README.md

In ohdsi-studies/EhdenRaDmardsEstimation: A Package Skeleton for Comparative Effectiveness Studies

DMARDs comparative safety in RA (EHDEN Study-a-thon Barcelona 2020)

• Analytics use case(s): Population-Level Estimation
• Study type: Clinical Application
• Tags: EHDEN
• Study lead: Jamie Weaver, Ed Burn
• Study lead forums tag: jweave17, edburn
• Study start date: January 13, 2020
• Study end date: -
• Protocol: Word file
• Publications: -
• Results explorer: Shiny app

This study aims to estimate the population level-effects of conventional synthetic disease-modifying antirheumatic drugs among patients with rheumatoid arthritis.

Requirements

• A database in Common Data Model version 5 in one of these platforms: SQL Server, Oracle, PostgreSQL, IBM Netezza, Apache Impala, Amazon RedShift, or Microsoft APS.
• R version 3.5.0 or newer
• On Windows: RTools
• Java
• 25 GB of free disk space

See this video for instructions on how to set up the R environment on Windows.

How to run

1. In R, use the following code to install the dependencies:

   r install.packages("devtools") library(devtools) install_github("ohdsi/SqlRender") install_github("ohdsi/DatabaseConnector") install_github("ohdsi/OhdsiSharing") install_github("ohdsi/FeatureExtraction") install_github("ohdsi/CohortMethod") install_github("ohdsi/EmpiricalCalibration") install_github("ohdsi/MethodEvaluation")

   If you experience problems on Windows where rJava can't find Java, one solution may be to add args = "--no-multiarch" to each install_github call, for example:

   r install_github("ohdsi/SqlRender", args = "--no-multiarch")

   Alternatively, ensure that you have installed both 32-bit and 64-bit JDK versions, as mentioned in the video tutorial.

2. In 'R', use the following code to install the EhdenRaDmardsEstimation package:

   r install_github("ohdsi-studies/EhdenRaDmardsEstimation", args = "--no-multiarch")

3. Once installed, you can execute the study by modifying and using the following code:

   ```r library(EhdenRaDmardsEstimation)

   Optional: specify where the temporary files (used by the ff package) will be created:

   options(fftempdir = "c:/FFtemp")

   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:/EhdenRaDmardsEstimation"

   Details for connecting to the server:

   See ?DatabaseConnector::createConnectionDetails for help

   connectionDetails <- DatabaseConnector::createConnectionDetails(dbms = "", server = "", user = "", password = "")

   The name of the database schema where the CDM data can be found:

   cdmDatabaseSchema <- ""

   The name of the database schema and table where the study-specific cohorts will be instantiated:

   cohortDatabaseSchema <- "" cohortTable <- ""

   Some meta-information that will be used by the export function:

   databaseId <- "" # required databaseName <- "" # required databaseDescription <- ""

   For Oracle: define a schema that can be used to emulate temp tables:

   oracleTempSchema <- NULL

   execute(connectionDetails = connectionDetails, cdmDatabaseSchema = cdmDatabaseSchema, cohortDatabaseSchema = cohortDatabaseSchema, cohortTable = cohortTable, oracleTempSchema = oracleTempSchema, outputFolder = outputFolder, databaseId = databaseId, databaseName = databaseName, databaseDescription = databaseDescription, createCohorts = TRUE, synthesizePositiveControls = FALSE, runAnalyses = TRUE, runDiagnostics = TRUE, packageResults = TRUE maxCores = maxCores, minCellCount = minCellCount) ```

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

r resultsZipFile <- file.path(outputFolder, "export", paste0("Results", databaseId, ".zip")) dataFolder <- file.path(outputFolder, "shinyData") prepareForEvidenceExplorer(resultsZipFile = resultsZipFile, dataFolder = dataFolder) launchEvidenceExplorer(dataFolder = dataFolder, blind = TRUE, launch.browser = FALSE)

Note that you can save plots from within the Shiny app (You can do this by re-building the package). 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.

1. When completed, the output will exist as a .ZIP file in the resultsZipFile directory location. This file contains the results to submit to the study lead.

License

The DMARDs comparative safety in RA package is licensed under Apache License 2.0

ohdsi-studies/EhdenRaDmardsEstimation documentation built on April 25, 2020, 5:17 p.m.