EHDEN COVID Use Case

Note: This is not the most updated version of this study package. This version contains the simplified PS models (no covariates, Age + sex, or CCI + Sex). For the most up to date study package containing the full PS model, visit the branch "Hydrated Minus Criteria 1"

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• Analytics use case(s): Estimation
• Study type: Clinical Application
• Tags: OHDSI, COVID-19
• Study lead: -
• Study lead forums tag: Ravinder Claire , Christina Read , lead]
• Study start date: -
• Study end date: -
• Protocol: EHDEN Use Case Protocol.pdf
• Results explorer: -

The aim of this study is to estimate treatment effects for COVID-19 treatments using data from the EHDEN network and to combine these observational results with data from randomised studies.

Primary objectives:

1. To assess comparative effectiveness and safety among Tocilizumab, Baricitinib, and Remdesivir in hospitalised patients
2. To assess comparative effectiveness and safety among Aspirin and Heparin in hospitalised patients

Subgroup Analyses:

1. Effectiveness and safety in ICU patients
2. Effectiveness and safety in patients receiving corticosteroids
3. Effectiveness and safety in patients receiving oxygen

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://github.com/ohdsi-studies/EhdenCovidUseCase/blob/NoCovariates/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(EHDENUseCase4.3)
   
   # 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:/EHDENUseCase4.3"
   
   # 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. Email results to christina.read@nice.org.uk and ravinder.claire@nice.org.uk

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 EHDENUseCase4.3 package is licensed under Apache License 2.0

Development

EHDENUseCase4.3 was developed in ATLAS and R Studio.