adultASSUREv6

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, Spark, 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. Create an empty folder or new RStudio project, and in R, use the following code to install the study package and its dependencies:

   install.packages("renv")
   download.file("https://raw.githubusercontent.com/ohdsi-studies/adultASSUREv6/main/renv.lock", "renv.lock")
   renv::init()

   If renv mentions that the project already has a lockfile select "1: Restore the project from the lockfile.".

3. 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(adultASSUREv6)
   
   # Optional: specify where the temporary files (used by the Andromeda package) will be created:
   options(andromedaTempFolder = "s:/andromedaTemp")
   
   # Maximum number of cores to be used:
   maxCores <- parallel::detectCores()
   
   # Minimum cell count when exporting data:
   minCellCount <- 5
   
   # The folder where the study intermediate and result files will be written:
   outputFolder <- "c:/adultASSUREv6"
   
   # Details for connecting to the server:
   # See ?DatabaseConnector::createConnectionDetails for help
   connectionDetails <- DatabaseConnector::createConnectionDetails(dbms = "redshift",
                                                               connectionString = keyring::key_get("redShiftConnectionStringOhdaMdcr"),
                                                               user = keyring::key_get("redShiftUserName"),
                                                               password = keyring::key_get("redShiftPassword"))
   
   # The name of the database schema where the CDM data can be found:
   cdmDatabaseSchema <- "cdm_truven_mdcr_v1911"
   
   # The name of the database schema and table where the study-specific cohorts will be instantiated:
   cohortDatabaseSchema <- "scratch_mschuemi"
   cohortTable <- "estimation_skeleton"
   
   # Some meta-information that will be used by the export function:
   databaseId <- "IBM_MDCR"
   databaseName <- "IBM MarketScan® Medicare Supplemental and Coordination of Benefits Database"
   databaseDescription <- "IBM MarketScan® Medicare Supplemental and Coordination of Benefits Database (MDCR) represents health services of retirees in the United States with primary or Medicare supplemental coverage through privately insured fee-for-service, point-of-service, or capitated health plans.  These data include adjudicated health insurance claims (e.g. inpatient, outpatient, and outpatient pharmacy). Additionally, it captures laboratory tests for a subset of the covered lives."
   
   # For some database platforms (e.g. Oracle): define a schema that can be used to emulate temp tables:
   options(sqlRenderTempEmulationSchema = NULL)
   
   execute(connectionDetails = connectionDetails,
           cdmDatabaseSchema = cdmDatabaseSchema,
           cohortDatabaseSchema = cohortDatabaseSchema,
           cohortTable = cohortTable,
           outputFolder = outputFolder,
           databaseId = databaseId,
           databaseName = databaseName,
           databaseDescription = databaseDescription,
           verifyDependencies = TRUE,
           createCohorts = TRUE,
           synthesizePositiveControls = TRUE,
           runAnalyses = TRUE,
           packageResults = TRUE,
           maxCores = maxCores)

4. Upload the file export/Results_<DatabaseId>.zip in the output folder to the study coordinator:

   uploadResults(outputFolder, privateKeyFileName = "<file>", userName = "<name>")

   Where <file> and <name< are the credentials provided to you personally by the study coordinator.

5. To view the results, use the Shiny app:

   prepareForEvidenceExplorer("Result_<databaseId>.zip", "/shinyData")
   launchEvidenceExplorer("/shinyData", blind = TRUE)

Note that you can save plots from within the Shiny app. It is possible to view results from more than one database by applying prepareForEvidenceExplorer to the Results file from each database, and using the same data folder. Set blind = FALSE if you wish to be unblinded to the final results.

License

The adultASSUREv6 package is licensed under Apache License 2.0

Development

adultASSUREv6 was developed in ATLAS and R Studio.

Development status

Unknown