# Introduction To DrugExposureDiagnostics

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

First, connect to the database. Examples of how to connect your database using CDMConnector can be found here: https://darwin-eu.github.io/CDMConnector/articles/a04_DBI_connection_examples.html
Here we use the internal mock database.

    cdm <- mockDrugExposure()

### Drug(s) of interest

In the DrugExposureDiagnostics package, all the diagnostics are conducted on ingredient level, and if requested, additionally on drug level. We will use the ingredient “acetaminophen” as an example.
Here is a brief look at this ingredient.

Property | Value
Concept Name | acetaminophen
Domain ID | Drug
Concept Class ID | Ingredient
Vocabulary ID | RxNorm
Concept ID | 1125315
Concept code | 161
Validity | Valid
Concept | Standard
Valid start | 01-Jan-1970
Valid end | 31-Dec-2099

### Running the diagnostics

We can run all available checks at the same time using the ´executeChecks()´ function. This will return a list which contains the results of each check.
Multiple ingredients can be added in the vector of ´ingredients´

    all_checks <- executeChecks(cdm,
                                ingredients = c(1125315),
                                subsetToConceptId = NULL,
                                checks = c("missing", "exposureDuration", "type", "route", "sourceConcept", "daysSupply",
                                           "verbatimEndDate", "dose", "sig", "quantity", "diagnosticsSummary"),
                                minCellCount = 5,
                                sample = 10000,
                                tablePrefix = NULL,
                                earliestStartDate = "2010-01-01",
                                verbose = FALSE,
                                byConcept = TRUE)
    #> population after earliestStartDate smaller than sample, sampling ignored
    #> ℹ The following estimates will be computed:
    #> • daily_dose: count_missing, percentage_missing, mean, sd, min, q05, q25,
    #>   median, q75, q95, max
    #> ! Table is collected to memory as not all requested estimates are supported on
    #>   the database side
    #> → Start summary of data, at 2024-07-02 14:23:46.414516
    #>
    #> ✔ Summary finished, at 2024-07-02 14:23:46.555502

The `cdm` is the database reference of the OMOP CDM using the `CDMConnector` package.
The `ingredients` is a list of ingredients of interests, by default it is 1125315 for acetaminophen. The `subsetToConceptId` vector of concept IDs of the ingredients to filter. If a concept ID is positive it will be included, a negative one will be excluded. If NULL, all concept IDs for an ingredient will be considered. `checks` allows to select the checks to be executed, by default the missing values, the exposure duration and the quantity checks will be run. The `minCellCount` is minimum number of events to report, numbers lower than this will be obscured. `sample` is the number of samples, by default, 10.000 drug record samples will be used.
The `tablePrefix` is an optional value for database tables that will be created during executeChecks. `earliestStartDate` is the earliest data from which drug records will be included. `verbose` is a parameter that enables the printing of messages to the console. `byConcept` is a boolean that determines if only overall results should be returned or also by drug concept.

The check “missing” outputs the `missingValuesOverall` if `byConcept = FALSE` and additionally `missingValuesbyConcept` if `byConcept = TRUE`
The check “exposureDuration” outputs the `drugExposureDurationOverall` if `byConcept = FALSE` and additionally `drugExposureDurationbyConcept` if `byConcept = TRUE`
The check “type” outputs the `drugTypesOverall` if `byConcept = FALSE` and additionally `drugTypesbyConcept` if `byConcept = TRUE`
The check “route” outputs the `drugRoutesOverall` if `byConcept = FALSE` and additionally `drugRoutesbyConcept` if `byConcept = TRUE`
The check “sourceConcept” outputs the `sourceConceptsOverall` (which is actually the drug concept level. It disregards the `byConcept` argument because only the drug concept level makes sense here)
The check “daysSupply” outputs the `drugDaysSupplyOverall` if `byConcept = FALSE` and additionally `drugDaysSupplybyConcept` if `byConcept = TRUE`
The check “verbatimEndDate” outputs the `drugVerbatimEndDate` if `byConcept = FALSE` and additionally `drugVerbatimEndDatebyConcept` if `byConcept = TRUE`
The check “dose” outputs the `drugDoseOverall` (output only on ingredient level. It disregards the `byConcept` argument because only the drug concept level makes sense here)
The check “sig” outputs the `drugSig` if `byConcept = FALSE` and additionally `drugSigbyConcept` if `byConcept = TRUE`
The check “quantity” outputs the `drugQuantity` if `byConcept = FALSE` and additionally `drugQuantitybyConcept` if `byConcept = TRUE`
The check “diagnosticsSummary” outputs a summary called `diagnosticsSummary`

