DataQualityDashboard R package
The DataQualityDashboard R package helps researchers check the quality of real-world health data that has been converted into a standard format (OMOP CDM). It runs over 3,300 automated checks to find possible data problems, so users can trust the results they get from analyzing this data.
At a glance
Use when
Assessing data quality before conducting observational studies; preparing databases for multi-site research; ensuring transparency in real-world evidence submissions
Avoid when
Working with non-standardized data formats not mapped to OMOP CDM; lacking technical capacity to run R-based tools
Inputs
OMOP CDM-formatted observational health data
Outputs
Comprehensive data quality report highlighting potential issues across conformance, completeness, and plausibility
How it works
An open-source R package that executes and summarizes over 3,300 configurable data quality checks on OMOP Common Data Model (CDM) instances. Implements checks across conformance, completeness, and plausibility dimensions, supporting both verification and validation. Enables customizable quality reporting through configurable thresholds and is designed to integrate into real-world evidence workflows to enhance transparency and trustworthiness.
- Project
- EHDEN
- Funding
- IMI
- Project status
- Completed 2024
- HTA domains
- Clinical Effectiveness
- Categories
- RWE
- Technology
- Non-specific
- Assumptions
- Data is mapped to the OMOP CDM structure; quality issues can be detected through predefined logic checks; user can interpret and act on findings
- Strengths
- Comprehensive coverage of data quality dimensions; open-source and customizable; supports transparency in real-world evidence generation; scalable across databases
- Limitations
- Limited to OMOP CDM structure; requires technical expertise in R and database systems; does not fix data issues, only identifies them
- Also known as
- DataQualityDashboard, DQDashboard
Questions this answers
- › How good is my real-world health data before I start analysis?
- › Are there missing or incorrect entries in my OMOP CDM database?
- › Does my observational data meet standard quality criteria?
- › Can I trust the results I get from this database?
- › How can I make my data analysis more transparent and reliable?
- › What types of data issues should I look for in a standardized health database?
Similar by meaning
Beta record. Generated from the primary source via AI extraction and independent audit, pending final human review.