The goal of OmopSketch is to characterise and visualise an OMOP CDM instance to asses if it meets the necessary criteria to answer a specific clinical question and conduct a certain study.
You can install the development version of OmopSketch from GitHub with:
# install.packages("remotes")
remotes::install_github("OHDSI/OmopSketch")Let’s start by creating a cdm object using the Eunomia mock dataset:
library(duckdb)
#> Loading required package: DBI
library(CDMConnector)
library(dplyr, warn.conflicts = FALSE)
library(OmopSketch)
con <- dbConnect(duckdb(), eunomia_dir())
cdm <- cdmFromCon(con = con, cdmSchema = "main", writeSchema = "main")
#> Note: method with signature 'DBIConnection#Id' chosen for function 'dbExistsTable',
#> target signature 'duckdb_connection#Id'.
#> "duckdb_connection#ANY" would also be valid
cdm
#>
#> ── # OMOP CDM reference (duckdb) of Synthea synthetic health database ──────────
#> • 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: -We first create a snapshot of our database. This will allow us to track when the analysis has been conducted and capture details about the CDM version or the data release.
summariseOmopSnapshot(cdm) |>
tableOmopSnapshot(type = "flextable")
#> ! Results have not been suppressed.
Once we have collected the snapshot information, we can start
characteristing the clinical tables of the CDM. By using
summariseClinicalRecords() and
tableClinicalRecords(), we can easily visualise the main
characteristics of specific clinical tables.
summariseClinicalRecords(cdm, c("condition_occurrence", "drug_exposure")) |>
tableClinicalRecords(type = "flextable")
#> ℹ Summarising table counts
#> ℹ The following estimates will be computed:
#> → Start summary of data, at 2024-09-25 12:14:06.676817
#>
#> ✔ Summary finished, at 2024-09-25 12:14:06.815944
#> ℹ Summarising records per person
#> ℹ The following estimates will be computed:
#> • records_per_person: mean, sd, median, q25, q75, min, max
#> ! Table is collected to memory as not all requested estimates are supported on
#> the database side
#> → Start summary of data, at 2024-09-25 12:14:07.908258
#>
#> ✔ Summary finished, at 2024-09-25 12:14:07.955041
#> ℹ Summarising in_observation, standard, domain_id, and type information
#> ℹ Summarising table counts
#> ℹ The following estimates will be computed:
#> → Start summary of data, at 2024-09-25 12:14:11.725276
#>
#> ✔ Summary finished, at 2024-09-25 12:14:11.877293
#> ℹ Summarising records per person
#> ℹ The following estimates will be computed:
#> • records_per_person: mean, sd, median, q25, q75, min, max
#> ! Table is collected to memory as not all requested estimates are supported on
#> the database side
#> → Start summary of data, at 2024-09-25 12:14:12.808874
#>
#> ✔ Summary finished, at 2024-09-25 12:14:12.850686
#> ℹ Summarising in_observation, standard, domain_id, and type information
#> ! Results have not been suppressed.
We can also explore trends in the clinical table records over time.
summariseRecordCount(cdm, c("condition_occurrence", "drug_exposure")) |>
plotRecordCount(facet = "omop_table")
#> ! The following column type were changed:
#> • variable_level: from double to character
### Characterise the observation period After visualising the main
characteristics of our clinical tables, we can explore the observation
period details. OmopSketch provides several functions to have an
overwied of the dataset study period.
Using summariseInObservation() and
plotInObservation(), we can gather information on the
number of records per year.
summariseInObservation(cdm$observation_period, output = "records") |>
plotInObservation()
#> ! The following column type were changed:
#> • variable_level: from double to character
You can also visualise and explore the characteristics of the
observation period per each individual in the database using
summariseObservationPeriod().
summariseObservationPeriod(cdm$observation_period) |>
tableObservationPeriod(type = "flextable")
#> ! Results have not been suppressed.
Or if visualisation is prefered, you can easily build a histogram to explore how many participants have more than one observation period.
summariseObservationPeriod(cdm$observation_period) |>
plotObservationPeriod()
OmopSketch also provides functions to explore some of (or all) the concepts in the dataset.
acetaminophen <- c(1125315, 1127433, 1127078)
summariseConceptCounts(cdm, conceptId = list("acetaminophen" = acetaminophen)) |>
filter(estimate_name == "record_count") |>
plotConceptCounts()
#> ℹ Getting use of codes from acetaminophen
#> ! The following column type were changed:
#> • variable_name: from integer to character
Finally, OmopSketch can also help us to characterise the population at the start and end of the observation period.
summarisePopulationCharacteristics(cdm) |>
tablePopulationCharacteristics(type = "flextable")
#> Warning: ! 1 casted column in og_015_1727262876 (cohort_set) as do not match expected
#> column type:
#> • `cohort_definition_id` from numeric to integer
#> Warning: ! 1 column in og_015_1727262876 do not match expected column type:
#> • `cohort_definition_id` is numeric but expected integer
#> ! cohort columns will be reordered to match the expected order:
#> cohort_definition_id, subject_id, cohort_start_date, and cohort_end_date.
#> ℹ Building new trimmed cohort
#> Warning: ! 1 column in tmp_011_og_017_1727262877 do not match expected column type:
#> • `cohort_definition_id` is numeric but expected integer
#> Creating initial cohort
#> ! cohort columns will be reordered to match the expected order:
#> cohort_definition_id, subject_id, cohort_start_date, and cohort_end_date.
#> ! cohort columns will be reordered to match the expected order:
#> cohort_definition_id, subject_id, cohort_start_date, and cohort_end_date.
#> ✔ Cohort trimmed
#> ℹ adding demographics columns
#>
#> ℹ summarising data
#>
#> ✔ summariseCharacteristics finished!
#>
#> ! The following column type were changed:
#> • variable_name: from integer to character
#> ! Results have not been suppressed.| Variable name | Variable level | Estimate name | Database name |
|---|---|---|---|
| Synthea synthetic health database | |||
| Number records | - | N | 2,694 |
| Number subjects | - | N | 2,694 |
| Cohort start date | - | Median [Q25 - Q75] | 1961-03-18 [1950-07-13 - 1970-08-29] |
| Range | 1908-09-22 to 1986-11-03 | ||
| Cohort end date | - | Median [Q25 - Q75] | 2018-12-14 [2018-08-02 - 2019-04-06] |
| Range | 1945-07-20 to 2019-07-03 | ||
| Age at start | - | Median [Q25 - Q75] | 0 [0 - 0] |
| Mean (SD) | 0.00 (0.00) | ||
| Range | 0 to 0 | ||
| Age at end | - | Median [Q25 - Q75] | 57 [47 - 67] |
| Range | 31 to 110 | ||
| Sex | Female | N% | 1,373 (50.97) |
| Male | N% | 1,321 (49.03) | |
| Prior observation | - | Median [Q25 - Q75] | 0 [0 - 0] |
| Mean (SD) | 0.00 (0.00) | ||
| Range | 0 to 0 | ||
| Future observation | - | Median [Q25 - Q75] | 20,870 [17,494 - 24,701] |
| Mean (SD) | 21,601.60 (5,460.69) | ||
| Range | 11,396 to 40,348 |
As seen, OmopSketch offers multiple functionalities to provide a general overview of a database. Additionally, it includes more tools and arguments that allow for deeper exploration, helping to assess the database’s suitability for specific research studies. For further information, please refer to the vignettes.