Cause-specific mortality package
Instructions To Build Package
- Build the package by clicking the R studio 'Install and Restart' button in the built tab
Instructions To Run Package
library(CauseSpecificMortality)
# USER INPUTS
#=======================
# The folder where the study intermediate and result files will be written:
outputFolder <- "./CauseSpecificMortalityResults"
# Specify where the temporary files (used by the ff package) will be created:
options(fftempdir = "location with space to save big data")
# Details for connecting to the server:
dbms <- "you dbms"
user <- 'your username'
pw <- 'your password'
server <- 'your server'
port <- 'your port'
connectionDetails <- DatabaseConnector::createConnectionDetails(dbms = dbms,
server = server,
user = user,
password = pw,
port = port)
# Add the database containing the OMOP CDM data
cdmDatabaseSchema <- 'cdm database schema'
# Add a database with read/write access as this is where the cohorts will be generated
cohortDatabaseSchema <- 'work database schema'
oracleTempSchema <- NULL
# table name where the cohorts will be generated
cohortTable <- 'CauseSpecificMortalityCohort'
# parameter settings for causePrediction
# TAR = (30,90,180,365)
# nTree = tree numbers of random forest algorithm, seedNum = Seed number
TAR <- 30
nTree <- 200
seedNum <- NULL
#=======================
execute(connectionDetails = connectionDetails,
cdmDatabaseSchema = cdmDatabaseSchema,
cohortDatabaseSchema = cohortDatabaseSchema,
cohortTable = cohortTable,
outputFolder = outputFolder,
createProtocol = F,
createCohorts = T,
runAnalyses = T,
causePrediction = T,
createResultsDoc = F,
packageResults = F,
createValidationPackage = F,
minCellCount= 5)
- If you want to run the causePrediction function with multiple parameter, you can use this
TAR <- c(30,60,90,180,365)
nTree <- 200
lapply(TAR, function(x) causePrediction(outputFolder, TAR = x))
- To predict cause of death after PLP analysis (model 1 = Lasso logistic regression, 2 = Gradient boosting machine)
CausePrediction(outputFolder, TAR = 30, nTree = 500, seedNum = NULL)
- You can then easily transport the trained models into a network validation study package by running :
execute(connectionDetails = connectionDetails,
cdmDatabaseSchema = cdmDatabaseSchema,
cohortDatabaseSchema = cohortDatabaseSchema,
cohortTable = cohortTable,
outputFolder = outputFolder,
createProtocol = F,
createCohorts = F,
runAnalyses = F,
createResultsDoc = F,
packageResults = F,
createValidationPackage = T,
minCellCount= 5)
- To create the shiny app and view run:
populateShinyApp(resultDirectory = outputFolder,
minCellCount = 10,
databaseName = 'friendly name'
)
viewShiny('CauseSpecificMortality')
Development status
Under development. Do not use