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README.Rmd
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README.Rmd
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---
output: github_document
---
<!-- README.md is generated from README.Rmd. Please edit that file -->
```{r, include = FALSE}
knitr::opts_chunk$set(
collapse = TRUE,
comment = "#>",
fig.path = "man/figures/README-",
out.width = "100%"
)
```
# DrugExposureDiagnostics <img src='man/figures/DrugExposureDiagnostics.png' align="right" width="139"/>
<!-- badges: start -->
[](https://CRAN.R-project.org/package=DrugExposureDiagnostics)
[](https://app.codecov.io/github/darwin-eu/DrugExposureDiagnostics?branch=main)
[](https://github.com/darwin-eu/DrugExposureDiagnostics/actions)
[](https://lifecycle.r-lib.org/articles/stages.html#stable)
<!-- badges: end -->
The goal of DrugExposureDiagnostics is to summarise ingredient specific drug exposure data in the OMOP CDM.
## Installation
You can install the DrugExposureDiagnostics from CRAN like this:
``` r
install.packages("DrugExposureDiagnostics")
```
or install the development version:
``` r
install.packages("remotes")
remotes::install_github("darwin-eu/DrugExposureDiagnostics")
```
## Citation
```{r}
citation("DrugExposureDiagnostics")
```
## Example use
```{r, message=FALSE}
library(DrugExposureDiagnostics)
library(CDMConnector)
library(dplyr)
```
```{r}
cdm <- mockDrugExposure()
```
Let´s look at the ingredient acetaminophen (https://athena.ohdsi.org/search-terms/terms/1125315).
We can run all the checks available in ´DrugExposureDiagnostics´ using the ´executeChecks´ function.
```{r}
all_checks <- executeChecks(
cdm = cdm,
ingredients = 1125315,
checks = c(
"missing", "exposureDuration", "type", "route", "sourceConcept", "daysSupply",
"verbatimEndDate", "dose", "sig", "quantity", "diagnosticsSummary"
)
)
```
The output is a list which contains the following set of tibbles:
```{r}
names(all_checks)
```
The first item contains information on the concept ids that are used in the database for a given ingredient.
```{r}
glimpse(all_checks$conceptSummary)
all_checks$conceptSummary %>%
select("drug_concept_id", "drug")
```
Other tibbles then contain information from the various checks performed.
For example, we can see a summary of missingness for the ingredient-related records in the drug exposure table, both overall and by concept.
```{r}
all_checks$missingValuesOverall
all_checks$missingValuesByConcept
```
Or we can also see a summary of drug exposure duration (drug_exposure_end_date - drug_exposure_end_date + 1), again overall or by concept.
```{r}
all_checks$drugExposureDurationOverall
all_checks$drugExposureDurationByConcept
```
For further information on the checks performed please see the package vignettes.
After running the checks we can write the CSVs to disk using the `writeResultToDisk` function.
```{r}
writeResultToDisk(all_checks,
databaseId = "Synthea",
outputFolder = tempdir()
)
```
```{r, echo=FALSE}
DBI::dbDisconnect(attr(cdm, "dbcon"), shutdown = TRUE)
```