website
👉 CRAN link
👉 Manual

📧 marti.catalasabate@ndorms.ox.ac.uk

OMOP and CDMConnector

Introduction: The OMOP Common Data Model

Standarisation of the data format

Mapping a database to the OMOP CDM

Mapping a database to the OMOP CDM

Mapping a database to the OMOP CDM

Standarisation of the vocabularies

Example of mapping

Example of mapping

Example of mapping

Example of mapping

More details

Let’s start coding

Create a mock reference

<cdm_reference> object

<cdm_reference> object

<cdm_table> object

<cdm_table> object

<cdm_table> object

<cdm_table> object

Connecting to a database from R (the DBI package)

Connecting to a database from R (the DBI package)

Databases organisation

Create a local database

Let’s create our first cdm reference

Access to tables of the cdm reference

Read tables in GiBleed

Operation with tidyverse

Database name

Create a new table

Create a new table

Create a new table

Drop an existing table

Drop an existing table

Drop an existing table

Drop an existing table

Insert a table

Insert a table

Use a prefix

Use a prefix

Use a prefix

Use a prefix

Use a prefix

Use a prefix

Use a prefix

Consistency rules

Consistency rules

Result model

<summarised_result>

<summarised_result>

tidy the result object

tidy the result object

tidy the result object

tidy the result object

tidy the result object

filtering the result object

filtering the result object

Other objects and classes

omock

omopgenerics

visOmopResults

CDMConnector

`<cdm_reference>` object

`<cdm_reference>` object

`<cdm_table>` object

`<cdm_table>` object

`<cdm_table>` object

`<cdm_table>` object

`<summarised_result>`

`<summarised_result>`