In this vignette, we will explore the OmopSketch functions
designed to provide information about the number of counts of concepts
in tables. Specifically, there are two key functions that facilitate
this, summariseConceptIdCounts() and
tableConceptIdCounts(). The former one creates a summary
statistics results with the number of counts per each concept in the
clinical table, and the latter one displays the result in a table.
Let’s see an example of the previous functions. To start with, we
will load essential packages and create a mock cdm using
mockOmopSketch().
library(duckdb)
library(OmopSketch)
library(dplyr)
cdm <- mockOmopSketch()
cdm
#>
#> ── # OMOP CDM reference (duckdb) of mockOmopSketch ─────────────────────────────
#> • omop tables: person, observation_period, cdm_source, concept, vocabulary,
#> concept_relationship, concept_synonym, concept_ancestor, drug_strength,
#> condition_occurrence, death, drug_exposure, measurement, observation,
#> procedure_occurrence, visit_occurrence, device_exposure
#> • cohort tables: -
#> • achilles tables: -
#> • other tables: -We now use the summariseConceptIdCounts() function from
the OmopSketch package to retrieve counts for each concept id and name,
as well as for each source concept id and name, across the clinical
tables.
summariseConceptIdCounts(cdm, omopTableName = "drug_exposure") |>
select(group_level, variable_name, variable_level, estimate_name, estimate_value, additional_name, additional_level) |>
glimpse()
#> Rows: 216
#> Columns: 7
#> $ group_level <chr> "drug_exposure", "drug_exposure", "drug_exposure", "d…
#> $ variable_name <chr> "celecoxib 200 MG Oral Capsule [Celebrex]", "meningoc…
#> $ variable_level <chr> "1118088", "40213180", "40213296", "933724", "1102527…
#> $ estimate_name <chr> "count_records", "count_records", "count_records", "c…
#> $ estimate_value <chr> "100", "100", "100", "100", "100", "100", "100", "100…
#> $ additional_name <chr> "source_concept_id &&& source_concept_name", "source_…
#> $ additional_level <chr> "0 &&& No matching concept", "0 &&& No matching conce…By default, the function returns the number of records
(estimate_name == "count_records") for each concept_id. To
include counts by person, you can set the countBy argument
to "person" or to c("record", "person") to
obtain both record and person counts.
summariseConceptIdCounts(cdm,
omopTableName = "drug_exposure",
countBy = c("record", "person")
) |>
select( variable_name, estimate_name, estimate_value)
#> # A tibble: 432 × 3
#> variable_name estimate_name estimate_value
#> <chr> <chr> <chr>
#> 1 Memantine count_records 100
#> 2 Memantine count_subjects 66
#> 3 zoster vaccine, live count_records 100
#> 4 zoster vaccine, live count_subjects 64
#> 5 Terfenadine count_records 100
#> 6 Terfenadine count_subjects 65
#> 7 fexofenadine count_records 100
#> 8 fexofenadine count_subjects 60
#> 9 Loratadine count_records 100
#> 10 Loratadine count_subjects 60
#> # ℹ 422 more rowsFurther stratification can be applied using the
interval, sex, and ageGroup
arguments. The interval argument supports “overall” (no time
stratification), “years”, “quarters”, or “months”.
summariseConceptIdCounts(cdm,
omopTableName = "condition_occurrence",
countBy = "person",
interval = "years",
sex = TRUE,
ageGroup = list("<=50" = c(0, 50), ">50" = c(51, Inf))
) |>
select(group_level, strata_level, variable_name, estimate_name, additional_level) |>
glimpse()
#> Rows: 17,078
#> Columns: 5
#> $ group_level <chr> "condition_occurrence", "condition_occurrence", "cond…
#> $ strata_level <chr> "overall", "overall", "overall", "overall", "overall"…
#> $ variable_name <chr> "Escherichia coli urinary tract infection", "Gastroin…
#> $ estimate_name <chr> "count_subjects", "count_subjects", "count_subjects",…
#> $ additional_level <chr> "0 &&& No matching concept", "0 &&& No matching conce…We can also filter the clinical table to a specific time window by setting the dateRange argument.
summarisedResult <- summariseConceptIdCounts(cdm,
omopTableName = "condition_occurrence",
dateRange = as.Date(c("1990-01-01", "2010-01-01")))
summarisedResult |>
omopgenerics::settings()|>
glimpse()
#> Rows: 1
#> Columns: 10
#> $ result_id <int> 1
#> $ result_type <chr> "summarise_concept_id_counts"
#> $ package_name <chr> "OmopSketch"
#> $ package_version <chr> "0.5.0"
#> $ group <chr> "omop_table"
#> $ strata <chr> ""
#> $ additional <chr> "source_concept_id &&& source_concept_name"
#> $ min_cell_count <chr> "0"
#> $ study_period_end <chr> "2010-01-01"
#> $ study_period_start <chr> "1990-01-01"Finally, you can summarise concept counts on a subset of records by
specifying the sample argument.
summariseConceptIdCounts(cdm,
omopTableName = "condition_occurrence",
sample = 50) |>
select(group_level, variable_name, estimate_name) |>
glimpse()
#> Rows: 37
#> Columns: 3
#> $ group_level <chr> "condition_occurrence", "condition_occurrence", "conditi…
#> $ variable_name <chr> "Laceration of hand", "Acute bacterial sinusitis", "Frac…
#> $ estimate_name <chr> "count_records", "count_records", "count_records", "coun…Finally, concept counts can be visualised using
tableConceptIdCounts(). By default, it generates an
interactive reactable
table, but DT datatables are
also supported.
result <- summariseConceptIdCounts(cdm,
omopTableName = "measurement",
countBy = "record"
)
tableConceptIdCounts(result, type = "reactable")tableConceptIdCounts(result, type = "datatable")The display argument in tableConceptIdCounts() controls
which concept counts are shown. Available options include
display = "overall". It is the default option and it shows
both standard and source concept counts.
tableConceptIdCounts(result, display = "overall")If display = "standard" the table shows only
standard concept_id and concept_name counts.
tableConceptIdCounts(result, display = "standard")If display = "source" the table shows only
source concept_id and concept_name counts.
tableConceptIdCounts(result, display = "source")
#> Warning: Values from `estimate_value` are not uniquely identified; output will contain
#> list-cols.
