IC Temporal Pattern Discovery
This R package is an implementation of the IC Temporal Pattern Discovery method to estimate risk by combining a self-controlled and cohort design. It is designed to run against observational databases in the OMOP Common Data Model.
- Extracts the necessary data from a database in OMOP Common Data Model format.
- Supports various risk and control window definitions.
- Create Chronographs to explore time-to-event relationships.
library(SelfControlledCohort) connectionDetails <- createConnectionDetails(dbms = "postgresql", user = "joe", password = "secret", server = "myserver") exposureOutcomePairs = data.frame(outcomeId = c(196794, 196794, 312648), exposurId = c(1501700, 1545958, 1551803)) ictpdData <- getDbIctpdData(connectionDetails, cdmDatabaseSchema = "cdm_schema.dbo", exposureOutcomePairs = exposureOutcomePairs) ictpdResults <- calculateStatisticsIC(ictpdData) ictpdResults
IcTemporalPatternDiscovery is an R package.
Requires R (version 3.1.0 or higher). Libraries used in this package require Java.
- The DatabaseConnector and SqlRender packages require Java. Java can be downloaded from http://www.java.com.
- In R, use the following commands to download and install IcTemporalPatternDiscovery:
install.packages("devtools") library(devtools) install_github("ohdsi/ParallelLogger") install_github("ohdsi/IcTemporalPatternDiscovery")
- Package manual: IcTemporalPatternDiscovery.pdf
- Developer questions/comments/feedback: OHDSI Forum
- We use the GitHub issue tracker for all bugs/issues/enhancements
IcTemporalPatternDiscovery is licensed under Apache License 2.0
IcTemporalPatternDiscovery is being developed in R Studio.
This package was developed by Tomas Bergvall, adapted by Martijn Schuemie.