Awesome R Packages for Statistical Analysis and Machine Learning in the OMOP CDM Research Setting

A curated list of R packages for statistical analysis and machine learning in the OMOP CDM (Common Data Model) research setting. Contributions welcome!

Citation

Contents

• Awesome R Packages for OMOP CDM
  • Citation
  • Contents
  • Packages
  • Contributing

Packages

1. CohortMethod - Tools for new-user cohort designs and comparative effect estimation.
2. FeatureExtraction - Create covariates from observational data in the OMOP CDM format.
3. Characterization - Conduct cohort characterizations with standard and custom analyses.
4. SelfControlledCaseSeries - Implements self-controlled case series designs for safety analysis.
5. EmpiricalCalibration - Adjust effect size estimates using empirical calibration methods.
6. PopulationLevelEstimation - Perform population-level effect estimation with large-scale observational data.
7. MethodEvaluation - Tools for evaluating predictive and causal inference methods.
8. PatientLevelPrediction - Develop and validate patient-level prediction models.
9. CommonDataModel - Functions for handling and transforming OMOP CDM data.
10. CohortDiagnostics - Diagnosing and visualizing cohort definitions.
11. Eunomia - Simulate OMOP CDM data for development and testing.
12. CDMConnector - Provides tools to connect to OMOP CDM databases.
13. HADES - A collection of R packages for observational data analytics.
14. CohortSurvival - Perform survival analysis for patient cohorts in the OMOP CDM.
15. Andromeda - Efficient in-memory data storage and manipulation for OMOP CDM.
16. allofus - Tools to work with All of Us OMOP CDM data.
17. Hydra - Rapidly create study packages for observational research.
18. DataSHIELD - Analyze distributed data securely without disclosing individual data points.
19. dsOMOP - DataSHIELD implementation for OMOP CDM.
20. DrugUtilisation - Analyze drug utilization patterns in observational healthcare data.
21. OmopSketch - Generate compact data summaries for privacy-preserving analytics.
22. IncidencePrevalence - Calculate incidence and prevalence rates from OMOP CDM data.
23. omopgenerics - Simplified generic tools for working with OMOP CDM data.
24. EvidenceSynthesis - Combine evidence from multiple studies or data sources.
25. ROMOP - A flexible framework for research using OMOP CDM data.
26. Strategus - Automation tools for executing large-scale observational studies.
27. DeepPatientLevelPrediction - Deep learning approaches for patient-level prediction in the OMOP CDM.

Contributing

Contributions to this list are welcome! If you'd like to add new packages or enhance descriptions, feel free to submit a pull request.

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This list is inspired by the HADES project and similar curated lists.