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CohortContrast

The goal of CohortContrast is to facilitate the comparison between cohorts in specified domains across all OMOP CDM datasets. It enables users to analyze and visualize the contrast between target and control cohorts effectively.

Installation

The development version of the package from GitHub:

# install.packages("devtools")
devtools::install_github("HealthInformaticsUT/CohortContrast")

Usage

To use CohortContrast, follow these steps to configure your environment and input data:

  1. Credentials: Make sure you can create a connection to your OHDS CDM instance using CDMConnector package.
pathToResults = getwd()

################################################################################
#
# Initiate the database connection
#
#################################################################################

user <- Sys.getenv("DB_USERNAME") #TODO
pw <- Sys.getenv("DB_PASSWORD") #TODO
server <- stringr::str_c(Sys.getenv("DB_HOST"), "/", Sys.getenv("DB_NAME")) #TODO
port <- Sys.getenv("DB_PORT") #TODO

cdmSchema <-
  Sys.getenv("OHDSI_CDM") #TODO # Schema which contains the OHDSI Common Data Model
cdmVocabSchema <-
  Sys.getenv("OHDSI_VOCAB") #TODO # Schema which contains the OHDSI Common Data Model vocabulary tables.
cdmResultsSchema <-
  Sys.getenv("OHDSI_RESULTS") #TODO # Schema which contains "cohort" table (is not mandatory)
writeSchema <-
  Sys.getenv("OHDSI_WRITE") #TODO # Schema for temporary tables, will be deleted
writePrefix <- "cc_"

db = DBI::dbConnect(
  RPostgres::Postgres(),
  dbname = Sys.getenv("DB_NAME"),
  host = Sys.getenv("DB_HOST"),
  user = Sys.getenv("DB_USERNAME"),
  password = Sys.getenv("DB_PASSWORD"),
  port  = port
)

cdm <- CDMConnector::cdmFromCon(
  con = db,
  cdmSchema = cdmSchema,
  achillesSchema = cdmResultsSchema,
  writeSchema = c(schema = writeSchema, prefix = writePrefix),
)

  1. Create target and control tables

    • Use functions cohortFromCohortTable, cohortFromDataTable, cohortFromJSON or cohortFromCSV for indicating your target and control cohort tables.
    • You can use createControlCohortInverse or cohortFromCohortTable for generating control tables.
cohortsTableSchemaName = cdmResultsSchema
cohortsTableName = 'cohort'
targetCohortId = 568
controlCohortId = 571

################################################################################
#
# CDM target and control modula
#
################################################################################

targetTable <- CohortContrast::cohortFromCohortTable(cdm = cdm, db = db,
   tableName = cohortsTableName, schemaName = cdmResultsSchema, cohortId = targetCohortId)
   
 controlTable <- CohortContrast::cohortFromCohortTable(cdm = cdm, db = db,
  tableName = cohortsTableName, schemaName = cdmResultsSchema, cohortId = controlCohortId)
  1. Run the Study: Execute the study by using the CohortContrast functions.
################################################################################
#
# Execute
#
################################################################################

data = CohortContrast::CohortContrast(
  cdm,
  targetTable = targetTable,
  controlTable = controlTable,
  pathToResults = getwd(),
  domainsIncluded = c(
    "Drug",
    "Condition",
    "Measurement",
    "Observation",
    "Procedure",
    "Visit",
    "Visit detail"
  ),
  prevalenceCutOff = 2.5,
  topK = FALSE, # Number of features to export
  presenceFilter = 0.2, # 0-1, percentage of people who must have the chosen feature present
  complementaryMappingTable = FALSE, # A table for manual concept_id and concept_name mapping (merge)
  getSourceData = FALSE, # If true will generate summaries with source data as well
  runZTests = TRUE,
  runLogitTests = FALSE,
  createOutputFiles = TRUE,
  safeRun = FALSE,
  complName = "CohortContrastStudy")

Outputs

The CohortContrast package generates the following outputs:

  1. Running CohortContrast returns a list of tables (patient level summarised data for target and control) as well as saves the object. These can be analysed in the GUI.
  2. Using GUI with runCohortContrastGUI generates plots as well as saves the last state of your analysis in the GUI.
  3. There is an example .rds file in ./inst/example/example.rds. You can view it in the GUI if you copy it to your pathToResults path.
CohortContrast::runCohortContrastGUI(
 pathToResults = pathToResults
)

Demo

Check out the demo on malignant neoplasm of breast cohort!

More information

CohortContrast provides much more insight generation possibilities. See the package vignettes for more details (coming soon …).

For feature requests create issues on Github or contact Markus Haug () personally.