SAGE

• Analytics use case(s): Characterization
• Study type: Clinical Application
• Tags: Regulatory Decision
• Study lead: Seng Chan You, Subin Kim, Seonji Kim
• Study lead forums tag: SCYou
• Study start date: 2023-05-16
• Study end date:
• Protocol: -
• Publications: -
• Results explorer: -

Medication use assessment has an important role in promoting the rational use of medicines. The examination of prescribing patterns is particularly valuable for policymakers and healthcare professionals, as it enables informed decision-making at a national level. However, few previous studies explored the prescription pattern for several drugs announced in regulatory decisions.

In this study, we aim to evaluate the effect of the regulatory decisions for drug safety surveillance on medication use. We will conduct a systematic, multinational study to investigate the monthly prescribing patterns (the number of patients prescribed drugs with safety warnings, prescriptions, prescription duration, first prescription, and first prescription days' supply of the first prescription) on safety warning drugs.

Using the SAGE package, we can extract the number of patients prescribed drugs with a safety warning, prescriptions, prescription duration, first prescription, and first prescription days' supply of the first prescriptions stratified by monthly calendar date, providing a detailed view of drug utilization patterns over time. After receiving the study results from co-researchers, we will apply an Autoregressive Integrated Moving Average (ARIMA) model to assess whether there are significant changes in prescribing following the publication of safety warnings.

Requirements

• A database in Common Data Model version 5 in one of these platforms: SQL Server, Oracle, PostgreSQL, IBM Netezza, Apache Impala, Amazon RedShift, Google BigQuery, or Microsoft APS.
• R version 4.0.0 or newer
• On Windows: RTools
• Java
• 25 GB of free disk space

How to run

1. Follow these instructions for setting up your R environment, including RTools and Java.

2. Open your study package in RStudio. Use the following code to install all the dependencies:

   install.packages('devtools')
   install.packages('dplyr')
   install.packages('data.table')
   install.packages('R.utils')
   install.packages('rvg')
   install.packages('renv')
   install.packages('lubridate')
   
   devtools::install_github("OHDSI/DatabaseConnector",ref="v5.0.2")
   devtools::install_github("OHDSI/ParallelLogger",ref="v2.0.2")

   Or use dockerfile in DockerImage

   Build Docker Image

   $docker build --build-arg GIT_ACCESS_TOKEN=[insert-access-token-here] -t [image_name]:[image_tag] .
   

   Run Docker Container

   $docker run --name [conatainer_name] -e USER=user -e PASSWORD=password -p 8787:8787 [image_name]:[image_tag]
   

3. In RStudio, select 'Build' then 'Install and Restart' to build the package.

4. Once installed, you can execute the study by modifying and using the code below. For your convenience, this code is also provided under extras/CodeToRun.R:

   library(SAGE)
   
   # The folder where the study intermediate and result files will be written:
   outputFolder <- file.path("outputFolderDir")
   
   connectionDetails <- DatabaseConnector::createConnectionDetails(
     dbms = 'postgresql',
     server = 'myserver',
     user = 'joe',
     password = 'secret',
     pathToDriver = 'S:/jdbcDrivders'
   )
   
   
   # The name of the database schema where the CDM data can be found:
   cdmDatabaseSchema<-'CDM_mydb.dbo'
   
   # The name of the database schema and table where the study-specific cohorts will be instantiated:
   cohortDatabaseSchema <- 'mydb.dbo'
   cohortTable <- "SAGE"
   
   # Some meta-information that will be used by the export function:
   databaseName <- 'MYDATABASE'
   
   
   execute(connectionDetails = connectionDetails,
           cdmDatabaseSchema = cdmDatabaseSchema,
           cohortDatabaseSchema = cohortDatabaseSchema,
           cohortTable = cohortTable,
           outputFolder = outputFolder,
           databaseName = databaseName,
           createCohorts = TRUE,
           runPrescriptionNum = TRUE,
           runDUR = TRUE,
           resultsToZip = TRUE,
           yearStartDate = as.Date("2006-01-01"),
           yearEndDate = as.Date("2022-12-31"),
           monthStartDate = as.Date("2006-01-01"),
           monthEndDate = as.Date("2022-12-31"))
   

5. Share the file drugCohort_<DatabaseId>.zip in the output folder to the study coordinator

Development

SAGE package is in development