#> • Use `values_fn = list` to suppress this warning.
#> • Use `values_fn = {summary_fun}` to summarise duplicates.
#> • Use the following dplyr code to identify duplicates.
#> {data} |>
#> dplyr::summarise(n = dplyr::n(), .by = c(cdm_name, group_level,
#> source_concept_name, source_concept_id, result_id, group_name, estimate_type,
#> estimate_name)) |>
#> dplyr::filter(n > 1L)If display = "missing source" the table shows only
counts for concept ids that are missing a corresponding source concept
id.
tableConceptIdCounts(result, display = "missing source")If display = "missing standard" the table shows only
counts for source concept ids that are missing a mapped standard concept
id.
tableConceptIdCounts(result, display = "missing standard")
#> Warning: `result` does not contain any `summarise_concept_id_counts` data.You can use the tableTopConceptCounts() function to
display the most frequent concepts in a OMOP CDM table in formatted
table. By default, the function returns a gt table, but you can also choose
from other output formats, including flextable, datatable, and reactable.
result <- summariseConceptIdCounts(cdm,
omopTableName = "drug_exposure",
countBy = "record"
)
tableTopConceptCounts(result, type = "gt")| Top |
Cdm name
|
|---|---|
| mockOmopSketch | |
| drug_exposure | |
| 1 | Standard: Memantine (701322) Source: No matching concept (0) 100 |
| 2 | Standard: zoster vaccine, live (40213260) Source: No matching concept (0) 100 |
| 3 | Standard: Terfenadine (1150836) Source: No matching concept (0) 100 |
| 4 | Standard: fexofenadine (1153428) Source: No matching concept (0) 100 |
| 5 | Standard: Loratadine (1107830) Source: No matching concept (0) 100 |
| 6 | Standard: pneumococcal conjugate vaccine, 13 valent (40213198) Source: No matching concept (0) 100 |
| 7 | Standard: Acetaminophen (1125315) Source: No matching concept (0) 100 |
| 8 | Standard: {28 (Norethindrone 0.35 MG Oral Tablet) } Pack [Errin 28 Day] (19133679) Source: No matching concept (0) 100 |
| 9 | Standard: 12 HR Cefaclor 500 MG Extended Release Oral Tablet (19074841) Source: No matching concept (0) 100 |
| 10 | Standard: Albuterol (1154343) Source: No matching concept (0) 100 |
By default, the function shows the top 10 concepts. You can change
this using the top argument:
tableTopConceptCounts(result, top = 5)| Top |
Cdm name
|
|---|---|
| mockOmopSketch | |
| drug_exposure | |
| 1 | Standard: Memantine (701322) Source: No matching concept (0) 100 |
| 2 | Standard: zoster vaccine, live (40213260) Source: No matching concept (0) 100 |
| 3 | Standard: Terfenadine (1150836) Source: No matching concept (0) 100 |
| 4 | Standard: fexofenadine (1153428) Source: No matching concept (0) 100 |
| 5 | Standard: Loratadine (1107830) Source: No matching concept (0) 100 |
If your summary includes both record and person counts, you must
specify which type to display using the countBy
argument:
result <- summariseConceptIdCounts(cdm,
omopTableName = "drug_exposure",
countBy = c("record", "person")
)
tableTopConceptCounts(result, countBy = "person")| Top |
Cdm name
|
|---|---|
| mockOmopSketch | |
| drug_exposure | |
| 1 | Standard: Fexofenadine hydrochloride 30 MG Oral Tablet (40223821) Source: No matching concept (0) 74 |
| 2 | Standard: dienogest (19054876) Source: No matching concept (0) 72 |
| 3 | Standard: {28 (Norethindrone 0.35 MG Oral Tablet) } Pack [Errin 28 Day] (19133679) Source: No matching concept (0) 69 |
| 4 | Standard: Hydrocortisone 10 MG/ML Topical Cream (19008572) Source: No matching concept (0) 69 |
| 5 | Standard: 1 ML medroxyprogesterone acetate 150 MG/ML Injection (40224805) Source: No matching concept (0) 69 |
| 6 | Standard: Isoflurane (782043) Source: No matching concept (0) 68 |
| 7 | Standard: Etonogestrel 68 MG Drug Implant (1519937) Source: No matching concept (0) 68 |
| 8 | Standard: Amoxicillin 500 MG Oral Tablet (19073188) Source: No matching concept (0) 68 |
| 9 | Standard: Astemizole 10 MG Oral Tablet (1150771) Source: No matching concept (0) 68 |
| 10 | Standard: Acetaminophen 21.7 MG/ML / Dextromethorphan Hydrobromide 1 MG/ML / doxylamine succinate 0.417 MG/ML Oral Solution (40229134) Source: No matching concept (0) 68 |