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The PhenotypeR package helps us to assess the research-readiness of a set of cohorts we have defined. This assessment includes:
You can install PhenotypeR from CRAN:
install.packages("PhenotypeR")
Or you can install the development version from GitHub:
# install.packages("remotes")
::install_github("OHDSI/PhenotypeR") remotes
To illustrate the functionality of PhenotypeR, let’s create a cohort using the Eunomia Synpuf dataset. We’ll first load the required packages and create the cdm reference for the data.
library(dplyr)
library(CohortConstructor)
library(PhenotypeR)
# Connect to the database and create the cdm object
<- DBI::dbConnect(duckdb::duckdb(),
con ::eunomiaDir("synpuf-1k", "5.3"))
CDMConnector<- CDMConnector::cdmFromCon(con = con,
cdm cdmName = "Eunomia Synpuf",
cdmSchema = "main",
writeSchema = "main",
achillesSchema = "main")
Note that we’ve included achilles results in our cdm reference. Where we can we’ll use these precomputed counts to speed up our analysis.
cdm#>
#> ── # OMOP CDM reference (duckdb) of Eunomia Synpuf ─────────────────────────────
#> • 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_definition,
#> attribute_definition
#> • cohort tables: -
#> • achilles tables: achilles_analysis, achilles_results, achilles_results_dist
#> • other tables: -
# Create a code lists
<- list("warfarin" = c(1310149, 40163554),
codes "acetaminophen" = c(1125315, 1127078, 1127433, 40229134, 40231925, 40162522, 19133768),
"morphine" = c(1110410, 35605858, 40169988))
# Instantiate cohorts with CohortConstructor
$my_cohort <- conceptCohort(cdm = cdm,
cdmconceptSet = codes,
exit = "event_end_date",
overlap = "merge",
name = "my_cohort")
We can easily run all the analyses explained above (database
diagnostics, codelist diagnostics,
cohort diagnostics, matched
diagnostics, and population diagnostics) using
phenotypeDiagnostics()
:
<- phenotypeDiagnostics(cdm$my_cohort) result
Once we have our results we can quickly view them in an interactive application. Here we’ll apply a minimum cell count of 10 to our results and save our shiny app to a temporary directory, but you will likely want to save this shiny app to a local directory of your choice.
shinyDiagnostics(result = result, minCellCount = 10, directory = tempdir())
See the shiny app generated from the example cohort in here.
To see more details regarding each one of the analyses, please refer to the package vignettes.
These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.