Skip to contents

Best Mapped Concepts

Source: R/best_mapped_concepts.R
check_bmc.Rd

This function will identify the existing concepts within the specified field so the user can assess which of these concepts are acceptable ("best") or should not be used in that field ("not best")

Usage

check_bmc(
  bmc_tbl,
  best_notbest_tbl,
  omop_or_pcornet = "omop",
  concept_tbl = NULL,
  check_string = "bmc"
)

Arguments

bmc_tbl

tabular input || required

The primary input table that contains descriptive information about the checks to be executed by the function. It should include definitions the fields that should be evaluated to determine if they only include "best" concepts. see ?bmc_input_omop or ?bmc_input_pcornet for examples of the input structure

best_notbest_tbl

tabular input || required

A table indicating the best/not best concept designations for each check. See ?bmc_best_notbest for an example of this input structure.

If it is easier to list the concepts that are best, like if there are a limited number of acceptable concepts but many unacceptable concepts, the best_notbest column should be set to 1 and the default_to column should be set to notbest. If the inverse is true and it is easier to list the unacceptable concepts, the best_notbest column should be set to 0 and the default_to column should be set to best.

omop_or_pcornet

string || defaults to omop

A string, either omop or pcornet, indicating the CDM format of the data

concept_tbl

tabular input || defaults to NULL

An optional parameter used to define a vocabulary table with concept definitions (for example, the OHDSI concept table). If left NULL, the concepts as they exist in the fact table will be returned to the user.

check_string

string || defaults to bmc

An abbreviated identifier that will be used to label all output from this module

Value

This function will return a list of two dataframes:

  • bmc_counts: A table with one row for each concept present in each user-defined field and the associated row and patient counts/proportions

  • bmc_concepts: A table with just the concepts from bmc_counts. This output is should be labelled with "best" (1) vs "not best" (0) indicators in a column called include for use in the processing step

Examples

# First create input file with desired checks to be executed
# You can access examples for both OMOP & PCORnet here:
ndq::bmc_input_omop
#> # A tibble: 4 × 7
#>   check_id  check_description schema table     concept_field concept_table_field
#>   <chr>     <chr>             <chr>  <chr>     <chr>         <chr>              
#> 1 rxnorm_di inpatient admin   cdm    drug_exp… drug_concept… concept_class_id   
#> 2 rxnorm_dp prescriptions     cdm    drug_exp… drug_concept… concept_class_id   
#> 3 race      race              cdm    person    race_concept… concept_name       
#> 4 ethnicity ethnicity         cdm    person    ethnicity_co… concept_name       
#> # ℹ 1 more variable: filter_logic <chr>
ndq::bmc_input_pcornet
#> # A tibble: 4 × 7
#>   check_id  check_description schema table     concept_field concept_table_field
#>   <chr>     <chr>             <chr>  <chr>     <chr>         <chr>              
#> 1 rxnorm_di inpatient admin   cdm    med_admin medadmin_code concept_class_id   
#> 2 rxnorm_dp prescriptions     cdm    prescrib… rxnorm_cui    concept_class_id   
#> 3 race      race              cdm    demograp… race          concept_name       
#> 4 ethnicity ethnicity         cdm    demograp… hispanic      concept_name       
#> # ℹ 1 more variable: filter_logic <lgl>

# Use this as your input to the BMC function
if (FALSE) { # \dontrun{
my_bmc_rslt <- check_bmc(bmc_tbl = ndq::bmc_input_omop,
                         omop_or_pcornet = 'omop',
                         concept_tbl = vocabulary_tbl("concept"), ## points to OHDSI concept table
                         check_string = 'bmc')
} # }