# Days Supply

## Run the days supply check

    library(DrugExposureDiagnostics)
    library(dplyr)
    library(DT)

    # acetaminophen concept id is 1125315
    acetaminophen <- 1125315
    cdm <- mockDrugExposure()
    acetaminophen_checks <- executeChecks(
      cdm = cdm,
      ingredients = acetaminophen,
      checks = "daysSupply"
    )

## Days supply Overall

This shows the days supply of the drug records summarised on ingredient level.

    datatable(acetaminophen_checks$drugDaysSupply,
      rownames = FALSE
    )

Column | Description
--- | ---
ingredient_concept_id | Concept ID of ingredient.
ingredient | Name of drug ingredient.
n_records | Number of records for ingredient concept. If n_records is the same as n_sample this means that there are more records but the number was cut at the pre-specified sample number for efficiency reasons.
n_sample | The pre-specified maximum sample. If n_records is smaller than the sample it means that sampling was ignored because the total number of records was already too small.
n_person | Number of individuals.
minimum_drug_exposure_days_supply | Minimum drug exposure days supply
q05_drug_exposure_days_supply_supply | 5th quantile of drug exposure days supply
q10_drug_exposure_days_supply | 10th quantile of drug exposure days supply
q25_drug_exposure_days_supply | 25th quantile of drug exposure days supply
median_drug_exposure_days_supply | Median drug exposure days supply
q75_drug_exposure_days_supply | 75th quantile of drug exposure days supply
q90_drug_exposure_days_supply | 90th quantile of drug exposure days supply
q95_drug_exposure_days_supply | 95th quantile of drug exposure days supply
maximum_drug_exposure_days_supply | Maximum drug exposure days supply
n_different_days_supply_and_drug_dates | Number of records where days supply differs to drug exposure days supply (estimated through drug_exposure_end_date - drug_exposure_start_date + 1). Ideally these two values should overlap because often the drug_exposure_end_date is derived from the days_supply field. However, the days_supply field is not a required field where as drug_exposure_end_date is a required field. If days_supply is missing then it is automatically captured as a difference.
n_days_supply_match_drug_dates | Number of records where days supply is identical to drug exposure days supply (estimated through drug_exposure_end_date - drug_exposure_start_date + 1). Ideally these two values should overlap because often the drug_exposure_end_date is derived from the days_supply field. However, the days_supply field is not a required field where as drug_exposure_end_date is a required field.
n_missing_days_supply | Number of records with missing days supply. This field together with n_different_days_supply_and_drug_dates and n_days_supply_match_drug_dates shall be identical to n_records.
proportion_different_days_supply_and_drug_dates | Proportion of records where days supply differs to drug exposure days supply (estimated through drug_exposure_end_date - drug_exposure_start_date + 1).
proportion_days_supply_match_drug_dates | Proportion of records where days supply is idential to drug exposure days supply (estimated through drug_exposure_end_date - drug_exposure_start_date + 1).
proportion_missing_days_supply |
result_obscured | TRUE if count has been suppressed due to being below the minimum cell count, otherwise FALSE.

## Days supply by drug concept

This shows the days supply on the drug concept level. The tables are identical to the overall just including two more columns at the beginning.

Column | Description
--- | ---
drug_concept_id | ID of the drug concept.
drug | Name of the drug concept.

    datatable(acetaminophen_checks$drugDaysSupplyByConcept,
      rownames = FALSE
    )