README

The MeasurementDiagnostics package helps us to assess the use of measurements present in data mapped to the OMOP CDM, either for the dataset as a whole or for a particular cohort.

Installation

You can install the development version of MeasurementDiagnostics from GitHub with:

# install.packages("pak")
pak::pak("ohdsi/MeasurementDiagnostics")

Example

Let’s say we are going to do a study where we are going to be using measurements of respiratory function. We can use MeasurementDiagnostics to better understand the use of these measurements.

library(duckdb)
#> Loading required package: DBI
library(omopgenerics)
#> 
#> Attaching package: 'omopgenerics'
#> The following object is masked from 'package:stats':
#> 
#>     filter
library(CDMConnector)
library(dplyr)
#> 
#> Attaching package: 'dplyr'
#> The following objects are masked from 'package:stats':
#> 
#>     filter, lag
#> The following objects are masked from 'package:base':
#> 
#>     intersect, setdiff, setequal, union
library(MeasurementDiagnostics)

con <- dbConnect(duckdb(), dbdir = eunomiaDir())
#> Creating CDM database C:/Users/nmercade/Dropbox/Documents/IDIAP/Formations/Curs_Introduccio_OMOP/MockDB/GiBleed_5.3.zip
#> ■■■■■■■■■■■■■■■■                  51% | ETA:  2s
#> ■■■■■■■■■■■■■■■■■■■               61% | ETA:  2s
#> ■■■■■■■■■■■■■■■■■■■■■             66% | ETA:  1s
#> ■■■■■■■■■■■■■■■■■■■■■■■■          78% | ETA:  1s
#> ■■■■■■■■■■■■■■■■■■■■■■■■■■        83% | ETA:  1s
#>                                                  
cdm <- cdmFromCon(
  con = con, cdmSchem = "main", writeSchema = "main", cdmName = "Eunomia"
)
cdm
#> 
#> ── # OMOP CDM reference (duckdb) of Eunomia ────────────────────────────────────
#> • omop tables: person, observation_period, visit_occurrence, visit_detail,
#> condition_occurrence, drug_exposure, procedure_occurrence, device_exposure,
#> measurement, observation, death, note, note_nlp, specimen, fact_relationship,
#> location, care_site, provider, payer_plan_period, cost, drug_era, dose_era,
#> condition_era, metadata, cdm_source, concept, vocabulary, domain,
#> concept_class, concept_relationship, relationship, concept_synonym,
#> concept_ancestor, source_to_concept_map, drug_strength
#> • cohort tables: -
#> • achilles tables: -
#> • other tables: -

Now we have a cdm reference with our data, we will create a codelist with measurement concepts.

repiratory_function_codes <- newCodelist(list("respiratory function" = c(4052083L, 4133840L, 3011505L)))
repiratory_function_codes
#> 
#> ── 1 codelist ──────────────────────────────────────────────────────────────────
#> 
#> - respiratory function (3 codes)

And now we can run a set of measurement diagnostic checks, here stratifying results by sex.

repiratory_function_measurements <- summariseMeasurementUse(cdm, repiratory_function_codes, bySex = TRUE)
#> → Getting measurement records based on 3 concepts.
#> → Subsetting records to the subjects and timing of interest.
#> Warning: cohort_name must be snake case and have less than 100 characters, the following
#> cohorts will be renamed:
#> • respiratory function_4052083 -> respiratory_function_4052083
#> • respiratory function_4133840 -> respiratory_function_4133840
#> • respiratory function_3011505 -> respiratory_function_3011505
#> → Getting time between records per person.
#> → Summarising results - value as number.
#> → Summarising results - value as concept.
#> → Binding all diagnostic results.

Among our results is a summary of timings between measurements for individuals in our dataset. We can quickly create a plot of these results like so