This study aims to compare the risk of mortality or severe clinical outcome between RAS blockers and CCB.
See these instructions on how to set up the R environment on Windows.
In R
, use the following code to install the dependencies:
r
install.packages("devtools")
library(devtools)
install_github("ohdsi/ParallelLogger", ref = "v1.1.1")
install_github("ohdsi/SqlRender", ref = "v1.6.3")
install_github("ohdsi/DatabaseConnector", ref = "v2.4.1")
install_github("ohdsi/OhdsiSharing", ref = "v0.1.3")
install_github("ohdsi/FeatureExtraction", ref = "v2.2.5")
install_github("ohdsi/CohortMethod", ref = "v3.1.0")
install_github("ohdsi/EmpiricalCalibration", ref = "v2.0.0")
install_github("ohdsi/MethodEvaluation", ref = "v1.1.0")
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 only the 64-bit versions of R and Java, as described in the Book of OHDSI
In R
, use the following devtools
command to install the RASBlockerVsCCBinCovid package:
r
install_github("ohdsi-studies/RASBlockerVsCCBinCovid") # Note: it is ok to delete inst/doc
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
:
```r library(RASBlockerVsCCBinCovid)
options(fftempdir = "c:/FFtemp")
maxCores <- parallel::detectCores()
minCellCount <- 1
outputFolder <- "c:/RASBlockerVsCCBinCovid"
connectionDetails <- DatabaseConnector::createConnectionDetails(dbms = "postgresql", server = "some.server.com/ohdsi", user = "joe", password = "secret")
cdmDatabaseSchema <- "cdm_synpuf"
cohortDatabaseSchema <- "scratch.dbo" cohortTable <- "my_study_cohorts"
databaseId <- "Synpuf" databaseName <- "Medicare Claims Synthetic Public Use Files (SynPUFs)" databaseDescription <- "Medicare Claims Synthetic Public Use Files (SynPUFs) were created to allow interested parties to gain familiarity using Medicare claims data while protecting beneficiary privacy. These files are intended to promote development of software and applications that utilize files in this format, train researchers on the use and complexities of Centers for Medicare and Medicaid Services (CMS) claims, and support safe data mining innovations. The SynPUFs were created by combining randomized information from multiple unique beneficiaries and changing variable values. This randomization and combining of beneficiary information ensures privacy of health information."
oracleTempSchema <- NULL
execute(connectionDetails = connectionDetails, cdmDatabaseSchema = cdmDatabaseSchema, cohortDatabaseSchema = cohortDatabaseSchema, cohortTable = cohortTable, oracleTempSchema = oracleTempSchema, outputFolder = outputFolder, databaseId = databaseId, databaseName = databaseName, databaseDescription = databaseDescription, createCohorts = TRUE, synthesizePositiveControls = TRUE, runAnalyses = TRUE, runDiagnostics = TRUE, packageResults = TRUE, maxCores = maxCores) ```
Upload the file export/Results<DatabaseId>.zip
in the output folder to the study coordinator:
r
submitResults("export/Results<DatabaseId>.zip", key = "<key>", secret = "<secret>")
Where key
and secret
are the credentials provided to you personally by the study coordinator.
To view the results, use the Shiny app:
r
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.
The RASBlockerVsCCBinCovid package is licensed under Apache License 2.0
RASBlockerVsCCBinCovid was developed in ATLAS and R Studio.
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