extras/CodeToRun.R

In ohdsi-studies/PhenotypeLibraryDiagnostics: Generating Cohort Diagnostics for the OHDSI Phenotype Library

# *******************************************************
# -----------------INSTRUCTIONS -------------------------
# *******************************************************
#
# This CodeToRun.R is provided as an example of how to run this study package.
# 
# Below you will find 3 sections: the 1st is for installing the study package and its dependencies, 
# the 2nd for running the package, the 3rd is for sharing the results with the study coordinator.
#
# In section 2 below, you will also need to update the code to use your site specific values. Please scroll
# down for specific instructions.
#
# 
# *******************************************************
# SECTION 1: Installing 
# *******************************************************
# 
# 1. See the instructions at https://ohdsi.github.io/Hades/rSetup.html for configuring your R environment, including Java and RStudio.
#
# 2. In RStudio, create a new project: File -> New Project... -> New Directory -> New Project. If asked if you want to use `renv` with the project, answer ‘no’.
#
# 3. Execute the following R code:

# Install the latest version of renv:
install.packages("renv")

# Download the lock file:
download.file("https://raw.githubusercontent.com/ohdsi-studies/PhenotypeLibraryDiagnostics/master/renv.lock", "renv.lock")

# Build the local library. This may take a while:
renv::init()

# *******************************************************
# SECTION 2: Running the package -------------------------------------------------------------------------------
# *******************************************************
#
# Edit the variables below to the correct values for your environment:

library(PhenotypeLibraryDiagnostics)

# Specify where the temporary files will be created:
options(andromedaTempFolder = "s:/andromedaTemp")

# Maximum number of cores to be used:
maxCores <- parallel::detectCores()

# Details for connecting to the server. See 
# http://ohdsi.github.io/DatabaseConnector/reference/createConnectionDetails.html for more details:
connectionDetails <- DatabaseConnector::createConnectionDetails(dbms = "postgresql",
                                                                server = "localhost/ohdsi",
                                                                user = "joe",
                                                                password = "secret",
                                                                port = 5432)

# For Oracle and BigQuery: define a schema that can be used to emulate temp tables. 
# You should have write access to this schema:
oracleTempSchema <- NULL

# A folder on the local file system to store results:
outputFolder <- "s:/PhenotypeLibraryDiagnosticsOutput"

# The database schema where the observational data in CDM is located. For SQL Server
# this should include both the database and schema, for example 'cdm.dbo'.
# You should have read access to this schema:
cdmDatabaseSchema <- "cdm"

# The database schema where the cohorts can be instantiated. For SQL Server
# this should include both the database and schema, for example 'cdm.dbo'.
# You should have write access to this schema:
cohortDatabaseSchema <- "scratch.dbo"

# The name of the table that will be created in the cohortDatabaseSchema:
cohortTable <- "pl_cohort"

# Some meta-data about your database. The databaseId is a short (<= 20 characters)
# name for your database. The databaseName is the full name, and databaseDescription 
# provides a short (1 paragraph) description. These values will be displayed in the 
# Shiny results app for all to see.
databaseId <- "CCAE"
databaseName <- "IBM MarketScan Commercial Claims and Encounters Database"
databaseDescription <- "IBM MarketScan® Commercial Claims and Encounters Database (CCAE) represent data from individuals enrolled in United States employer-sponsored insurance health plans. The data includes adjudicated health insurance claims (e.g. inpatient, outpatient, and outpatient pharmacy) as well as enrollment data from large employers and health plans who provide private healthcare coverage to employees, their spouses, and dependents. Additionally, it captures laboratory tests for a subset of the covered lives. This administrative claims database includes a variety of fee-for-service, preferred provider organizations, and capitated health plans." 

# For uploading the results. You should have received the key file from the study coordinator:
keyFileName <- "c:/home/keyFiles/study-data-site-pldiag.dat"
userName <- "study-data-site-pldiag"

# This statement instatiates the cohorts, performs the diagnostics, and writes the results to
# a zip file containing CSV files. This will probaby take a long time to run:
PhenotypeLibraryDiagnostics::executePhenotyeLibraryDiagnostics(
  connectionDetails = connectionDetails,
  cdmDatabaseSchema = cdmDatabaseSchema,
  vocabularyDatabaseSchema = cdmDatabaseSchema,
  cohortDatabaseSchema = cohortDatabaseSchema,
  cohortTable = cohortTable,
  oracleTempSchema = oracleTempSchema,
  verifyDependencies = TRUE,
  outputFolde = outputFolderr,
  cohortIds = NULL,
  incrementalFolder = file.path(outputFolder, "incrementalFolder"),
  databaseId = databaseId,
  databaseName = databaseName,
  databaseDescription = databaseDescription
)

# (Optionally) to view the results locally:
CohortDiagnostics::createMergedResultsFile(
  dataFolder = file.path(outputFolder, "diagnosticsExport"),
  sqliteDbPath = file.path(outputFolder, "MergedCohortDiagnosticsData.sqlite")
)
CohortDiagnostics::launchDiagnosticsExplorer(sqliteDbPath = file.path(outputFolder, "MergedCohortDiagnosticsData.sqlite"))

# *******************************************************
# SECTION 3: Sharing the results -------------------------------------------------------------------------------
# *******************************************************
#
# Upload results to the OHDSI SFTP server:
uploadResults(outputFolder, keyFileName, userName)

# Please send the study-coordinator an e-mail when done