Here, we see the files created:

    names(all_checks)
    #>  [1] "conceptSummary"                "missingValuesOverall"
    #>  [3] "missingValuesByConcept"        "drugExposureDurationOverall"
    #>  [5] "drugExposureDurationByConcept" "drugTypesOverall"
    #>  [7] "drugTypesByConcept"            "drugRoutesOverall"
    #>  [9] "drugRoutesByConcept"           "drugSourceConceptsOverall"
    #> [11] "drugDaysSupply"                "drugDaysSupplyByConcept"
    #> [13] "drugVerbatimEndDate"           "drugVerbatimEndDateByConcept"
    #> [15] "drugDose"                      "drugSig"
    #> [17] "drugSigByConcept"              "drugQuantity"
    #> [19] "drugQuantityByConcept"         "diagnosticsSummary"

### Assessing the drug concepts (of interest)

The first item shown, the `conceptSummary` is not the output from a check but always output. This table is used within the function as an input for all the requested checks.
It contains information from the CONCEPT table and DRUG_STRENGTH table.
The `conceptSummary` has several use:
It can be quickly seen whether a certain drug is mapped on ingredient level. Thereby, the drug_concept_id and ingredient_concept_id are identical.
Furthermore, the `conceptSummary` can be used to make decisions about further refine the concept list. For example, though the argument `subsetToConceptId` in the `executeChecks()` function, the selected concepts can be requested or eliminated.
The `conceptSummary` in the case of acetaminophen with `subsetToConceptId = NULL`, the following drug concepts are part of the diagnostics investigation.

    datatable(all_checks$conceptSummary,
      rownames = FALSE
    )

Column | Description
--- | ---
drug_concept_id | ID of the drug concept.
drug | Name of the drug concept.
ingredient_concept_id | Concept ID of ingredient.
ingredient | Name of drug ingredient for which the checks have been performed.
n_records | Number of records for drug concept.
domain_id | From the CONCEPT table. A foreign key to the domain table the drug concept belongs to.
vocabulary_id | From the CONCEPT table. A foreign key to the vocabulary table indicating from which source the drug concept has been adapted.
concept_class_id | From the CONCEPT table. The concept class of the drug concept (e.g.‘Ingredient’ or ‘Clinical Drug’).
standard_concept | From the CONCEPT table. A flag indicating whether the drug concept is standard (‘S’), a classification (‘C’), or a non-standard Source (NULL).
concept_code | From the CONCEPT table. The concept code represents the identifier of the Concept in the source vocabulary.
valid_start_date | From the CONCEPT table. The date when the drug concept was first recorded
valid_end_date | From the CONCEPT table. The date when the drug concept became invalid because it was deleted or superseded (updated) by a new concept.
invalid_reason | From the CONCEPT table. Reason the Concept was invalidated. Possible values are D (deleted), U (replaced with an update) or NULL when valid_end_date has the default value.
amount_value | From DRUG_STRENGTH table. The numeric value or the amount of active ingredient contained within the drug product.
amount_unit_concept_id | From DRUG_STRENGTH table. The Concept representing the Unit of measure for the amount of active ingredient contained within the drug product.
numerator_value | From DRUG_STRENGTH table. The concentration of the active ingredient contained within the drug product.
numerator_unit_concept_id | From DRUG_STRENGTH table. The Concept representing the Unit of measure for the concentration of active ingredient.
numerator_unit | From the CONCEPT table. The concept name associated with the numerator_unit_concept_id.
denominator_value | From DRUG_STRENGTH table. The amount of total liquid (or other divisible product, such as ointment, gel, spray, etc.).
denominator_unit_concept_id | From DRUG_STRENGTH table. The Concept representing the denominator unit for the concentration of active ingredient.
denominator_unit | From the CONCEPT table. The concept name associated with the denominator_unit_concept_id
box_size | The number of units of Clinical Branded Drug or Quantified Clinical or Branded Drug contained in a box as dispensed to the patient.
amount_unit | From DRUG_STRENGTH table.
dose_form | From CONCEPT_RELATIONSHIP table. The RxNorm dose form associated with the drug concept.
result_obscured | TRUE if count has been suppressed due to being below the minimum cell count, otherwise FALSE.

### Saving the diagnostic output

After running the checks, we can write the created outputs (CSV files) into a zip file to disk using the `writeResultToDisk()` function. The zip created from using the `writeResultToDisk()` function can be input (unzipped) into the correspoding Shiny Application.
Please replace the “your_database” with the name of your database. Create your designated “output_folder” at your desired location and put the path if not the working directory.

    writeResultToDisk(all_checks,
                      databaseId = "your_database",
                      outputFolder = "output_folder")